Developing a Multidisciplinary Best Practice Manufacturing Education and Research Laboratory for 21st Century Competitiveness by Yamkelani Moyo A dissertation submitted to the Graduate Faculty of Auburn University in partial ful llment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama Dec 14, 2013 Keywords: Manufacturing education, Problem based learning, Interdisciplinary engineering education, Experiential learning Copyright 2013 by Yamkelani Moyo Approved by John. L. Evans, Chair, Thomas Walter technology Professor in Industrial and Systems Engineering Robert Thomas,Professor Emeritus in the Industrial and Systems Engineering Mustafa. V. Uzumeri,Assistant Professor Department of Management Abstract In the last decade both industrialists and educators have acknowledged the presence of competency gaps in graduates entering manufacturing careers. As a result, the United States is presently faced with a crippling skills shortage in the manufacturing sector that is adversely a ecting relative growth of the manufacturing sector and thus the relative decline in its share of the Gross domestic product (GDP) over the years. Despite the depression and record high unemployment rates in many states, it is widely reported that manufacturers are currently nding it di cult to ll critical manufacturing jobs that are needed to meet customer delivery dates, maintain margins and plan for future expansion. Recent studies have attributed this di culty in lling manufacturing positions to the skills gap phenomenon. In the early in the 19th century, Engineering had been taught primarily as a hands-on subject. However with advances in science, beginning in the 19th century, the pedagogical emphasis in engineering education shifted more towards classroom and lecture based instruc- tion with less emphasis placed on hands-on education. Researchers in education have shown that despite the emphasis on classroom/lecture based instruction, Engineering students ten to favor sensual, visual and active learning styles. Competency gaps have emerged due in part to incompatibilities in teaching and learning styles. The manufacturing industry is a dynamic industry that has seen advances in Information technology (IT) and continual emergence of new technologies. These changes in manufac- turing require a new breed of manufacturing engineers who are less understood by today?s educators. Today?s manufacturing engineer needs to be versatile and have the ability to take a systems view of the manufacturing environment. In this research we attempt to provide ii answers to the questions; what are competency gaps of entry level graduates viewed from both an educator?s and industry perspectives, and what methodologies need to be applied to bridge these competency gaps. As an initial step toward bridging the competency gaps in manufacturing, a meta-analysis was conducted to uncover the competencies that are consid- ered important in the manufacturing industry. This was accomplished through an extensive literature review in addition to a manufacturing industry survey. Once the competency gaps have been identi ed, there will be a need to prioritize them in order to establish what components/elements should be made part of a hands-on manufactur- ing laboratory, whose goal is to bridge the gap between industry needs and a manufacturing curriculum. The objective of this research is to make a contribution towards the development of a taxonomy that could be used as a general best practice for manufacturing education. This research documents two years of experience developing a hands-on manufacturing teach- ing laboratory. The foundation for this research is based on the development of a realistic manufacturing environment that mimics the intricacies of a real world manufacturing envi- ronment. This was accomplished by designing and building a model factory/learning factory called Tiger Motors. By mimicking realistic problems commonly found in a manufacturing environment, stu- dents? experiences in the lab would lead to a conceptual understanding and reinforcement of theoretical concepts taught in class. In addition, Tiger Motors provides a test bed for stu- dents to experiment and validate various theoretical concepts in a practical setting, as well as allowing students to put into practice the various manufacturing/industrial engineering tools used for designing and analyzing of manufacturing systems. iii Reaching a consensus on whether the use of engineering laboratories is e ective in achiev- ing student outcomes in manufacturing education has remained a contentious topic among academia, and is subject to ongoing research. As contribution to this cause, several interdisciplinary manufacturing labs were developed for junior, senior and graduate level instruction in industrial engineering. To evaluate the e ectiveness of hands-on labs with respect to student outcomes, student surveys were con- ducted at the end of the semester to establish students perceptions on the value of hands-on learning. In addition, a post-only experimental design was created in which the performance of a control group was compared to the performance of a treatment group. A total of three di erent treatment groups received hands-on training in the lab in addition to participating in the lecture. The control groups in all cases participated in just the lecture. The hypoth- esis for this experimental design was that the treatment group?s performance on a post test would be signi cantly better than that of the control group. Results indicated statistical sig- ni cant di erences for the overall score related to the subject matter tested, thus supporting the hypothesis that students hands-on labs do add value to student?s learning. Assembly line balancing (ALB) is one the most important problems in assembly work associated with manufacturing environments. This problem has been studied for many years with several methods and heuristics techniques being proposed. An important input to the ALB problem is the standard operation time which can be established using stopwatch time study method or any one of the many available predetermined time study methods. Predetermined time and motion studies are an alternative method for establishing standard operation times and can be used for existing or yet to be built assembly lines. Despite the signi cant amount of research on ALB, little has been mentioned on what methods were used for establishing standard operation times used as input in ALB problems. It is logical that the quality of the line balancing solution can be a ected by the standard operation time used. Since standard operation time used in ALB is dependent on the method iv used, the research question to be addressed is what method would yield a better line balancing solution. A study of the e cacy of the method used for establishing standard operation times for use in an ALB problem was conducted. Results indicated that predetermined time study underestimated the actual time spent on an assembly task. Despite the di erence in task times between the two methods, the quality of the line balancing solution seemed una ected by the method used to establish the time standards. v Acknowledgments First and foremost, I am thankful to the Lord-almighty for a ording me this opportunity to pursue my dreams. I am thankful to my Family for having the patience and giving me the encouragement through this long journey. I am extremely appreciative of the support and guidance that my advisor Dr. John Evans has given me over the course of my studies at Auburn. To Mr. Tom Duval, I thank you for your unparalleled support during this endeavor, you played a big role in the development of the Manufacturing lab and you were a great help in ensuring that everything ran smoothly. I am also grateful to Dr. Vic Uzumeri for his invaluable input. I?d like to extend my thanks to Dr. Thomas who has been a huge source of encouragement for me during my time at Auburn. I also acknowledge the support from other teaching assistants, Mr Gang Hao and Mr Raja Chezian who played a huge part in the development of the lab and assisting with the various lab setups. To Samu Siziba, I thank you for the love and support. vi Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 The Objectives of the Research . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1 State of Manufacturing in North America: . . . . . . . . . . . . . . . . . . . 11 2.2 Scope of Manufacturing in the World: . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Perspectives of manufacturing education in the USA . . . . . . . . . . . . . . 14 2.4 Methods used in teaching/modeling manufacturing systems: . . . . . . . . . 18 2.5 The need to revamp Manufacturing education in the US: . . . . . . . . . . . 20 2.5.1 America?s aging workforce: . . . . . . . . . . . . . . . . . . . . . . . . 20 2.5.2 Projected growth of manufacturing: . . . . . . . . . . . . . . . . . . . 21 2.6 Manufacturing jobs that are in demand: . . . . . . . . . . . . . . . . . . . . 22 2.7 Manufacturing skills sought by Manufacturers: . . . . . . . . . . . . . . . . . 24 2.8 Worldwide programs to improve Manufacturing: . . . . . . . . . . . . . . . . 24 2.9 Psychology of Learning: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.10 Problem Based Learning: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.10.1 Learning through Simulation Games: . . . . . . . . . . . . . . . . . . 31 2.10.2 Use of Simulation and Games in Manufacturing education: . . . . . . 33 2.10.3 A review of manufacturing teaching labs in US colleges . . . . . . . . 34 vii 2.11 Methods used for assessing e ectiveness of learning: . . . . . . . . . . . . . . 36 2.11.1 Concept Mapping: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.11.2 Matching: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.11.3 Baseline Data: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.11.4 Post-test only: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.12 Strategies for Enhancing the Role of Manufacturing Education . . . . . . . . 38 3 Design and Development of a Model Learning Manufacturing Lab at Aubunrn University (AU) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.1 Tiger Motors Manufacturing Systems Design methodology . . . . . . . . . . 41 3.2 Academic Perspective of competency gaps and alignment with industry re- quirements: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.3 Comprehensive list of elements considered for hands-on learning activities in a manufacturing lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.4 A methodology for selecting of potential elements/components to include in a hands-on manufacturing teaching lab . . . . . . . . . . . . . . . . . . . . . 45 3.4.1 Determination of adequate teaching levels for identi ed elements : . . 48 3.4.2 A three dimensional model for establishing interdisciplinary compo- nents of manufacturing teaching lab: . . . . . . . . . . . . . . . . . . 49 3.4.3 Research Objectives for manufacturing industry perspective survey . 50 3.4.4 Research Questions for the Questionnaire: . . . . . . . . . . . . . . . 50 3.5 A Manufacturing industry perspective on the important elements required for manufacturing education . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.5.1 Participant demographics . . . . . . . . . . . . . . . . . . . . . . . . 51 3.5.2 Establishing potential manufacturing laboratory elements and level of instruction required for e ective learning . . . . . . . . . . . . . . . . 54 3.5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Developing the lab for bridging competency gaps of manufacturing graduates . . 60 viii 4.1 Tiger Motors oor layout and workstation design . . . . . . . . . . . . . . . 60 4.2 Design and implementation of material replenishment strategy for Tiger Mo- tors Manufacturing system . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.1 Kitting material replenishment strategy . . . . . . . . . . . . . . . . . 66 4.2.2 Kanban based just in time replenishment strategy . . . . . . . . . . . 66 4.2.3 Line stocking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.2.4 Establishing the adequate number of Kanban card for each part . . . 70 4.2.5 Automatic Kanban card updating and printing formulation in Excel . 74 4.3 design of a hands-on assembly line balancing lab . . . . . . . . . . . . . . . . 76 4.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.3.2 Mixed models assembly Lines: . . . . . . . . . . . . . . . . . . . . . . 77 4.3.3 multi-model assembly lines . . . . . . . . . . . . . . . . . . . . . . . . 78 4.3.4 Formulation of assembly line balancing problem . . . . . . . . . . . . 78 4.3.5 Inadequacies of SALBP . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.4 Developing a Practical Assembly line balancing problem for manufacturing system course (INSY 3800) . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.4.1 establishing standard times (Tek) for elements . . . . . . . . . . . . . 83 4.4.2 Conducting the line balancing lab . . . . . . . . . . . . . . . . . . . . 86 4.5 Using computerized line balancing software for teaching line balancing . . . . 96 4.5.1 A comparison of stopwatch time study and Predetermined times in manual assembly task . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.5.2 Data collection during the lab . . . . . . . . . . . . . . . . . . . . . . 105 4.5.3 Student?s Assembly line Balancing labs . . . . . . . . . . . . . . . . . 108 4.5.4 Results of assembly line line balancing labs . . . . . . . . . . . . . . . 110 4.5.5 Results of running a balanced line ( Production run-2 and Production run-3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 ix 4.5.6 Comparison of Computerized line balancing method with Manual as- sembly method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 4.5.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 4.5.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 4.6 A hands-on Robotics lab for teaching introductory automation . . . . . . . 121 4.7 A hands on programmable logic controller lab . . . . . . . . . . . . . . . . . 125 4.7.1 Students PLCs lab projects . . . . . . . . . . . . . . . . . . . . . . . 126 4.8 Student perceptions on introductory manufacturing lab in enhancing student learning and interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 4.8.1 Students responses to survey questions . . . . . . . . . . . . . . . . . 130 4.8.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 5 A hands-on approach to enhancing student learning in Lean Production course . 136 5.1 abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5.3 Background information in lean manufacturing training . . . . . . . . . . . . 138 5.4 Methodolgy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 5.5 Individual group lean manufacturing project . . . . . . . . . . . . . . . . . . 143 5.5.1 Value stream mapping Tiger motors (VSM) . . . . . . . . . . . . . . 145 5.5.2 Lab implementation of pull based 2 Card Kanban Production Control with production leveling . . . . . . . . . . . . . . . . . . . . . . . . . 150 5.5.3 Setup reduction using single minute exchange of dies . . . . . . . . . 152 5.6 Development of a Simulation tool for assisting with Lean production training 156 5.6.1 Developing a computer simulations model to mimic the production operation at Tiger Motors . . . . . . . . . . . . . . . . . . . . . . . . 158 5.6.2 Simulating a traditional push based manufacturing system using Tiger Motors oor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 x 5.6.3 Simulating a Lean based production manufacturing system using Tiger Motors oor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 5.6.4 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 168 5.7 An Assessment of the e ectiveness of hands-on laboratory participation in enhancing student learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 5.7.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 5.7.2 Evaluation of student outcomes through written test assessment . . . 171 5.7.3 A survey to assess students attitudes and perceptions towards lean manufacturing hands-on laboratory learning . . . . . . . . . . . . . . 172 5.7.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 5.7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 5.8 Evaluating the e ect of hands-on laboratory participation on students con- ceptual understanding through written tests . . . . . . . . . . . . . . . . . . 179 5.8.1 Evaluating the performance of treatment group(SMED Lab participa- tion) against control group(None participation in SMED lab . . . . . 179 5.8.2 Evaluating the performance of treatment group(SMED Lab participa- tion) against control group(None participation in SMED lab . . . . . 182 5.8.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 5.8.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 6 Summary, Conclusions, and Future Work . . . . . . . . . . . . . . . . . . . . . . 188 6.0.5 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 A An Industry perspective on important elements required for manufacturing edu- cation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 A.1 Student perceptions on introductory manufacturing lab in enhancing student learning and interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 xi B Unbalanced work instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 C Data Collection forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 D Line Balancing solutions Using Excel spreadsheet template . . . . . . . . . . . . 230 D.1 Cell 1 Line Line balancing solution . . . . . . . . . . . . . . . . . . . . . . . 230 E Assessing the e ectiveness of hands-on labs through student surveys . . . . . . . 239 E.1 Student perceptions on introductory manufacturing lab in enhancing student learning and interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 E.2 Student perceptions on introductory manufacturing lab in enhancing student learning and interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 F Assessment through written test . . . . . . . . . . . . . . . . . . . . . . . . . . 252 F.1 Midterm exam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 F.2 Quiz 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 G Computer simulation results for Tiger Motors shop oor . . . . . . . . . . . . . 258 xii List of Figures 2.1 Manufacturing output by state . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Manufacturing modeling techniques . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3 Di culty to ll positions in manufacturing . . . . . . . . . . . . . . . . . . . . . 23 2.4 Manufacturing skills sought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.5 Cone of learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.6 Interdisciplinary course integration . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.7 Manufacturing labs bench marking in US colleges . . . . . . . . . . . . . . . . . 35 3.1 Manufacturng lab model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2 CASA/CIM wheel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3 TeachingLevels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 Dimensions for de ning the content and scope of manufacturing hands on learning 49 3.5 Academia perspective of product design elements, Ereveles (1996) . . . . . . . . 55 3.6 Manufacturing industry perspective of product design elements . . . . . . . . . 55 3.7 Academia perceptions of automation and new technology elements integration, Ereveles (1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 xiii 3.8 Manufacturing industry perceptions of automation and new technology elements integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.9 Academia perceptions of manufacturing elements integration, Ereveles (1996) . 58 3.10 Manufacturing industry perceptions of manufacturing elements integration . . . 58 4.1 Models of Vehicles assembled at Tiger Motors . . . . . . . . . . . . . . . . . . . 61 4.2 Material replenishment routes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.3 Tiger motors Lay out con guration used . . . . . . . . . . . . . . . . . . . . . . 63 4.4 Master Production Schedule: MRP Vs Kanban . . . . . . . . . . . . . . . . . . 64 4.5 Material replenishmentpolicies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.6 Categories of Lego Bricks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.7 Excel formulation of Kanban quantities . . . . . . . . . . . . . . . . . . . . . . . 68 4.8 A-B-C-D classi cation of raw material stock . . . . . . . . . . . . . . . . . . . . 69 4.9 Time required for picking parts at SuperMarket . . . . . . . . . . . . . . . . . . 72 4.10 Establishing time required for picking parts at Supermarket . . . . . . . . . . . 73 4.11 Kanban card automatic updating excel worksheet . . . . . . . . . . . . . . . . . 74 4.12 Container arrangement at a workstation . . . . . . . . . . . . . . . . . . . . . . 75 4.13 Kanban card attached to a container . . . . . . . . . . . . . . . . . . . . . . . . 75 4.14 SuperMarket intermediate storage area . . . . . . . . . . . . . . . . . . . . . . . 75 4.15 Push cart for material delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 xiv 4.16 OEM assembly instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.18 Partial Precedance table for Speeder vehicle . . . . . . . . . . . . . . . . . . . . 87 4.19 Precedence Diagram for Speeder vehicle . . . . . . . . . . . . . . . . . . . . . . 88 4.20 Precedence matrix displaying the relationship between elements . . . . . . . . . 90 4.21 Stage 2: Establishing Precedance in Probalance software . . . . . . . . . . . . . 98 4.22 Stage 3: Evaluated line balance using Probalance software . . . . . . . . . . . . 98 4.23 Comparing stopwatch standard time data and PMTS standard time data as input to in assembly LB problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.24 Scatter plots for establishing correlations of standard times with actual service times for manual balanced assembly line . . . . . . . . . . . . . . . . . . . . . . 101 4.25 Comparison of stopwatch standard times Vs Predetermined times . . . . . . . . 102 4.26 Unbalanced line line balance sheets . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.27 Establishing VA and NVA times at each station . . . . . . . . . . . . . . . . . . 106 4.28 Production run 1 results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 4.29 Boxplot of stopwatch service time Vs Actual service times . . . . . . . . . . . . 111 4.30 Paired t test for comparing Stopwatch data to Actual service times . . . . . . . 111 4.31 Manual line balancing method with stop watch data input . . . . . . . . . . . . 112 4.32 Probalance Computerized line balancing method with PMTS data input . . . . 113 xv 4.33 Production runs throughput rate comparisons . . . . . . . . . . . . . . . . . . . 114 4.34 Simulation run production run results . . . . . . . . . . . . . . . . . . . . . . . 115 4.35 Throughput rate vs LB method by Cell . . . . . . . . . . . . . . . . . . . . . . 115 4.36 LB vs Cell 2way Anova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 4.37 Boxplot of Throughput rate by LB Method, Prod run . . . . . . . . . . . . . . . 116 4.38 Semi-Automated robotic assembly station . . . . . . . . . . . . . . . . . . . . . 123 4.39 Cell Production run 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 4.40 Cell 2 Production run 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 4.41 PLC training station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4.42 PLC ladder logic for Machine batching project . . . . . . . . . . . . . . . . . . . 128 4.43 Desired employability skills of students . . . . . . . . . . . . . . . . . . . . . . . 130 4.44 Students perception of their learning ability during lectures and in hands-on lab 131 4.45 Students prior practical experience with lab elements . . . . . . . . . . . . . . . 132 4.46 Students prior practical experience with lab elements . . . . . . . . . . . . . . . 133 4.47 Students perception on virtual learning environments . . . . . . . . . . . . . . . 134 5.1 INSY 6800 calender of events and experimental design . . . . . . . . . . . . . . 141 5.2 Production run -1 results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 5.3 Tiger Motors current value stream map . . . . . . . . . . . . . . . . . . . . . . 148 xvi 5.4 Tiger Motors Future Value stream map . . . . . . . . . . . . . . . . . . . . . . . 149 5.5 Pull Production Kanban loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 5.6 implementation of a load leveling using a heinjunka board . . . . . . . . . . . . 152 5.7 Supermarket bu er at the end of Tiger Motors manufacturing Cell . . . . . . . 152 5.8 SMEDSolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 5.9 Tiger Motors oor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 5.10 Excel input for station cycle times . . . . . . . . . . . . . . . . . . . . . . . . . 161 5.11 Simio development user interface-Push system . . . . . . . . . . . . . . . . . . 161 5.12 3D-Simio representation of Tiger Motors shop oor . . . . . . . . . . . . . . . . 162 5.13 Simio experimental setup for investigating the e ects . . . . . . . . . . . . . . . 162 5.14 Batch sequence levels used for investigating in uence of batching on system per- formance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 5.15 Cell -1 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 5.16 Cell-1 Throughput time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 5.17 E ect of batching with large change over times exist . . . . . . . . . . . . . . . 164 5.18 E ect of batching with small change over times exist . . . . . . . . . . . . . . . 164 5.19 Simio model of Tiger motors pull based production control system . . . . . . . 166 5.20 Cell-1 throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 5.21 System Lateness response results . . . . . . . . . . . . . . . . . . . . . . . . . . 168 xvii 5.22 Current job positions and expected career paths . . . . . . . . . . . . . . . . . . 172 5.23 Students perceived learning when participating in hands-on lab activities . . . . 173 5.24 Perceived importance of lean lab elements, Q8a . . . . . . . . . . . . . . . . . . 174 5.25 Perceived importance of lean lab element, Q8b . . . . . . . . . . . . . . . . . . . 175 5.26 Ranking of hands on lab activities according to the best learning experience o ered175 5.27 bene ts of hands-on lab participation with respect to interest level . . . . . . . 176 5.28 Long distance lab participation through video streaming . . . . . . . . . . . . . 177 5.29 Comparing group performance with respect to speci c VSM test questions . . . 181 5.30 Group performance comparison wrt VSM using General linear model (GLM) . . 182 5.31 Group performance Comparison with respect to SMED test questions . . . . . . 183 5.32 Signi cantly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 C.1 throughput Data capture sheet . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 C.2 Established value added and Non- value added times . . . . . . . . . . . . . . . 228 D.1 Cell 1 Precedence diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 xviii List of Tables 3.1 Business Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Management Philosophies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.3 Product Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.4 Information and Decision Support Systems for Factory Management . . . . . . 46 3.5 Manufacturing Control: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.6 Manufacturing Process Automation: . . . . . . . . . . . . . . . . . . . . . . . . 46 3.7 Manufacturing Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.8 Ranking of important competencies by industry representatives . . . . . . . . . 53 3.9 Desired competencies in manufacturing . . . . . . . . . . . . . . . . . . . . . . . 54 3.10 Industry and academia perceptions of the important manufacturing elements and associated teaching levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.1 Precedence matrix displaying the relationship between elements . . . . . . . . . 91 4.2 RPW values sorted in descending order . . . . . . . . . . . . . . . . . . . . . . . 92 4.3 Decision support table for assigning tasks to stations . . . . . . . . . . . . . . . 94 4.4 stage 1: Probalance task sheet showing task ID and task times . . . . . . . . . . 97 4.5 theoretical initial line balance metrics . . . . . . . . . . . . . . . . . . . . . . . . 104 4.6 Capturing throughput time data . . . . . . . . . . . . . . . . . . . . . . . . . . 105 4.7 Value added Non-value time analysis . . . . . . . . . . . . . . . . . . . . . . . . 107 5.1 Pre SMED Analyis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 C.1 Value added/ Non- value added excel template . . . . . . . . . . . . . . . . . . 229 xix Chapter 1 Introduction The Encyclopedia Britannica de nes manufacturing as the making of products from raw materials by using manual labor or, machinery and is usually carried out systematically with division of labor. Manufacturing has been intrinsically linked to the strength of economies. Countries with strong manufacturing sectors tend to have much stronger economies, and consequently, improved standards of living. However, with the passage of time and the advent of globalization, more and more countries are starting to embrace manufacturing as a means of strengthening their economies. As a result of this there is now a greater variety of products that consumers can choose from. Globalization has led to open market economies, and increased competition among man- ufacturers resulting in a high level of competition in the manufacturing environment. Con- sequently, the survivability for many companies is at an all time high, especially for US companies that are faced with high operation costs as opposed to their counterparts in other parts of the world. Manufacturing has always been an evolving industry, thus the companies need to stay ahead of the curve if survivability is desired. Manufacturing started o as craftsmanship industry in which a complete product was fashioned from a pile of raw material by individual craftsman. In the 18th Century, the industrial revolution began to take shape and in came organized manufacturing where the goal was to improve productivity. Early industrialists realized the need to fragment man- ufacturing into series of unskilled tasks.This was accomplished by substitution of manual 1 operations with machines, the objective being to attain accuracy and repeatability as well as increasing productivity. This led to better quality products that were more a ordable. This was the beginning of mass production. Mass production utilized the concept of material ow through a factory by standardizing operations as well as product components. The early 20th century saw the advent of industrial engineering pioneered by consultants such as Frederic Taylor, Frank Gilbreth, and others. Taylor and compatriots created rules for measuring industrial time, and the de nition of industrial productivity. After the world war Japanese scientists invited Deming to help them revolutionize the Japanese industry. Deming introduced new ideas of management including statistical process control which the Japanese fully embraced. During this time, Toyota Motor Corporation was having di culties with suppliers of electrical components and decided to study Ford Motor Company manufacturing with hopes of incorporating their methods. However, they found the Ford system to be too resource intensive and demanding in capital to work in Japan. Using the teaching of Deming and ideas of Taiichi Ohno and Eiji Toyoda, Toyota Motor Corporation was able evolve mass production to a new system called Lean manufacturing. Information technology (IT) is an integral component of today?s manufacturing environ- ment. Relative to manufacturing IT encompasses a broad range of computer and commu- nications technologies. IT includes the hardware that computes and communicates, the software that provides the data, knowledge, and information while at the same time control- ling the hardware; the robots, machinery, sensors, and actuators, or e ectors. Information technology can be viewed as an integrator of the application of robots and computers in manufacturing. The application of computers and robots in automation has driven the ef- forts of manufacturing engineers. In environments where process e ciency is a desired trait, the use of robots and computers in automated processes o ers a competitive advantage. An 2 example of a good business strategy based on automation is found in production of alu- minum beverage cans. IT is used to support a number of other dimensions such as agility in manufacturing and product design response to changing consumer preferences. Lean Manufacturing helped Japanese companies produce better quality products at more a ordable prices. This new competitive edge saw Japanese companies eating into a huge share of North American market share, even forcing some European and America companies to fold. Those companies that survived have been forced to reevaluate their manufacturing principles, with many companies adopting Lean manufacturing starting in early 1980s. In 1995, a group of industrial leaders and academics set out to answer questions on the future of manufacturing. A report entitled, Next Generation Manufacturing, (NGM) was published in which a framework of actions deemed necessary to be competitive in 21st man- ufacturing environment were identi ed. Next Generation Manufacturing report set out to identify competitive drivers in the future business environment and de ne attributes that would be necessary to succeed in the 21st century manufacturing business environment. The next generation manufacturing company is viewed as one that has an integrated entity of people, business process, and technology with excellent response capability. The respon- siveness applies to (1) customers, (2) plant and equipment, (3) human resources, (4) global market and, (5) practices and culture. One of the recommendations of NGM was that man- ufacturing must be addressed as a total, dynamic system that tightly integrated people, processes, and technology (Kennedy, 2003, p. 146). In a manufacturing environment that is changing more rapidly, it is becoming increasingly imperative for US manufacturers to provide customers with shorter lead times between orders and delivery , product conceptualization and realization, greater product customization, and higher product quality and performance while meeting more stringent environmental constraints(Manufacturing studies Board, 1995). These new demands of manufacturing has necessitated for fundamental changes in the workforce, a workforce that is more skilled and 3 educated. Nearly two thirds of workplace jobs that will be created in the coming years will require education beyond high school (Lawless, 2000). We are in a transition period which is poorly understood by educators and you people planning to join the work force. For example, unskilled jobs are disappearing and being replaced by smart machines or a technologically savvy workforce. While Europe, Japan, and South America have developed e ective ways to produce the right type of skilled employee without a university degree, the same cannot be said of North America. While in the past it was adequate for industrial and manufacturing engineers to measure only tangible yardsticks, there has been a gradual change, with the new breed of engineers requiring the ability to measure soft values, such as knowledge, technology, and skill assets. The importance of manufacturing education has been extensively discussed in many re- search studies. Traditionally many engineering schools and other technology based programs have relied to a huge extent on classroom based instruction. However, there have been count- less criticisms by industrialist regarding the work readiness of many manufacturing engineer- ing graduates entering the job market. According to the ndings of a number of surveys conducted seeking industrialist perspectives on the readiness of entry level manufacturing personnel, a number of incompetences were found inherent among entry level manufacturing personnel. The ndings of these surveys has led to a number of questions being asked about the relevance and ability of our manufacturing education system to respond to the dynamic nature of today?s manufacturing environment. In order for the US to maintain its global position as a manufacturing super power, there is a need to embrace a new breed of manufacturing engineer, one who is versatile, and has an understanding of the systems concept of manufacturing engineering. For this to happen, there is a need for manufacturing programs to embrace new and already proven teaching 4 ideologies that are appropriate for engineering education. The need to close the competency gaps of engineering personnel has never been more important than it is now. Researchers have posited that manufacturing education as well as the quality of graduates coming out these programs will only improve if manufacturing programs embrace appropriate learning taxonomies that have been proposed by researchers in education as well as integrating input of industrialist. Although research has revealed the presence of di erent teaching styles and learning styles, little has been done to determine the compatibility of various teaching styles of instructors to the favored learning styles of students. Some of the well-known learning styles that have been researched in education and their relevance to manufacturing education will be dis- cussed later as part of this research. They include the Myers Briggs type indicator (MBTI), Kolbs learning style Model (KLSM), Herman Brain Dominance instrument (HBDI), and Felder-Silverman Learning Style Model. According to research, engineering students have been shown to favor learning styles that incorporate elements of hands-on training. Accord- ing to Felder-Silverman Learning style model (FLSM), it has been shown that a majority of engineering instructors and academics tend to have a intuitive, verbal, deductive and se- quential teaching styles while engineering students have been shown to favor sensual, visual, and active learning styles (Ssemakula, Liao, & Darin, 2010). Because of these incompati- bilities between the traditional teaching styles and the favored learning styles of students in engineering, competency gaps have resulted. Hopter and Kopka (2001) reported employers felt that college graduates had competency gaps in some essential skills needed to enter the workforce. The Society of manufacturing engineering has identi ed some of these compe- tency gaps by conducting repeated surveys over a number of years. The surveys revealed a number of competency gaps of new graduate students. This research is being carried out in direct response to this inadequacy to fully address the alleged competency gaps of manufac- turing engineering graduates. The important question that needs to be constantly addressed 5 is: How manufacturing educators can implement learning activities that allow the competency gaps identi ed by industry to be closed. In addition, there is a need to continuously address the misalignment of manufactur- ing industry requirements relating to skill-sets of entry level manufacturing personnel and manufacturing curriculum. Any revelation of a disconnect between manufacturing industry requirement and manufacturing engineering curriculum can be used as a goal , then inves- tigation on how these competency gaps can be closed should be the primary focus of this research. Another criticism of current manufacturing education is in the disconnect that ex- ist between parts of manufacturing curriculum. The Tayloristic approach to manufacturing education that encourages the compartmentalization of manufacturing into functions, each of which is taught in di erent courses, has been found to be an ine ective way of readying students for manufacturing (Domblesky, Vikram, & Rice, 2001). In recent years, new ndings in cognitive processes (Felder & Silverman, 1988; Mestre, n.d.) and behavioral psychology (Koen, 1994) have demonstrated the limits of lecture, and alternatives to augment its e ectiveness have been proposed (Wankat & Oreovicz, 1993), including laboratories and cooperative learning. The recognition for the need to integrate hands-on learning activities into present manufacturing curriculums that are largely lecture oriented has seen a number of initiatives in manufacturing education. The learning Fac- tory is one these initiatives to revitalize manufacturing education. This concept that has been implemented in a number of universities (Penn State, University of Washington, The University of Puerto Rico Mayagez) has been largely successful in a ording student the opportunity to participate in practice based learning. However, despite its inherent bene ts, the Learning factory concept still has its limitations. According to (Domblesky et al., 2001), one major limitation of the Learning factory is its focus on integrating design and manufac- turing rather than emphasizing the integration of manufacturing principles. The Learning 6 factory concept tends to focus on the discrete entities of a manufacturing system e.g. design and relevant manufacturing processes rather than emphasizing on systems approach with di erent manufacturing functions all integrated together e.g. business function, technical functions, and support functions such as Quality control. Manufacturing teaching laboratories can provide invaluable support to theoretical courses that are necessary to give students the foundation they need in understanding the various elements and competencies desired of the manufacturing profession. In the manufacturing eld, laboratories are important as they provide the physical link between taught concepts and application of various tools in the development and manufacturing of products. Labora- tory exercises can serve to ll the void between theory taught in various curricula and skills and knowledge expected in industry. Since the primary aim of manufacturing education is to provide enabling skills that will help students perform their intended actions e ciently in the workplace, it is imperative that an ideal training ground for students of manufacturing engineering should entail an integrated manufacturing environment that is conceptually sim- ilar to the real factory. By providing a laboratory environment that attempts to replicate a real manufacturing environment, a smoother transition from the classroom to the workplace can be promoted. 1.1 Research Question The general consensus among academics and industrialist is that there exists a gap re- garding the skill-set that manufacturing industry desires and the content of manufacturing curriculum as the methods used to deliver the content. There is a need to bridge the gap between what manufacturing industry values and what manufacturing curriculum in general o ers. This research is thus meant to provide answers to the following questions 7 1. What does industry consider the important competency gaps of entry level manufac- turing personnel? 2. What perspectives does manufacturing industry have with regards to the content of manufacturing curriculum and associated training methods used in manufacturing cur- riculums? 3. Does incorporating hands-on manufacturing laboratories to support various topics that are considered to contribute to the competency gaps in manufacturing reinforce student learning and interest in the subject? 1.2 The Objectives of the Research The objective of this research is to ultimately contribute to the development of manu- facturing best practices for e ective teaching of manufacturing topics. This lab is intended to provide interdisciplinary training in manufacturing where students can use the training acquired from other courses from the curriculum. The manufacturing lab is intended to provide an environment where students work in teams to nd solutions to manufacturing related problems as well as investigate particular concepts and validate theoretical ndings through hands-on learning. A good example that would be investigated in this research is that of assembly line balancing. While the traditional classroom approach to this problem is to provide students with data to the problem as well as other pertinent information to enable the student to successfully solve the problem, the problem itself is far from the reality found in industry. The goal of this research is thus to attempt to establish the value of solving more realistic manufacturing related problems with regards to instilling con dence on the part of the student with the hope of closing the alleged competency gaps. 8 A number of manufacturing related labs will be developed with the goal of testing the main Hypothesis (H1) : Reinforcing particular topics in the manufacturing curriculum with hands-on laboratory exercises increases student learning and interest in the subject In order to test the hypothesis the following manufacturing related labs were be developed and used as the test bed of this research: Assembly line balancing Lean Values stream mapping Setup reductions Single minute Exchange of Dies Pull strategies for shop oor Control and Cell Design 9 Chapter 2 Literature Review Classi cation of Manufacturing Manufacturing can be classi ed as either primary, sec- ondary or tertiary. Primary industries are those that cultivate and exploit natural resources, such as agriculture and mining. Secondary industries are those that convert outputs of primary industry into products. Tertiary industry constitutes of the service sector such as banking, communications, health, medical etc. Manufacturing belongs to the secondary in- dustry. Manufacturing can further be divided into discrete manufacturing and continuous manufacturing. Continuous manufacturing is when production equipment is exclusively used for a given product and the output of the product is uninterrupted. Good examples of contin- uous manufacturing is found in the manufacture of pharmaceuticals , chemicals, petroleum, beverages etc. On the other hand, discrete manufacturing produces individual products and includes such industries as automobiles, aircraft, appliances, machinery (Grover, 2008) etc. This project is concerned with operations of discrete manufacturing in manufacturing edu- cation. Certain manufacturing operations are required to convert raw material. Processing and assembly operations are the two basic operations used for producing nished products. Processing operations transform a work material from one to a more advanced state closer to the desired state or product while assembly operations join two or more components to form a new entity. While the processing and assembly operations are the basic transfor- mation operations required for the manufacture of a product, additional factory operations are required if the product is meeting the desired goals of: (1) High product quality and performance, (2) On time delivery, (3) Greater product customization. The ability of a manufacturing organization to meet these goals is largely dependent on the skills of their 10 workforce in use of more advanced technologies, greater use of information technology to reduce waste and defects, and more exible manufacturing styles. Only when the combined skills of the workforce, advanced technologies and exible management styles are appropri- ately applied to manufacturing support activities such as material handling, inspection and testing, factory coordination and control can US manufacturing remain competitive in face of growing global competition. 2.1 State of Manufacturing in North America: The three largest manufacturing industries today are : (1)food products, (2) computers and electronic products, (3) and chemicals. Automobiles and auto parts have since dropped from third to fourth between 2002 and 2007, and fabricated metal products slipped from fourth to fth during the same time. Manufacturing is the engine that drives American prosperity. It is central to the economic security and national security of any country. Federal Reserve Chair Ben Bernanke stated on February 28, 2007, "I would say that our economy needs machines, new factories and new buildings and so forth in order for us to have a strong and growing economy.Mark Zandi, chief economist at Moody?s Economy.com calculates that 20.5 percent of the manufactured goods bought in American in 2005 were imported. This was up from 11.7 percent in 1992 and 20 percent in 2004. Manufacturing supports state economies and is a vital part of the economies of most states, even in those areas where manufacturing has declined as a portion of the Gross State Product (GSP). As a share of GSP, manufacturing is among the three largest private-industry sectors in all but ten states. Manufacturing remains the largest sector in ten states and in the Midwest region as a whole. It is the second largest in nine states, and the third largest in 21 others. 11 Manufacturing?s share of State Output Figure 2.1: Manufacturing output by state To give an example of the impact manufacturing has on state economies, an analysis of manufacturing in Connecticut reveals that more than half of the top 100 companies head- quartered in Connecticut are manufacturing rms. Nearly 5,300 Connecticut manufacturing rms combined directly employ almost 200,000 workers and generate $11.1 billion in wages and salaries, and produce over $20 billion of the gross state product. Each new manufac- turing position creates between 1.2 and 5 additional jobs in the state, and manufacturers purchase over $10 billion per year in goods and services from other Connecticut businesses. It?s clear from those numbers that the health of Connecticut?s entire economy is inextricably linked to the well-being of the state?s manufacturing industry. A key competitiveness factor for manufacturers is access to a skilled workforce. Manu- facturers say they value Connecticut as a business location for its supply of skilled workers. However, they are also nding it increasingly di cult to ll many positions requiring ad- vanced manufacturing skills. In addition, many manufacturers in that state have expressed 12 concern in the quantity and quality of job candidates interested in pursuing career opportu- nities in manufacturing. 2.2 Scope of Manufacturing in the World: First, manufacturing has moved from localized operations to global manufacturing pri- marily due to the advances in digital, communication, transportation and other technologies. It has also occurred due to the unprecedented developments and growth in educating the manufacturing workforce in places where manufacturing was insigni cant only 20 years back. Global manufacturing is also driven by the arrival of new entrepreneurs in many parts of the world. Equipped with world-class infrastructure for nance, marketing and other areas, a capable workforce, and forward looking governmental organizations, the new entrepreneurs have come up to take control of global manufacturing and exploit new markets. The growth in global manufacturing is also the result of the never-ending search to pay the least for the manufacturing workers. Over the last two decades, manufacturing organizations in the developed countries have used low cost labor as a means to justify moving manufacturing operations to global destinations. The growth in the global manufacturing workforce is yet another cause for the current transformation. With the emergence of a new political order in many parts of the world since the 1950s, countries that are large and small have invested a sizable share of their national resources to educating an engineering workforce. Starting with Taiwan and Korea in the 1950s, and more recently followed by China, India and the others, these countries have built up their educational infrastructure to produce a large number of engineering graduates ca- pable of supporting the competency requirements of global manufacturing operations. The educational systems in those countries do not limit themselves to developing a technological workforce; instead they are preparing world-class entrepreneurs, capable of managing and 13 challenging the established global order in business, nance and other sectors of the econ- omy. There has been an unprecedented commitment to education that is found at both the individual and collective levels. Those commitments have helped develop and promote edu- cation from the primary to the tertiary level. Although initiated in the beginning as a means to attain higher standards of living, today the drive is to attain excellence in industrial and economic development. One cannot, and should not ignore the fact that the drive in many countries is not limited to developing a workforce to meet the current skills requirements of the industry, but to develop their tertiary education and become a strong force in research and innovation. 2.3 Perspectives of manufacturing education in the USA A number of colleges in the USA have initiated programs to reinforce manufacturing education with hands-on laboratories in an attempt to close the competency gaps of manu- facturing engineering students. The motivation behind the development of various instruc- tional labs and learning factories found in several universities and colleges has been triggered by industry criticism that engineering students are entering the workforce with signi cant competency gaps which has necessitated remedial action on the part of the employer. Much of the focus has centered on competency gaps related to design experiences, while similar concerns have been echoed with respect to manufacturing related skills. Various e orts have been undertaken to address the problem of engineering and technology graduates lacking key industry skills. The Society of Manufacturing engineers has funded some of these e orts through its initiative called Manufacturing education plan launched in 1997. In addition, the National Science Foundation (NSF) as well as other funding agencies have been involved in addressing these concerns. The learning factory (LF) was one major outcome of NSF funding attempt to address some the competency gaps that were found 14 to exist in manufacturing education. The objective of the learning factory was to create an integrated practice-based curriculum that balances analytical and theoretical knowledge with physical facilities for product realization in an industrial like setting (Ssemakula et al., 2010, p. 3). The original learning factory was developed jointly by Pennsylvania State University (PSU), University of Washington (UW), and University of Puerto Rico-Mayaguez in collaboration with Sandia National Laboratories. The objectives of the Learning factory were to develop were to create a practice-based engineering curriculum which balances analytical and theoretical knowledge with manufac- turing, design business realities, and professional skills. Learning factories at each partner institution were to be integrally coupled to the curriculum for hands-on experience in de- sign, manufacturing, and product realization, as well as a strong collaboration with indus- try, outreach to other academic institution, government and industry. The Learning factory concept proved quite successful in participating institutions, supporting a number of new courses such as: (1) Product dissection (2) Concurrent Engineering (3) Technology based Entrepreneurship (4), Process Quality engineering as well as other interdisciplinary design projects. These new courses were built around existing courses which were modi ed to take advantage of the new facilities made possible by the Learning factory. The implementation also involved partnership with local industries at each institution, with local industries con- tributing signi cant resources in terms funds, sta , equipment, and internships. Although the learning factory is not easily adaptable due to its large size and cost associated with its implementation, it does provide some insights into how practice based manufacturing cur- riculum can be developed and implemented. One example of the adaption of the Learning factory can be found at Wayne State University. Using the same concept as that of the leaning factory, Wayne State University developed and implemented a number of hands-on laboratory activities that supported a targeted number of courses around a unifying theme of designing and making a model engine. Using this approach, students were able to generate drawings of engine components and use the drawing in developing process plans and actually 15 fabricating the components. Finally the components were assembled into a working model engine. Each of the activities are part of an appropriate course in the curriculum, with those activities coordinated between the courses. The advantage of this approach is that students take di erent courses all of which are linked to one particular functional product that stu- dents actually make in the laboratory. Using this approach, students are able to see the whole picture of an integrated manufacturing system at work, as opposed to the tayloristic approach of compartmentalizing manufacturing along functional lines and teaching speci c functions in separate courses (Domblesky et al., 2001, p. 2). Traditionally manufacturing education can be viewed as Tayloristic in its approach, which consequently does not help students connect how the activities taught in the di erent courses relate and t together within a manufacturing enterprise. The Learning factory and its adaptations thus provide a means of overcoming this inad- equacy of the lack of integration between learning concepts and courses. The experiential hands-on approach using a common product in multiple courses, gives students a good un- derstanding of the range of issues involved in design, planning, fabrication, assembly, and testing of a functional product. Using an integrated project of this nature exposes students to all processes involved as well as providing motivation and a sense of accomplishment and satisfaction. In an attempt to close the competency gaps in manufacturing education, a number of universities and colleges in the USA have launched initiatives that incorporate practice based manufacturing activities to reinforce lecture based learning. This has been done by developing manufacturing based laboratory activities. Among the popular practice based curriculum initiatives has been the development of Computer Integrated Manufactur- ing laboratories (CIM). While the goal for most institutions has been to attain computer integrated manufacturing status, it has to be understood that a signi cant e ort in terms of resources and commitment is required to attain such a status. It would seem that a number of institutions surveyed followed a common progression in the development of CIM. It would appear that this progression was from (1) stand alone machines, (2) islands of 16 automation (3) exible manufacturing system, and (4) integrated manufacturing systems. In some cases the labs developed were used to support a number of unrelated courses. For instance (Macedo, Colvin, & Colvin, 2005) describes the development of a 10 week course in machine vision as part of the automation course, while (Shiver, Needler, & Cooney, 2003) describes the development of automation course used to teach students how to interface a wide range of equipment such as programmable logic controllers (PLCS), conveyors, pneu- matic actuators, control relays, hardware sensors; robots, machine vision and smart sensors. Oakland University at Rochester operates a lab called Arti cial intelligence and Manufactur- ing laboratory (AIM)(Van Til, Sengupta, Srodawa, & Patrick, 2000). The Aim laboratory is an interdisciplinary laboratory proposed and developed by both computer science and en- gineering faculty. Its purpose is to facilitate issues concerned with education in automated manufacturing. It allows students to learn about how people are integrated in a modern manufacturing environment through their involvement in team based projects. Two major systems found in this laboratory are the intelligent manufacturing cell and intelligent factory. Equipment found in the intelligent cell in CNC lathe, CNC mill, robotic manipulator, PC based cell controller and PC based computer aided design/Computer aided manufacturing (CAD/CAM) system. The intelligent factory consists of an automated storage and retrieval system (ASRS), simulated manufacturing cells, computer controlled transportation system, programmable logic controllers, and a factory controller. It is important to note that the intelligent factory operated in the AIM laboratory is a physical simulator of a real factory and o ers the opportunity to identify constraints which are otherwise not recognizable by a simulation model. The availability of physical simulation in conjunction with computer simulation enhances the learning environment. Arizona State University (ASU), the lead award winner in 1990 has spent some time developing an identi able CIM curriculum (Koelsch, 1990). This program was built as a response to industry needs in Arizona and is focused on multidisciplinary research centers. 17 In discussing how academia can better provide the education for manufacturing leaders, Leo Hani n of Renselear Polytechnic Institute (RPI) de nes better to mean more students focusing on manufacturing with more realistic experiences, and with greater emphasis on people issues in manufacturing(Hani n, 1991) . Based on the analysis of various manufacturing programs benchmarked, it is apparent that there exists signi cant amounts of diversity in the implementation of hands-on manufacturing laboratories in academia. It is apparent the various manufacturing laboratories in many of the institutions were developed without a common roadmap. 2.4 Methods used in teaching/modeling manufacturing systems: Omurtag discussed the need to nd a medium between theory intensive and laboratory intensive extremes that constitute the domain of manufacturing education (Ormutag, 1987). Designers and developers of manufacturing systems education have a plethora of manufac- turing modeling techniques that range from very abstract to real. Figure 2.2 shows the work of Benjamin and Smith that depicts the di erent modeling techniques available that can be used for teaching various concepts in association with manufacturing (Benjamin & Smith, 1990; Borchelt & Alpetiin, 1990) 18 Figure 2.2: Manufacturing modeling techniques It is important for designers of manufacturing education to be fully aware of these mod- eling techniques and to know how each can be e ectively applied to teach various concepts in manufacturing education. It may be argued that each element/topic in manufacturing education can be e ectively taught using any of the methods depicted in Figure 1. The research question that can be asked is: What modelling technique should be applied to a particular manufacturing topic for e ective learning to occur 19 2.5 The need to revamp Manufacturing education in the US: 2.5.1 America?s aging workforce: Americas graying workforce will soon a ect the USAs manufacturing industry. According to a recent report from the Center for Workforce Success (National Association of Manufac- tures & Manufacturing Institute of Deloitte and Touche, 2003), it is reported more than 76 million baby boomers will retire over the next 20 years, with only 46 million generation-Xers taking their places. This may lead to shortage of skills in the manufacturing industry. Cur- rently the US has been forced to rely heavily on skilled immigrant workers to ll the shortfall of skilled workers in the manufacturing industry. This need for skilled workers is projected to increase by 10 million by 2020. Even during the recession when employers had to lay o many workers, employers still reported a shortage of highly skilled, technically competent employees who could fully exploit the potential of new technologies and support increased product complexity (National Association of Manufactures, 2001). This development is not helped by the inherent lack of interest among the next generation of America?s workforce to pursue careers in manufacturing industry. The Manufacturing Institute and Deloitte and Touch recently conducted two major research studies that revealed negative student perceptions about careers in manufacturing. With near unanimity, respondents across the country saw manufacturing opportunities to be in stark con ict with the characteristics they so desired in their careers and as a result, many of these youths had no plans or envisaged themselves pursuing careers in manufacturing in the coming future (National Association of Manufactures & Manufacturing Institute of Deloitte and Touche, 2003). 20 2.5.2 Projected growth of manufacturing: Most positively, the looming retirement of baby boomers is not all that will necessitate the hiring of additional manufacturing employees. After weathering a particularly harsh recession and very slow recovery, many Connecticut manufacturers say they will need to add employees because of favorable business prospects. A substantial 79 % of manufacturers responding to the survey indicated they would need more employees in the next ve years because of the development of new products, increases in their sales or the expansion of their companies. About 23% said they would need to replace 25% or more of their employees within the next ve years. 21 2.6 Manufacturing jobs that are in demand: What types of skilled workers does the US need to ll current and future shortages of skilled employees in manufacturing? A number of surveys have been conducted by di erent economic regions of the US to determine the skills shortage in the US. In one such survey conducted on manufacturers in Connecticut to establish the current and long-term demand for speci c types of manufacturing positions , Connecticut manufacturers were asked about their current skills shortages as well as their projected needs in 2008 and in 2011. The survey sought employers perceptions regarding skills shortage in 12 di erent job categories outlined in the list below. Assuming that results of the survey of Connecticut manufactures are a realistic represen- tation of the entire country, the following sectors were found to have the most critical needs for skilled workers: 1. Tool and die makers 2. CNC programmers 3. CNC machinists 4. Engineers 5. CAD/CAM workers 6. Technical sales sta 7. Plant Managers 8. Production Managers 9. Technical trainers 22 10. Research and development sta Among those positions identi ed by manufacturers as extremely di cult to ll were CNC programmers and CNC machinists (27% each), tool and die makers (22%), technicians (21%), and machinists (20%). Engineering positions were described as extremely di cult to ll by 10%, and very di cult to ll by another 37%. Figure 2.3: Di culty to ll positions in manufacturing Among a number of reasons manufacturers found some jobs in Figure 2.3 di cult to ll were: 1. lack of necessary skills for the given position 2. Applicants were found not ready to enter the job market for a variety of reasons 23 2.7 Manufacturing skills sought by Manufacturers: Through surveys, the skills that manufacturers seek in employees were established (see Figure 2.4). the skill areas most frequently identi ed as current needs were, team build- ing/problem solving, lean manufacturing, equipment operation, blueprint reading, and engi- neering. Projecting ahead ve years, employers said the skills that will be most needed are lean manufacturing, equipment operation and engineering, team building/problem solving. Figure 2.4: Manufacturing skills sought 2.8 Worldwide programs to improve Manufacturing: Endeavors to enhance manufacturing activities in various parts of the world have seen the emergence of country speci c, regional, and international e orts to strengthen the man- ufacturing education. While new educational and training programs in manufacturing are developed, existing programs are revised and updated. Special programs are being created to address the needs of advances in technologies, and unique arrangements are being made for on-the job education in areas where such arrangements are appropriate. While these 24 e orts are helping to address some of the demands of manufacturing, there are continuing challenges in meeting the needs of the changing manufacturing world. This segment of this project reports the e orts of two major international entities, namely the Society of Manufacturing Engineers (SME) in the USA and the Intelligent Manufacturing Systems (IMS) in Europe in preparing the manufacturing engineering workforce. SME serves more than half a million manufacturing engineers, executives and professional members in about 70 countries around the globe. SME also serves as the source for knowledge, networking and skills development for aspiring manufacturing engineers and other related careers. SME has also been the agency responsible for developing and helping implement the criteria for accreditation of the collegiate level manufacturing engineering and technology programs. SME initiated a process in 1985 to study the skills and competencies needed in the manufacturing industry and develop curricular models for implementation by academic in- stitutions. The process expanded in scope and operation over a ten year period to the point that in 1994, a series of workshops organized by the Education Committee of SME produced a formal document entitled Curricula 2002 that included recommendations for curricular contents for the manufacturing engineering and manufacturing engineering technology de- gree programs at the baccalaureate and masters level. The recommendations of Curricula 2002 have not only been the basis for many of the manufacturing programs established since 1995, they have also served as the foundation for establishing the criteria used for accrediting manufacturing engineering and technology programs. In 2008, SME initiated a review of the recommendations of Curricula 2002 and a study of the skills and competencies needed for the long term growth of the manufacturing industry. The process started with the First Manufacturing Education Leadership Forum in Pitts- burgh, PA in June 2008. A diverse group of invited guests representing academia, industry, and government met to assess the need for continuing development, upgrading, and updat- ing of manufacturing education programs. The workshop recommendations are currently 25 being compiled for publication as Curricula 2015 document. Since it was a work-in-progress document, it was expected to take its nal shape after SMEs Manufacturing Education con- ference that was conducted in June 2009 in Austin, Texas. Some of the recommendations reached included the following key components of the manufacturing degree programs : a. Technological Competencies - Product Realization Process Engineering Materials Engineering Mechanics and Design Manufacturing Processes Manufacturing Systems Design, Analysis, and Control Control of Machines Quality Systems Computer Systems Electrical Circuits and Electronics b. Professional Competencies Communication Global Multiculturalism Teamwork Ethics Creativity and Innovation Enterprise Management 26 Manufacturing Information Systems Product Life Cycle Management Enterprise Resource Management Financial Management Human Resource Management and Supervision Entrepreneurship Intellectual Property Rights c. Mathematics and Science Competencies Mathematics Physics Chemistry Bioscience 2.9 Psychology of Learning: While acknowledging the various competency gaps identi ed in manufacturing education, and the methods proposed in order to close these competency gaps, there is also a need to examine the behavioral issues that a ect manufacturing education. Students have to go through the system, and it is imperative that not only the curriculum have the right content, but the manner in which the curriculum is delivered to the students goes a long 27 way in determining the readiness of manufacturing graduates to meet the needs of indus- try. The process of curriculum development is irrelevant without a discussion on the needs of the primary customer, the student in this particular case (Erevelles, 1992, p. 33). A manufacturing curriculum should be designed in such a manner that students learning is enhanced. This leads us to ask the question: what is learning? And how can it be deter- mined that learning has indeed occurred. Learning is a di cult phenomena to de ne, let alone measure.Merriam-Websters dictionary de nes the word teach to mean the following: to cause to know a subject; to show how; to make to know the disagreeable consequences of an action; to guide studies of and to impart knowledge (Merriam-Webster, 1989). Educators need to understand that an innate level of learning takes place in a number of ways. Simply telling students something does not mean they will understand complex the- oretical or social phenomena (Mumford, 1993). Facts and theories are dry, one dimensional and will not take seed unless they are put into context, brought to life and practiced. Theory supported by practice makes understanding, and hence learning, far more likely (Meredith & Burkle, 2008). It is common knowledge that individuals are unique, in the sense that what interests one individual does not necessarily interest the other. Despite these di erences amongst individuals, the variations of behavior between individuals are known to be quite consistent and orderly. This has prompted psychological scientists like Myers and McCauley, 1985 to develop psychological classi cation method, the Myers Briggs type indicator (MBTI). The MBTI can be used to identify learning preferences by plotting four bipolar personality traits which may be combined to yield sixteen di erent personality traits. Among the di erent indices found that are part of MBTI indices include: Extroverted or Introverted (EI) index, Sensing (SN) index, thinking personality (TF) index, and Judgment process index (JP). A good explanation of these indices is found in (Myers & McCaulley, 1985). MBTI can thus be used to develop teaching methods to address the di erent personalities of students and 28 also to understand and accept type di erences in learning styles. A good understanding of MBTI can be useful in developing di erent motivational techniques for di erent learning styles. (Ssemakula et al., 2010), suggested that traditional learning based on lectures favored by engineering academics tends to produce graduates with limited real world hands-on expe- rience favored by industry. While acknowledging the di erent learning styles of individuals, it is important that a manufacturing curricula be designed so as to strike the right balance between the di erent learning styles of individuals. (Lamancusa, Jorgensen, Zayas-Castro, & Ratner, 1995) determined that a majority of engineering students favored more visual and tactile learning styles. In order to enrich the learning experience, the instructor must be pre- pared to develop a portfolio that would stimulate, and be of interest to various personality types. Discussions on the use MBTI in adapting teaching styles to learning styles have been carried out in a number of research studies (Rosati, Russel, & Rodman, 1988). Acknowledging the di erent learning styles is one step in a series of steps required for developing an e ective learning environment. There is also the additional need for educa- tors to agree on what would consist of learning objectives for a particular subject matter. Blooms taxonomy is a classi cation of learning objectives proposed in 1956 by a committee of educators chaired by Benjamin Bloom. Blooms taxonomy classi ed learning objectives into 3 di erent domains: Cognitive, A ective, and Psychomotor. The ful llment of these objectives follows a hierarchy of needs policy, implying that learning at higher level is depen- dent on having attained prerequisite skills and knowledge at a lower level. A goal of bloom taxonomy is for educators to focus on all three domains, creating a more holistic form of education (Anderson & Krathwohl, 2001). At higher domains, mental processing results in greater understanding of the subject matter. Thus the question that needs to be addressed is: In which category would traditional classroom based lectures fall into? 29 Traditional classroom based lectures require low level processing on the part of the stu- dents, while it is anticipated that laboratory exercises can be used to reinforce classroom material. Laboratory exercises designed for manufacturing education would require the high- est level of processing. To complement Blooms taxonomy, other research activities support Blooms theory. In Figure 2.5, the depiction shows the cone of learning, tailoring manufac- turing curriculum to include hands-on manufacturing activities which may lead to enhanced learning, thus ful lling the objective of preparing preparing the graduate for immediate usefulness in the workplace. Figure 2.5: Cone of learning 30 2.10 Problem Based Learning: 2.10.1 Learning through Simulation Games: The development of the manufacturing Lab at Auburn University is intended to support a number of manufacturing related courses by providing a hands-on learning environment. The lab is intended to provide an integrated manufacturing environment. Figure 2.6 below illustrates the interaction of courses intended to be supported by the manufacturing systems lab: Figure 2.6: Interdisciplinary course integration However the focus of this dissertation is related to the problem based learning activities intended to support mainly manufacturing systems courses (INSY 3800) and Lean Produc- tion (INSY 5800/6800). The Lean production and manufacturing systems courses have been 31 taught at Auburn University for a number of years. The courses have been centered on tra- ditional classroom based lecture. In line with the objective of closing the competency gaps of entry level manufacturing personnel in Engineering, it is envisaged that incorporating prob- lem based learning to supplement the lecture based material will enhance students knowledge and interest in the subject matter. Some scholars have posited that Problem based learning not only helps stimulate interest in a subject matter, but also promotes knowledge transfer and long term retention as (Riis, 1995). However, this point still remains contentious as reviewed in (Gijbels, Dochy, Van Den Bossche, & Segers, 2005) and (Prince, 2004). The focus of problem based learning is to provide an experience that a ords participants a sense that they are engaged in a real problem situation, in which case learning becomes a natural byproduct of their engagement and motivation to solve a problem. (Brown & Duguid, 2000) pp 136) points out that people learn in response to a need. When people cannot see the need for what is being taught, they ignore it, reject it or fail to assimilate it in any meaningful way, hence the motivation behind hands-on learning activities. PBL is can be a valuable approach to learning how to implement and practice lean manufacturing because all cultural values that are the cornerstone of lean manufacturing can be practiced. Because the underlying practices of Lean manufacturing di er from the traditional western cultural values that focus on individual achievement, independence, emphasis on short term goals and so on (Holfstede, 1991), it is important to foster a culture that encourages lean social dynamics. Therefore to be successful in lean manufacturing it is important to not only emphasize the hard factors (Industrial Engineering tools for process improvement), but it is absolutely necessary that the so called soft factors that are so vital for process improvement be embraced. The combination of the hard factors and soft factors has seen Toyota continue to out- perform its competitors. Not only does PBM help students acquire knowledge about a particular subject matter, but it also creates an environment where students acquire skills in 32 an experiential way in addition to helping them to learn about themselves and others that are part of the group (Badurdeen, Marksberry, Hall, & Gregory, 2009, p. 3). Therefore Lean Manufacturing education requires that there be training in both soft and hard skills in both the social/cultural and technical aspects. PBL is thus a valuable tool for learning how to implement and practice lean because it embraces the concept of teamwork. The value of PBL has prompted a number of scholars to develop a large number of hands- on simulations used for teaching lean manufacturing concepts in academia and in Industry. A comprehensive list of simulation games developed for lean manufacturing training can be found in (Badurdeen et al., 2009, p. 6). Practitioners of lean manufacturing acknowledge that a successful and sustained lean manufacturing transformation requires the transformation of an organization?s culture, and this has been by far the largest factor that contributed to failure of sustained lean initiatives in most companies. In recent years the use of computers in simulating a lean manufacturing environment has increasingly been used (Feinstein, Mann, & Corsun, 2002). However the e ectiveness of computer based simulation for lean education is limited due to the inability of the technology to facilitate the right kinds of realistic interactivity and collaborations between members. 2.10.2 Use of Simulation and Games in Manufacturing education: Simulation and gaming has been a valuable tool for training purposes in both industry and institutions of learning. A meta-analysis of simulation and games in manufacturing revealed that 75% had a production line focus, meaning that they mainly emphasize the application of lean tools to improve material ow with only a few focusing on enterprise wide operations, which implies that other functional areas that support manufacturing such as logistics and distributions, ergonomics and safety are often ignored (Badurdeen et al., 2009). It would appear that the majority of the Lean simulation and games were developed for large volume, discrete product manufacturing. 33 The study also revealed that the most demonstrated lean tools were cell design and layout, line balancing, pull production and one piece ow, Kanban, quality at the source, standardized work, value stream mapping, cross training, set up reduction, 5S and visual control. Most of the lean simulation games that have been developed for training purposes usually involve multiple iterations, in which a conventional push system is transformed into a pull production. In some cases, lean simulation games involve a single team of participants working through a number of iterations to transform a process by applying lean tools, while in other cases multiple teams work parallel and competing with each other. In some cases of lean simulation and games, the developers have encouraged that participants to these games be divided based on their learning styles e.g. Kolbs Model. Kolbs learning theory sets out four distinct learning styles (or preferences) which are based on a four stage learning cycle). His theory o ers us a way to understand individual people?s learning styles and also an explanation of the cycle of experiential learning that applies to us all. 2.10.3 A review of manufacturing teaching labs in US colleges A review of manufacturing labs in some of the top colleges in the US was done. Sahin, 2006 carried out a survey to benchmark manufacturing labs across US colleges. The top Industrial Engineering and Manufacturing programs were selected to be benchmarked. A summary of Sahin?s nding is given in Figure and it shows that the major focus in labs was in manufacturing automation and manufacturing control. A number of these colleges had some form of Computer integrated manufacturing lab in place, in which the use of Robots, automated material handling and in a few cases the used of radio frequency identi cation technologies were explored (University of Arkansas, Eastern Illinois University). The use of Computer numerical controlled machines was popular among many of the programs surveyed. Rapid prototyping was another common feature among many of manufacturing labs. It was 34 interesting to note that only a handful of programs benchmarked had labs that focused on lean manufacturing system or production related manufacturing concepts. However, further review of literature and Internet search did reveal some programs that did have a production focus. Rochester Institute of Technology maintains a lab called "Toy- ota production systems lab". This lab is supported by Toyota Motor Engineering. The emphasis in the lab is placed on concepts of team work, problem solving by studying funda- mental behavior of production lines. Further review of Lean manufacturing training revealed signi cant number of organizations that o er lean manufacturing related training. Badur- deen et al.,2009, gives a good overview of a number of physical simulation games that have been developed over the years to provide lean manufacturing related training. Again, only a handful of colleges programs appear to o er these type of hands-on type of activities as per- manent part of their curriculum. Fang, Cook, and Hauser (2007) developed a Lean Lego lab for training students on lean manufacturing concepts. Other similar initiatives were found with University of Kentucky (Veebot simulation, ciruit board simulation), and University of Dayton (Pipe factory simulation). Figure 2.7: Manufacturing labs bench marking in US colleges 35 2.11 Methods used for assessing e ectiveness of learning: A signi cant challenge associated with introducing learning methodologies is measuring the impact on student?s learning. This ability to evaluate the e ectiveness of a new learning methodology is key to deciding whether the new learning methodology can be retained as well deciding whether this method can adopted as new best practice for engineering educa- tion. Despite the di culties associated with measuring the value of hands-on manufacturing laboratories, a number of research studies have been designed to evaluate the e ectiveness of new learning methods that have been developed while other research is ongoing. In this section, a discussion on some of these methods will be made as well as their relevance to the development of the manufacturing teaching lab at Auburn University. 2.11.1 Concept Mapping: Concept maps are a procedure that is used to measure the structure and organization of an individual?s knowledge (Novak & Gowin, 1984; Ruiz-Primo, Schultz, & Shavelson, 1997; Stoddart, Abram, Gasper, & Canady, 2000). Concept mapping was originally developed by Novak and the members of his research group as a means of representing frameworks for the interrelationships between concepts (Novak & Gowin, 1984; Stewart, Van Kirk,& Rowel, 1979). Concept mapping is part of a broad family of graphic organizing tools that includes mind mapping, (Buzan & Buzan, 2000) spider diagramming and other related approaches (D. Hay & Kinchin, 2008). A concept map is a hierarchical set of concept labels all linked together, with big and inclusive ideas placed at the top, with exemplary and subordinate ideas below. The concept-mapping method facilitates quick and easy measurement of student knowledge-change so that teachers can identify the parts of the curriculum that are being understood and those that are not. This is possible even among very large student groups. The concept mapping method can be taught in 20 minutes and studies have shown that 36 an additional 30 to 40 minutes can be su cient to make satisfactory maps of most topics. The construction of e ective concept maps has been thoroughly reviewed by (J. D Novak & Canas, 2006). Concept mapping is an application that can be used in di erent elds, and more recently it has been used in mechanical engineering and environmental engineering (Moreira & Greca, 1996; Muryanto, 2005, 2006). For example, Muryanto explored the use of concept maps as learning tool in chemical engineering (Muryanto & Hadi, 2005). The concept mapping tool has made possible new studies of human learning in any context. For example, Otto Silesky, a principal of a secondary school in Costa Rica, sought to apply concept mapping tool in all subject in all grades(Novak. D, 2010, p. 24). 2.11.2 Matching: When random assignment is not possible researchers undertake studies that involve con- trol and treatment groups without random assignment. A good example of such a study was completed by Merino and Abel(2003). They compared the e ect of computer tutorials (treatment) on learning to that of lecture style tutorials (Control). In this approach demo- graphic information e.g. Grade point average, declared major are used as basis for assigning individuals to groups. This demographic data is then used to demonstrate that the two groups share similar characteristics on what is believed to be relevant variables. As a result of this, any performance di erences can be attributed to the intervention. 2.11.3 Baseline Data: If for whatever reason, it proves di cult to have treatment and control groups, then baseline data can be used as basis for analysis. Baseline data are collected to represent the status quo before an intervention is done. Depending on the nature of the research, this data could be taken from participants currently enrolled in the study or from a totally 37 di erent set of subjects. For example, Kashy et al compared student grade distribution in physics for scientists and engineers at Michigan State University before and after computer assisted personalized approach (CAPA) was implemented. Baseline data can also be used in conjunction with self report survey, which can be used as further evidence to support the ndings from the baseline data. 2.11.4 Post-test only: When a pretest is not possible then a post-test design is an option. A study by Ogot, Elliot, and Glumac (Ogot, Elliot, & Glumac, 2003) provides an illustration of how this type of study can be implemented. When post-test only design is used, random assignment to the treatment or control group becomes an important factor. By randomly assigning the subjects to a treatment and control group, factors such as self-selection are eliminated as an in uence to the outcome. Additionally triangulation can be used to explain the validity of the conclusion drawn from the results. 2.12 Strategies for Enhancing the Role of Manufacturing Education The future of manufacturing will depend upon the bold steps taken to prepare a competent workforce and a new generation of entrepreneurs. In the context of global manufacturing, the manufacturing professionals must be prepared not only to seek jobs in established busi- nesses, but to create jobs by establishing new manufacturing businesses. Entrepreneurship must become part of the educational process for the future manufacturing professionals. Furthermore, the e orts to prepare the workforce should place an increasing emphasis on student learning over teaching. The traditional educational process has emphasized the teaching methods as the primary means to prepare a competent workforce. Future e orts toward the development of manufacturing professionals must break the traditional barriers 38 in creating educational opportunities, utilizing the advances in digital and communication technologies and delivering programs all over the world. Manufacturing education must be made available to anyone interested in it, anywhere in the world and at any time they like to learn. Extensive collaboration among the leading educational institutions and industry in a given country or around the world must become part of the means to prepare the fu- ture manufacturing workforce. Organizations such as SME, IMS and others should become agents of change and enable extensive global academic and industry collaboration, ongoing changes in curricular content to address the needs of industry, emphasis on learning over teaching, programs to develop a new generation entrepreneurs, and provisions for access to manufacturing education at anytime and anywhere. Beyond preparing a competent work- force through the educational process outlined, the agents of changage must assume the responsibilities of educating the public on the scope and the prospects of manufacturing in the future. They must also become the leading proponents to shape the policies of the governments at all levels. We believe that a new educational experience is needed to teach the methods and technolo- gies required for 21st century competitiveness.This dissertation addresses new developments in the teaching of manufacturing needed to bridge the competency gaps of manufacturing engineering students of the 21st century. 39 Chapter 3 Design and Development of a Model Learning Manufacturing Lab at Aubunrn University (AU) This dissertation discusses the design and development of a new age 21st century teaching manufacturing lab intended to bridge the alleged competency gaps of entry level manufactur- ing graduates. The development of this lab is an attempt to prove that hands-on education in manufacturing should be made an integral component of the curricula if substantial value to students learning is to occur. In this dissertation we will discuss the design and imple- mentation of speci c lab modules deemed important to manufacturing education. In order to assess the value of the introduced hands-on learning modules to students learning, two student surveys were conducted to establish students? perception with respect to hands-on learning activities related to two courses. The details of the students surveys are discussed in section 4.8 and section 5.7.3. In addition to students? surveys, a post only experimental design where treatment and control groups were subjected to di erent learning experiences was used to assess the e ect of various hands-on labs on conceptual understanding of course topics 5.8. The ndings of this research are related to the work carried at Auburn Univer- sity?s Industrial and Systems Engineering manufacturing lab (Tiger Motors), however, we are hopeful that the results will shed light on how hands-on curricula can be integrated into manufacturing that is largely dependent on the lecture with positive results. In this chapter we will discuss the proposed development and implementation of the teaching manufacturing lab at Auburn University. It is important to note the elements of this lab have taken into consideration signi cant ndings from prior research from various programs put in place in manufacturing education in the past decade. The availability of literature surveyed has 40 helped the author gain perspectives on what an e ective manufacturing laboratory would look like. In this section a discussion on the development of the manufacturing research lab at Auburn University?s Industrial and Systems Engineering department will be given. The following diagram shows the illustration of the inputs and perspectives that may have been considered by various universities and colleges in developing manufacturing laboratories. Figure 3.1: Manufacturng lab model 3.1 Tiger Motors Manufacturing Systems Design methodology This lab was developed to teach hands-on manufacturing systems. A model car factory assembly manufacturing system was designed in a similar manner as the Learning Factory Concept (Lamancusa et al., 1995) , complete with automated material delivery system (Au- tomated storage and retrieval system). Students are required to work in teams to solve manufacturing problems like line balancing, establishing the bu er size between stations, assignment of roles among team members, and participating in continuous improvement meeting, among many other tasks necessary for e cient running of a model factory. Be- cause of the limited laboratory time available for students to solve large problems, students were, in some cases, presented with problems that were incompletely solved. The students 41 were then tasked with solving the problem using the tools presented to them in the lecture. Equipment and hardware for this lab were selected to demonstrate material replenishment strategies, material handling, manual and automated assembly, Inspection, Ergonomics and Safety, Computer aided drafting/Computer aided manufacturing. By seeking academic per- spectives on manufacturing education ( Erevelles, 1992) was able to de ne particular ele- ments/components that would be best learned through hands-on laboratory activities rather than through lecture alone. A decision on what elements/components to include in an e ec- tive manufacturing education laboratory can be best initiated by reviewing the Computer and Automated Systems Association?s Computer integrated manufacturing CASA/CIM wheel, which represents a comprehensive list of important components of a manufacturing enter- prise shown in Figure 3.2 CASA/CIM Wheel. Considering the complexity of manufacturing enterprise as seen in the CIM, selecting elements/components that lend themselves well to hands-on learning is no trivial matter. 3.2 Academic Perspective of competency gaps and alignment with industry requirements: Based on earlier discussions, it is apparent that there is diversity in the manner hands- on manufacturing labs are developed to support manufacturing education, and how they have been implemented in the various programs studied. It is apparent that there is a lack of a common road map in the way the various manufacturing labs in di erent institutions have been implemented. The scope and content of manufacturing based curriculum tend to vary wildly, leading to my belief that programs in manufacturing stand to gain consider- ably if a consensus on the content and scope of e ective manufacturing educational labs is found. Such a consensus would be considerably be useful in developing a set of guidelines and benchmarks for any manufacturing programs. E orts to develop such guidelines have been made in the past, with a number of researchers o ering insights on how this can be 42 Figure 3.2: CASA/CIM wheel done. Erevelles developed a taxonomy for developing a Computer Integrated Manufacturing (CIM) Laboratory after consulting with academics in the eld of CIM (Erevelles, 1992). His work is being used as the foundation for this research. As a rst step for developing an integrated manufacturing laboratory to support manufacturing education, there is a need to identify an exhaustive list of elements, subsystems, and manufacturing process technologies that may be considered potential candidates for inclusion in a hands-on manufacturing lab- oratory. The list of elements considered for this research has taken into consideration the list of CIM elements outlined by Erevelles in addition to other manufacturing related topics 43 derived from areas such as lean manufacturing, Six Sigma business practice, Ergonomics and Safety. Many programs in manufacturing utilize stand-alone courses prior to senior design projects or senior capstone courses. In most cases these prior courses utilize stand-alone lab- oratories. The tools learned in stand-alone topics tend to be limited to textbook problems or projects that are limited in scope. As a result, students have di culties when there is a need to apply the tools in an integrated environment even though they may have excelled in using the tools in the individual courses. In order to develop a new manufacturing lab or to develop an assessment tool that can be used to evaluate an already existing laboratory, as well as attain an understanding of intended learning objectives and experience to be pro- vided by the various laboratories, there is a need to devise a list of elements, subsystems and associated manufacturing technologies that are a vital component of any viable manu- facturing organization. A comprehensive list of elements for consideration for inclusion in practice based curriculum is given on the next page. It has to be acknowledged that this list is signi cantly long and unpractical as it would be impossible to incorporate most of elements into a manufacturing curriculum. 44 3.3 Comprehensive list of elements considered for hands-on learning activities in a manufacturing lab Table 3.1: Business Functions 1. Marketing 2. Demand forecasting 3. Project Management Skills 4. Business Knowledge skills 5. Written and Oral Communication 6. Team work/Working e ectively with others 7. Customer billing 8. Payroll 9. Accounting Finance 10. Cost Accounting 11. Engineering economic analysis 12. Documenting procedures 13. Distribution Table 3.2: Management Philosophies 14. Lean Manufacturing Concepts (5S, Kanban, Continuous improvement etc) 15. Six Sigma Methodologies (DM Table 3.3: Product Design 16. Computer aided Design/Drafting 17. Process Design 16. Facility Design/Plant Layout 17. Manufacturing Ergonomics /Human Factors/ Safety considerations in Process Design 16. Design for manufacture 17. Group technology 3.4 A methodology for selecting of potential elements/components to include in a hands-on manufacturing teaching lab As a rst step toward developing an e ective manufacturing laboratory to support man- ufacturing education, it is necessary to select elements that are considered important for manufacturing education and to determine which of these elements are best learned through 45 Table 3.4: Information and Decision Support Systems for Factory Management 18. Communication Networks and Protocols 19. Radio frequency identi cation technology applications in Manufacturing 20. Process simulation software 21. Systems Integration Software 22. Database management Table 3.5: Manufacturing Control: 23. Process monitoring 24. Process control 25. Shop oor control 26. Computer aided inspection/Testing 27. Diagnostics/Error Recovery Table 3.6: Manufacturing Process Automation: 28. Automated Material handling 29. Automated Assembly 30. Automated Packaging 31. Programmable logic Controllers 32. Direct/Distributed Numerical control 33. Adaptive control 34. Finishing/Coating 35. Flexible Manufacturing Cells 36. Foundry/Casting 37. Plastic Injection Molding 38. Machine Vision 39. Metrology 40. None Traditional machining 41. Robotic Manufacturing in manufacturing 42. Sensors in manufacturing 43. Sheet Metal Fabrication Table 3.7: Manufacturing Planning 44. Bill of materials for processes 45. Materials Requirements planning (MRP ) 46. Pull Production systems (Kanban) 47. Cost Estimating 48. Database management 49. Computer aided process planning 50. NC part programming 50. Scheduling 46 hands-on laboratory exercises. The relative importance of each of these elements can be de- termined through stakeholder surveys. There are a number of stakeholders in manufacturing education, which include students, their prospective employers, and faculty of manufactur- ing education. Faculty members are considered designers of the education system. Thus it is important that all stakeholder requirements, with regards to the content of and scope of manufacturing education, be solicited if e ective hands-on manufacturing laboratory is to be developed. . One way of soliciting stakeholder perspectives can be achieved through stakeholder surveys. As a rst step in determining the important elements of a hands- on manufacturing laboratory (Erevelles, 1992)) hypothesized that the academic community would classify some of the so mentioned elements as more important in an instructional setting than other elements. To test this hypothesis, a survey was conducted to determine faculty perspectives on the content and scope of a hands-on manufacturing laboratory. A qualitative scale for determining the relative importance of each of the elements was de ned in the following manner: Necessary/Required elements: These elements are considered as the must have ele- ments in the lab Useful element: These are not as important but add value to the manufacturing lab by adding onto the capabilities of the lab. Optional elements: These are lowest ranked elements in a manufacturing lab, and can be viewed as nice to have. The exclusion of any element from this list will not adversely a ect the quality of instruction Not needed: this accounts for any elements that should not be considered as part of a manufacturing lab. 47 However, in Erevelles? work, consideration for manufacturing employer?s perspectives were not taken into account. Since manufacturers are a big stakeholder in manufacturing educa- tion, it becomes imperative that both academia and employers? perspective be sought and aligned if an e ective manufacturing laboratory is to be developed. As part of this research a survey instrument has been developed to capture employers? perspectives on the subject, and the results of this survey will be compared against perspectives of academia, so as to determine the competency gaps that need to be addressed through hands-on laboratory exercises. 3.4.1 Determination of adequate teaching levels for identi ed elements : An additional domain that was considered is the teaching domain. The teaching domain identi es the level of teaching that would be considered adequate for successful learning to occur for a particular element. This domain answers the following question: What teaching level Y method should be applied to element X to e ectively impart learning to the students. For the purpose of this research the teaching levels are illustrated in Figure 3.3: Figure 3.3: TeachingLevels 48 The levels depicted in Figure3.3: are used to de ne the minimum level at which instruction for a particular manufacturing topic can be considered e ective. From the above discussion it can be noted the three steps or dimensions needed to determine the content and scope for an e ective manufacturing laboratory can be illustrated in 3 dimensions as in Figure 3.4 below. Figure 3.4: Dimensions for de ning the content and scope of manufacturing hands on learning 3.4.2 A three dimensional model for establishing interdisciplinary components of manufacturing teaching lab: In order to determine how each de ned element in the manufacturing education pro le t into 3 dimensional structures shown in Figure 3.4, a stakeholder survey will be carried out. It has to be noted that this survey will only seek perspectives of manufacturing employers, taking into consideration the work done by Erevelles as he had already established the perspectives of academics with respect to CIM lab development. To compliment his work, a survey seeking the perspective of manufacturing employers will be carried out. The objective for developing this survey instrument were as follows: 49 3.4.3 Research Objectives for manufacturing industry perspective survey 1. This study is intended to evaluate what target skill-set manufacturing employ- ers expect entry level manufacturing/Industrial engineers to posses. This study also seeks to expose employer?s perceptions regarding areas in a manufacturing curriculum that need to be improved to better close the gap between employ- ers desired skill-set and the educational training given to students as part of the manufacturing curriculum (Competency gaps). This study will also evalu- ate manufacturers current level of involvement or anticipated future involvement with emerging technologies that are predicted to be an integral component in the manufacturing. 2. Establish manufacturers? perspective on what they would consider to be impor- tant elements of a hands-on manufacturing laboratory intended to prepare un- dergraduates students for 21st century competitiveness. 3. Establish manufactures? perspectives on what they would consider to be appro- priate level of instruction associated with each element of manufacturing pro le as illustrated in Figure 3.4 4. Based on ndings, develop best practice for manufacturing education for 21st cen- tury competitiveness and assessment instrument that can be used for evaluating the e ectiveness of manufacturing curriculum. 3.4.4 Research Questions for the Questionnaire: 1. What level of competency in various components of manufacturing education are employers seeking in entry level manufacturing/industrial engineers 50 2. From a historical perspective, what are the employer?s perceived inadequacies of entry level manufacturing/Industrial engineers that have required further train- ing. 3. What does the manufacturing industry envisage as the best way for training manufacturing engineering students and what recommendations do they have regarding manufacturing curriculum 3.5 A Manufacturing industry perspective on the important elements required for manufacturing education A survey seeking the perspectives of manufacturing industrialist on how manufacturing education curricula should be shaped in order to better prepare students for careers in the manufacturing industry was conducted. The survey was anonymous and was conducted through using an on-line survey software Qualtrics. Survey participants were invited through email to participate, as well as through an open invitation through social networking websites Linked-In. The survey sought to establish what manufacturing industry representatives considered important elements that need to be emphasized in a manufacturing curriculum. The survey was made of 20 questions composed of multiple choice, matrix, and rank order type questions. The full survey is given in appendix A. 3.5.1 Participant demographics A total of 50 survey responses were obtained. However, only 30 participants participated fully in the survey and the results presented for this survey re ect this number. 29% of the respondents were executives while 47% were in upper management positions, and about 6% held professional positions like Quality engineer, Safety manager etc. About 35% of the 51 respondents worked for companies involved in some form of metal fabrication (automobiles, aircraft, machine building), 6% in metal processing industry, while 53% worked for compa- nies involved in the manufacture of other types of products. 65% of respondents? indicated working of companies involved with high volume, low variety type goods while 35% respon- dents? companies were low volume, high variety manufacturers. A number of responses to statements put through to participants will be discussed. Q9:Statement 9. In order to establish what competency gaps may exist in the manufacturing industry, participants were asked to express their perception regarding what competencies could be improved through the introduction of hands-on oriented manufacturing activities in the manufacturing curriculum. Participants were required to state their agreement with the following statement: "Taking into account your own experience as an entry level professional and any interac- tions you may have had with other entry level professionals in manufacturing related jobs, please indicate your agreement with the following statement": Introducing a hands-on approach to teaching the the given topics in a manufacturing curriculum at college level would be bene cial in addressing the competency gap in manufac- turing. Participants were given a list of potential topics relevant to manufacturing that could be introduced into the manufacturing curricula through the use of hands-on laboratory activ- ities. Participants were asked to indicate the topics that they felt could be most bene cial in addressing the competency gaps of students. 52 Table 3.8: Ranking of important competencies by industry representatives Problem solving skills 0 0 0 0 0 6 11 17 6.65 Teamwork/ work e ectively with others 0 0 0 0 1 7 9 17 6.47 Written and Oral Communication 0 0 0 1 1 5 9 16 6.38 Product and Process Design 0 0 1 0 3 4 9 17 6.18 Manufacturing process Control 0 0 0 1 3 5 7 16 6.13 Manufacturing Systems knowledge 0 0 0 1 2 10 4 17 6 Quality Systems knowledge 0 0 0 1 3 9 4 17 5.94 Project Management 0 0 1 1 3 6 6 17 5.88 Speci c manufacturing process Knowledge 0 0 0 1 5 6 5 17 5.88 Business knowledge/Skills 0 0 0 1 5 7 3 16 5.75 Supply Chain Management 0 1 0 2 5 6 3 17 5.41 Materials knowledge 0 0 0 5 4 4 4 17 5.41 International perspectives 0 2 3 4 5 3 0 17 4.24 Table 3.8 shows the ranked competencies by manufacturing industry representatives. it is clear that problem solving skills, teamwork as well as written and communication skills are competencies that participants felt could bene t from hands-on skills. 53 Q10:Statement 10 Participants were asked to rank a list of identi ed competencies according to how impor- tant or relevant they were to manufacturing industry. This input is important for prioritizing the order in which particular elements could be introduced or improved in any manufacturing curriculum, or used to evaluate current elements of a particular manufacturing curriculum. Table 3.9: Desired competencies in manufacturing 3.5.2 Establishing potential manufacturing laboratory elements and level of instruction required for e ective learning With a large of number of potential elements that could be integrated as part of hands- on manufacturing education, it becomes necessary to classify all the elements according to how important each of these elements are to the manufacturing industry and thus prioritize them for inclusion in a manufacturing curriculum. A comprehensive list of these elements was given in section 3.3. Erevelles (1996) had hypothesized that the academic community would classify some of these elements as more important in an instructional laboratory than other elements. He went on to conduct a survey to capture those perceptions. We will discuss some of these ndings along with our own survey results that sought the perceptions of manufacturing industry professionals. In ranking the potential elements, four levels of importance (Necessary, Useful, Optional, and Not needed) were used as shown in gure 3.4 54 . In addition to classifying each element according to importance , it was also necessary to understand what method of instruction could be used to teach that particular element to e ectively elicit student learning. These levels of instructions range from one being able to teach e ectively at the conceptual/theoretical to one in which industrial grade equipment would be required to teach e ectively. These teaching levels are illustrated in gure 3.3 Q13:Statement 13-Establishing potential product design elements for hands-on integration. Participants were asked to rank a list of elements associated with product design using the importance scale discussed (necessary, useful, optional and not needed). In addition to ranking the importance of each element, they were required to o er their perceptions regarding the minimum teaching level required to e ectively teach that element. Figure 3.5 and gure 3.6 shows the results of academia perspectives (Erevels, 1996) and industry perspectives from this study respectively. Figure 3.5: Academia perspective of product design elements, Ereveles (1996) Figure 3.6: Manufacturing industry per- spective of product design elements Figure 3.6 shows that most academia survey respondents indicated CAD,CAE, and Pro- cess design as the important elements of product design. They also indicated that for e ective learning to occur, each of those elements would be needed to teach above the physical level of instruction, with CAD requiring industrial grade/commercial software to be e ectively taught. On the other hand, survey respondents from the manufacturing industry favored 55 Process design, facility design, and DFM/DFA as integration elements. They believed those elements needed to be taught at a minimum using scaled down equipment as indicated in gure 3.6. Q15:Statement 15-Establishing potential automation and new technologies el- ements for hands-on lab integration. In a similar manner to Q13, participants were asked to rank various elements of automation and associated technologies according to importance. In addition to the ranking, the respon- dents were also required to suggest the method of instruction that was adequate to elicit students? e ective learning of that particular element. Responses to these survey questions are summarized in gure 3.7 and gure 3.8. In a survey conducted by Erevelles, 2006 in which the perception of academia was sought, ndings suggested that automated assem- bly, automated manufacturing handling, robotic assembly, Computer numerical control, and sensors are important elements that needed to be taught using a minimum of scaled down equipment or industrial grade equipment. Figure 3.7: Academia perceptions of au- tomation and new technology elements in- tegration, Ereveles (1996) Figure 3.8: Manufacturing industry per- ceptions of automation and new technol- ogy elements integration Figure 3.8 shows the summarized responses from manufacturing industry respondents. In- dustry respondents believed that the elements indicated in gure 3.8 are necessary to include in a manufacturing curriculum. A majority of respondents perceived exible manufacturing 56 systems (FMS), programmable logic controller (PLC), and Automated storage and retrieval systems (ASRS) as important elements in any manufacturing curriculum. Respondents found it necessary that FMS and RFID be taught using industrial grade equipment/Software, while teaching at the scaled down version was perceived as adequate for teaching ASRS. In ad- dition, a relatively larger percentage of respondents indicated that automated inspection, PLCs, and machine vision would be useful elements to include in an e ective manufacturing curriculum. Q17:Statement 17-Establishing potential Manufacturing planing elements for hands-on lab integration. In a similar manner, participants in both academia and manufacturing industry were asked for their perceptions of what elements of manufacturing planning would be important for integration in a manufacturing curriculum, and what method of instruction would be adequate for e ectively teaching the identi ed elements. The results are shown in gures 3.9 and 3.10. According to academia perspectives the most important element associated with manufacturing planning are NC part programming, Computer aided process planning, and MRP. On the other hand, manufacturing industry survey respondents indicated that time studies, assembly line balancing , and capacity resource planing (CRP) are necessary elements in a manufacturing curriculum. Each of these elements were identi ed as requiring instruction at the physical level or above as indicated in gure 3.10 3.5.3 Discussion All elements that are deemed as necessary can be considered as the must have elements in a manufacturing curricula, while the useful elements are those that are considered good to have. It is thus prudent to say that necessary elements could be those elements that 57 Figure 3.9: Academia perceptions of man- ufacturing elements integration, Ereveles (1996) Figure 3.10: Manufacturing industry per- ceptions of manufacturing elements inte- gration should be considered for inclusion as part of requisite courses while useful elements could be considered for inclusion in elective courses in a manufacturing curricula. The perceptions of respondents from manufacturing industry displayed some similarities with those academia as reported by (Erevelles, 2006). However di erences in perceptions on the importance of each identi ed element and associated teaching level prescribed still existed. There is therefore the need to balance the perceptions of both industry and academia in building and e ective manufacturing taxonomy. Table 3.10 shows a summary that could be used as guideline for setting up a new hands-on oriented manufacturing curriculum. 58 Table 3.10: Industry and academia perceptions of the important manufacturing elements and associated teaching levels 59 Chapter 4 Developing the lab for bridging competency gaps of manufacturing graduates The rst step in developing a best practice manufacturing education and research labora- tory, where integrated topics in manufacturing span various functional areas, is the creation of the model physical factory. This factory should have various appropriate hardware com- ponents as well the product to be manufactured. In the case of the Auburn Manufacturing lab, a small scale Lego based model automobile assembly plant (Tiger Automotive) that is used to assemble 3 models of a Lego vehicles was selected. The creation of educational sys- tems that emulate the complexity of industrial systems for studying manufacturing systems is not a trivial task. In order to create an environment with the intricacies of an industrial setting, three models of Lego cars were selected, namely: the Speeder, SUV and a Convert- ible model (See, Figure 8). These models have a total of 96 unique parts that are used for assembly and as many as 270 parts going into the assembly of a vehicle. The idea of using Lego for educational purposes is not a new idea. Several examples exist of the use of the Lego concept in tertiary education and research for manufacturing systems simulation, Lean manufacturing principle, mechanism design, and virtual prototyping. However, a study of the documented activities did not yield an example that compares in size and scope as the one being developed at Auburn University?s Industrial and system department. 4.1 Tiger Motors oor layout and workstation design A mixed model assembly line with 15 stations was selected to be used for assembling two models of vehicles on a mixed assembly line. The manufacturing system was designed 60 Figure 4.1: Models of Vehicles assembled at Tiger Motors to incorporate some element of (CIM). This was achieved by designing a semi automated workstation complete with machine vision capability for automated inspection. All the man- ual workstations were retro tted to allow re-recon gurability of the manufacturing system. The re-recon gurability of the layout allows students to experiment with di erent layouts discussed during the lecture. The proposed shop oor layout is shown in gure 4.2. Students worked in teams in solving line balancing problems (LBP). Each team was allocated a man- ufacturing work cell (Cell-1, Cell-2 and Cell-3, as depicted in gure 4.2) whose workstations were characterized by unbalanced workloads . Each team was led by a team leader who was responsible for coordinating the activities of the cell. Unlike textbook line balancing prob- lems, the lab line balancing problem was more realistic, similar to a real world line balancing problems. The lab line balancing problem had the following additional tasks: 1. Students establish standard times using stop watch time study or any the prede- termined time motion studies (PTMS) 2. Students establish precedence constraints from assembly charts 3. Students establish resource and zoning constraints 61 Figure 4.2: Material replenishment routes The red arrows depict the ow of raw material stock from the storage area to the point of use at each workstation, while the green arrows depict the ow of work in process ma- terial (WIP) along workstation and manufacturing cells. It is evident from 4.2 that the manufacturing system is arranged in three distinct departments , namely: 1. Under-body/Chassis Assembly 2. Cab Assembly 3. Trim/Final Line Cell 1 is where production begins. Cell 2 and Cell 3 are progressively upstream depart- ments. Cell 1 products all go to the beginning of Cell 2, and similarly all Cell-2 output goes to beginning of Cell 3. Students are divided into equal groups and assigned to a manu- facturing cell. Each manufacturing Cell acts as an autonomous department responsible for decisions associated with running that particular cell. 62 Figure 4.3: Tiger motors Lay out con guration used The semi-automated assembly station consists of Selective Compliance Assembly Robotic Arm (SCARA) Adept One Robot. This Adept One Robot is a three axis robot which is suitable for assembly operations. This station serves the purposes of teaching students aspect of automation in assembly operations, in particular robotic programing as well as for demonstration purposes. The robotic assembly station has the ability of be integrated into the system, replacing a manual workstation in Cell-1. Because of the large class size and safety concerns, student interaction with the robot was only limited to demonstration purposes. However, through the use Adept Ace Emulation software, students were taught hands-on robotic programming. 4.2 Design and implementation of material replenishment strategy for Tiger Motors Manufacturing system One of the challenges that was faced in designing Tiger Motors Manufacturing system was planning for material replenishment and implementing an e cient shop oor material delivery system. Traditionally MRP has been the system of choice as a means of ensuring that material needed for production is available to meet demand. MRP planning was pio- neered in the 1970s by Joseph Orlicky and others and later got a boost when the American 63 Production and Inventory Control Society (APICS) launched its MRP crusade to promote its use. Since that time MRP has become a principal production control paradigm (Hopp & Spearman, 2001., p. 110). MRP is a push system since it works backwards from a production schedule of an independent demand item to derive schedules for demand components. MRP computes schedules of what should be started or pushed into production based on demand. However, there are inherent disadvantages associated with using MRP, which include the cost associated with software, which often needs maintenance from a well quali ed personnel . An alternative to MRP is a much newer Kanban production control which is a simpler, more visual system, and more responsive system. It is therefore important for students in manufacturing to have an innate understanding on how both systems work so as to be in a position to apply the tools appropriately. In this section we discuss the development of Kan- ban controlled production material replenishment systems. A review of literature on how to teach MRP in a laboratory setting revealed a scarcity of information. MRP software is ex- pensive and is di cult for educational institutions to acquire for the purpose of acquainting students with its intricacies. However, to demonstrate the di erence between the workings of the two systems, two master production schedules, each representing the normally large batch sizes associated with MRP systems, and the smaller batch sizes associated with leveled production found in pull based manufacturing systems were demonstrated as illustrated in 4.3 Figure 4.4: Master Production Schedule: MRP Vs Kanban The master schedule is speci c regarding the products to be manufactured through the planning horizon. Although the planning horizon for MRP and Kanban could be similar, typical di erences lie in how the products are sequenced and lot size as depicted in gure 4.4. For a variety of reasons that we will not dwell on , the lot sizes in MRP systems are 64 much bigger than in Kanban systems. In conducting the lab, the e ect of lot sizes on the performance of the manufacturing system was demonstrated. In the the implementation of a Kanban system, key decisions that needed to made included the choice of a material replenishment system. In assembly systems, three distinct material replenishment strategies are typically available, namely: 1. Line-stocking 2. Kitting 3. Kanban Continuous supply Figure 4.5: Material replenishmentpolicies In order to make a choice among the strategies listed, there was a need to understand how each of them work and how they would impact the overall e ciency of the manufacturing system with regards to Work in Process (WIP), material handling e ort, space utilization, and personnel requirement and costs. Although acknowledging the availability of a number of quantitative methods for deciding on replenishment policy, the selection of policy in this particular case was solely based on qualitative comparisons, as the main aim was to increase awareness of the di erent strategies. 65 4.2.1 Kitting material replenishment strategy In kitting, parts inventories are kept at the assembly stations, with an assortment of parts required for a speci c operation all put into one container. The kits are prepared in a central stockroom utilizing a pick list generated from an order of bill of material. This method was not selected for use at Tiger Motors based on the fact that it is labor intensive and requires generation of a pick list which is tedious to accomplish, taking into account the numerous re-balancing of the line which is typical of assembly lines. 4.2.2 Kanban based just in time replenishment strategy In this strategy each di erent part number is put in an individual container and supplied to the assembly line ( gure 4.5). Component containers are moved just in time to the point of use leading to a continuous ow of material. This strategy requires the set up of a supermarket where exchange of empty containers and full containers is done. A key decision regarding this strategy concerns establishing the quantities of parts in each container, and ultimately the frequencies of deliveries to be made to point of use. This leads to a trade-o between service level , holding cost, and transportation cost. This strategy was selected because it is the easiest to implement since it is manual based, solely relying on kanban cards as the control mechanism. 4.2.3 Line stocking This is the traditional system and parts are stored in bulk containers along the line and periodically replenished. This strategy requires bigger containers, and raw parts and is expected to last much longer before replenishment is done. The frequency of volume moves is less, however the holding costs are higher with line stocking. 66 Description of components parts used for assembly On average each vehicle at Tiger Motors uses around 273 parts and there are about 95 unique parts. Lego is a popular line of construction toys manufactured by The Lego Group. It consists of colorful interlocking plastic bricks. Lego bricks can be assembled and connected in many ways, to construct objects such as toy vehicles and buildings among many others. Lego pieces come in 3 main di erent classes as indicated below: Figure 4.6: Categories of Lego Bricks Considering the large number of parts used in each vehicle, the raw material replenish- ment was a challenge. The goal was to demonstrate material replenishment strategy using the Kanban replenishment strategy. Establishing the stock levels for each part type thus became important. The stock level established for each part contained in replenishment bins establishes the frequency at which each part in the bill of material was to be replenished. To prioritize the replenishment intervals for di erent parts, ABC classi cation inventory classi cation was used. In ABC inventory classi cation, three categories of inventory are recognized. The A class inventory which is considered to represent the critical few items that constitute biggest cost, the B class which is the immediate class, and the C class consists of the trivial many. the boundary between the classes is usually a matter of company policy. 67 In this project establishing the cost of individual parts was a challenge since vendors do not sell individual parts, but sell kitted units. To overcome this obstacle it has been assumed that the cost of each part is proportional to its volume. The volume of each part in the assembly bill of material was established. Therefore multiplying the volume of each part in the BOM with the frequency that the part is used in each vehicle establishes the total volume requirements of that part in a vehicle. Figure 4.7 shows a partial table used to calculate the required Kanban quantities. If the number of containers used is not an issue then the number of containers required at station j to avoid starving was calculated in Column p. However, Tiger Motors had a limited number of containers, thus the decision to adopt a 2 bin system. The corresponding container quantities for this system is shown in column k. Figure 4.7: Excel formulation of Kanban quantities Using column k (total Volume), a Pareto chart was drawn to establish the priority by which parts needed to be controlled. Figure 4.7 illustrates the classi cation of raw materials in the bill of materials. The classi cation is as follows: Class A: Raw material that cost the most and accounts for above 50 % of the total cost of raw material used in the vehicle. This classi cation of raw stock will be replenished 68 Figure 4.8: A-B-C-D classi cation of raw material stock three times during a production run. Around 13% of the raw stock accounts for 50% of the total cost of raw material. Class B In this category are those parts which are only replenished twice during the pro- duction run (Shift). These parts make next up 13% of total number of parts in the BOM. These parts account for about 16% of the total cost of the car. Class C In this category you nd parts that are only replenished once during a shift. These parts consist of about 13% of the total number of parts in the BOM and account for about 12% of the total cost of a vehicle. Class D In this category you nd parts that do not need to be replenished during a shift. These parts consist of around 60% of the total number of parts in the BOM but account for only 20% of the total cost of the car. 69 4.2.4 Establishing the adequate number of Kanban card for each part Material required in the cells were retrieved from the ASRS (location 1) as illustrated in gure 4.2 and temporarily stored in SuperMarket bu er. At the SuperMarket, material handlers pick parts as indicated on a Kanban card attached to the empty bin and place them in the bin. The Kanban card contains information about the quantity of raw material that is required at a particular station within a cell. The Kanban cards will be used as the basis for which raw material replenishment is done. The individual part numbers will be stored in the ASRS system. Material from the ASRS system will thus be retrieved as needed in the cells. Using a Kanban material replenishment policy, material is resupplied at each station with a lead time of LT in separate containers dedicated to each component type. De ning nij as the number of items of the component utilized at station j. Then, the number of containers needed at station j to hold the material needed to avoid starving during the supply lead time (with zero bu er stock) is ncontij and the total number of utilized containers is Nctot. ncontij = LT D nijmin[Vc vi ; pmax pi ] (4.1) Nctot = MX j=1 NX i=1 ncontij (4.2) Vc vi in equation 4.1 above represents the standard number of units of part i that can be held in a standard container size. The supply lead time LTDnij can be viewed as an estimate of how long the consuming process will need to wait for parts once replenishment has been authorized. The replenishment lead time dictates the number of parts that must be available at the consumption point to assure production can continue uninterrupted until replenishment parts arrive. The larger the replenishment lead time (LTDnij) , the greater the amount of inventory in the system. Factors that may in uence LTDnij include (Vatalaro 70 & Taylor, 2003, p. 42): (1) The number of orders that arrived at the supplying process ahead of the one just sent, (2) Service time at the point of use, (3) Quantity of parts in the container, (4) The replenishment signal method, and (5) Product transit time. For the purposes of this project, 3 di erent sized containers were selected for holding individual parts. The bigger containers are used for the larger volume parts while the small containers are used for small volume parts. Taking into consideration the 1 hour available time to conduct live simulation runs, two sets of replenishment lead times were selected to be used. In accordance to ABCD inventory classi cation, Class A parts were assigned a lead time LTDnijA of 10 minutes, while Class B parts were assigned LTDnijB of 15 minutes. Class C and D parts are replenished once and expected to last the entire simulation run. Since the parts used in our systems were small in volume compared to the size of the container, a 2 bin material replenishment was implemented (see Column R in gure 4.7 on page 68. In the 2 bin system, ncontij = 2 . To establish container quantities for each part type i at station j , equation 4.3 is utilized. Qtycontij is the quantity of parts needed in each container in a 2 bin system, D is the Demand (Units/ hour), and nij is part count of parti that is needed at station j. Qtycontij = LT D nij2 (4.3) The objective for the parts replenishment for the AU Model Manufacturing Factory is to maintain minimum inventory while ensuring that parts required during the production period (physical simulation run) do not run out. A minimum of 24 students, divided into 3 groups of 8 students each, are required to do simulation run lasting 1 hour. Each team is assigned to a manufacturing cell (see gure 4.3), and is empowered with making decisions that a ect the performance of their respective cells. The frequency of replenishment cycles and thus the amount of inventory in the system is constrained by the capabilities of the personnel that each team assigns to this task. The material replenishment team is responsible for restocking the parts bins with the right quantities of material and ultimately delivering them to the work 71 cells at the right time. It is important to note that in the formulation of the 2 bin material replenishment strategy of Tiger Motors the replenishment lead time is established. The key has thus become establishing the resources needed to meet this objective. LT is a function of the capacity of material handling people to re ll the empty containers at the supermarket. A stopwatch time study was carried out to establish the amount of time required to pick parts at the supermarket, the results of which are illustrated in gure TimetoPickparts . Figure 4.9: Time required for picking parts at SuperMarket After a regression relationship shown in gure 4.9 that enabled the amount of time re- quired to pick parts was established, it became possible to determine the amount of time that would be required to pick all the parts of a particular class, thus enabling the determination of manpower requirements for ful lling parts replenishment with starving the station in each cell. Figure 4.10 shows an excel formulation that was used for determining the replenishment time requirements as well as input for automated printing of Kanban cards. The time required by one man to replenish all parts of a certain class is shown in cell range (Z5:AB7). For instance, all group A parts are always replenished after 8 cycles while group B parts are replenished after 13 cycles. For example a total of 14 minutes is required to replenish all 72 Figure 4.10: Establishing time required for picking parts at Supermarket parts in Cell-1. Referring to the gure 4.10, column D contains the part number, column F contains the operation number, and columns A and B contain the supermarket address where the part is stored in the supermarket. Column C contains the station where the part is used. Column C is determined by line balancing which assigns operation to stations. Once all of the Kanban quantities and assignment of operations to stations were done, printing of Kanban cards was the next required step. Because table 4.10 contains all the information needed in a Kanban card, a worksheet for printing kanban cards that referenced particular cells containing pertinent information in table 4.10 was created. 73 4.2.5 Automatic Kanban card updating and printing formulation in Excel Figure 4.11: Kanban card automatic updating excel worksheet Figure 4.11 shows the kanban card used for authorized replenishment of material to the stations. The kanban displayed shows key information necessary for the running of a kanban pull system. The following minimum information is displayed on the kanban card: Part identi er identi es the part type required. Figure 4.11 shows two kanban cards used for part numbers 2 and 38. The external and internal supply process. The supermarket address A1 and A2 indi- cates where the part numbers 2 and 38 are to be located in the upstream supplying process. The container quantity is important as it determines the permissible stock quantities needed in each container to avoid starving at the downstream consuming process. These quantities were calculated using equation 4.3 in the excel worksheet 4.10. The Assay address indicated in Cell A5 shows the consuming process. This shows where the container of parts is to be delivered to. The displayed Kanban card indicates station 1 as the consuming process. Because this assembly address can change depending on the line balancing solution or re-balancing of the line, it was necessary to create an automated Kanban updating. All the information described above is linked to Excel, a database that automatically updates the Kanban information subjected to revisions. 74 Figure 4.12: Container arrangement at a workstation Figure 4.13: Kanban card attached to a container Figure 4.14: SuperMarket intermediate storage area Figure 4.15: Push cart for material deliv- ery Figure 4.12 through gure 4.15 illustrated the implemented material replenishment system at Tiger motors. As per ABCD material classi cation described, class A and B materials were replenished on a regular interval as indicated by 2 bin Kanban system (see gure 4.12). Figure 4.13 shows the Kanban card attached to parts container. The raw material needed at each station is stored at the supermartet shown in gure 4.14. A material handler used the cart shown in gure 4.15 to replenish parts at regular intervals. 75 4.3 design of a hands-on assembly line balancing lab Assembly line balancing is an integral part of manufacturing systems where assembly operations are common. The textbook assembly line balancing problem is an oversimpli ed version of the real world assembly line balancing problem and does not provide the student with a realistic assembly line balancing experience. It is on this basis a realistic assembly line problem that has been implemented. The simplest form of the assembly line balancing problem is dubbed SALBP (Simple Assembly Line Balancing Problem), and was used as basis for developing the hands-on learning lab. 4.3.1 Introduction The Classic assembly line balancing problem is a problem where a given set of tasks, task durations, precedence constraints among the set of tasks, and a set of workstations, assign each task to exactly one one station in such a way that no precedence constraints are violated (Becker and Scholl, 2004). When a xed cycle time is given, the cumulative task time for any particular station cannot exceed the cycle time. this type of line is called paced line. In paced lines the cycle time cannot be smaller than the largest task time. A good example of a paced line is an assembly with stations linked by a conveyor belt. On the other hand, in the absence of xed cycle time, all stations operate at individual speeds and instances of a workstation becoming idle are of common. To mitigate the e ects of station idleness bu ers can be used between stations. Unpaced lines are thus faced with the additional decisions of sizing the bu ers, as well as positioning the bu ers within the line. Since assembly line balancing is a very common and important problem in many manufacturing environments, it was selected as a practical problem that would allow students a realistic environment for implementing assembly line balancing. LB is a classic operations research optimization problem that has been studied for many years. Despite the e orts that academics have 76 expended on this problem, there are still are a few commercially available types of software to help industry deal with this problem (Becker & Scholl, 2006). Assembly line can take many variants. If one product is assembled, then the assembly line is a single model assembly line. This is the simplest variant of the assembly line since all work pieces are the same. If several products are assembled on the same assembly line, a mixed model assembly line results, and there is the additional sequencing problem that is needed to determine the sequence in which models are introduced into the line. If the same line is used produce di erent models, with each model introduced to the line as a batch and with the di erent models separated with intermediate setup operations, then multi-model line results with inherent need to determine the lot sizes of vehicles that are introduced to the line. Another important characteristic of assembly lines that is often ignored is the considerable variation due to the instability of humans with respect to work rate, skill and motivation. This leads to highly stochastic assembly task times. However, the stochastic nature of tasks can be reduced through learning e ects or successive improvement of production processes ((Boucher, 1987; Chakravarty, 1988). In general, the variance of task times increases with complexity. Various distributions for task times have been suggested by di erent researchers. Moodie and Young (1965) for example assumed tasks times to be independent normal variates which can be considered realistic for human work. 4.3.2 Mixed models assembly Lines: Mixed- model lines manufacture several models of a standardized commodity in an in- termixed sequence. The models may di er with respect to size, color, material used, or equipment used on them. As a result the task times of the models di er and the challenge is to determine a line balance whose station loads have the same station time and equipment requirements for whatever model is produced. This requires exibility in the equipment used and the quali cation of operators. Finding a line balance where the stations loads have 77 the almost identical station time and equipment requirement o ers the greatest challenge in mixed-model assembly. In addition to balancing workloads between stations in mixed model assembly, the sequencing of models is an important aspect of mixed model assembly. If several work intensive models follow each other at the same station, the cycle time might be exceeded, which requires some kind of reaction to overcome it (line stoppage, utility workers, o ine workers )(Boysen, Fliedner, & Scholl, 2008). The only way of avoiding exceeding the cycle time is to nd the sequence for the models which cause high station times to alternate with less work intensive ones at each station. 4.3.3 multi-model assembly lines Multi-model lines produce di erent variants of product and are produced in batches be- cause the uniformity in products is not su cient to enable ease and quickness of changeovers from one product to the other. In this instance a trade o problem occurs when deciding batch sizes and sequences. 4.3.4 Formulation of assembly line balancing problem SALBP usually takes into account two constraints which may be cycle time and precedence constraints, or the precedence constraints plus the number of workstations. 4.3.5 Inadequacies of SALBP The constraints used in SALBP can in many cases be insu cient for adequately addressing practical assembly line balancing problems that normally are more complex and exhibit far greater numbers of constraints than considered in SALBP. Despite this inadequacy SALBP has remained the single most researched variant of assembly line balancing problem by 78 academics. This inadequacy of the SALBP has not escaped the attention of researchers, thus the development of the generalized assembly line balancing problem (GALBP) which is an extension of SALBP. Although even simple SALBP is NP hard, in most cases it fails to capture the true complexity of the problem in real life. GALBP is thus an attempt to close the gap between the academic LB problem and actual problem being faced by industry. Despite the di culty of solving assembly line balancing problems to Optimality, many assembly line balancing problems and small instances of the problem can even be solved close to optimality by hand, thus making the case for the development of commercial assembly line balancing software a di cult one to make. It is therefore not surprising to see that there is a depth of commercially availably assembly balancing software in the market today. (Falkenauer, 2005) outlines some inadequacy of the SALBP as it relates to the real life problem found in industry. The SALBP problem assumes that the assembly line balancing problem is that of a new, yet to be developed facility, yet this is hardly the case as a majority of real world line balancing problems involve existing lines needing to be rebalanced. SALBP also fails to consider operation and zoning constraints that are a common occurrence in many assembly lines. An operation is considered unmovable if it must be assigned to a given workstation. This is due to some kind of heavy equipment that is unmovable or too expensive to move. Another assumption of SALBP which may be problematic is the assumption that workstations can be eliminated. In most real world cases, elimination of workstations can only occur if the candidate workstation is either at the start or the end of the line. Elimination of any other workstation creates the possibility of gaping holes in cases where unmovable workstations exist. Since the elimination of workstations is in most cases unpractical, then the objective of assembly line balancing should be that of workload equalization among the given workstations. This objective makes sense considering that the line cycle time is almost exclusively given by the company?s marketing department. In other words, consideration for decreasing the cycle time should only be entertained in cases where the cycle time exceeds targets set up by marketing. Other practical constraints that 79 are often ignored in SALBP include the uses of multiple operator operations at one station, implying that the lead time for such a workstation becomes the time for the slowest worker. Some operations require more than one operator to be carried out. A typical operation would be the mounting of a bumper which may require two operators, one at each end. Ergonomics constraints should also be made an important part of assembly line balancing. A good discussion of how ergonomic considerations are an important component that should be made an integral part of assembly line balancing constraints is discussed in (Falkenauer, 2005). In this project we discuss the development of a practical hands on lab for teaching line balancing in an undergraduate manufacturing course. 4.4 Developing a Practical Assembly line balancing problem for manufacturing system course (INSY 3800) Manufacturing systems 1 course (INSY 3800) is an industrial engineering course o ered in the spring Semester at Auburn University?s . The average size for this class is 90 stu- dents composed mainly of sophomore, junior and a few senior students. This course has a lab component which requires students to attend a 3 hour lab each week . Students were randomly assigned to a team of no more than 10 members. Each team was then assigned to a manufacturing cell ( See gure 4.2 on page 62 for the purposes of simulating the operations of a manufacturing assembly line). The assembly plant model is a mixed model assembly plant that assembles 2 models of vehicles as shown in gure 4.1. Each team was allocated a manufacturing work cell whose workstations are characterized by unbalanced workloads (See Figure 12, page 59). Each team will be led by a team leader who will be responsible for co- ordinating the activities of the cell. Unlike line balancing problems found in textbooks, this line balancing problem is a more realistic problem similar to real world balancing problems. Contrary to textbook formulated line balancing problems, there are additional tasks that are required to be successful in carrying out the exercise. These additional tasks provide 80 students with good practical experience on how to approach a real world problem and how to solve it. In order for students to have an appreciation of the line balancing problem and how it relates to real world line balancing problems, the following steps were designed into the lab. 1. Participate in live simulated production run (Production run 1) and record line per- formance metrics. 2. Establish standard times using stop watch time study or any the predetermined time motion studies (PTMS). 3. Establish precedence constraints from assembly charts. 4. Establish resource and zoning constraints by taking into consideration design for as- sembly (DFA) and (DFM) principles. 5. Using Line balancing heuristics solve the line balancing problem, and establish theo- retical line balance metrics. 6. Using LegoCad software (MLCad) edit the work instructions to re ect changes to the work instructions as necessitated by new line balance solution. 7. Team leader coordinates training on the rebalanced using 8. Conduct a 1 hour production run (Production run 2) and record data that allow for evaluation of system performance. 9. Participate in continuous improvement meeting and make adjustment to the line 10. Participate in a nal production run (Production 3) and record data that allows for evaluation of system performance. 81 The steps above were carried out over a period of 3 separate lab sessions . To initiate the assembly line balancing problem, an initial unbalanced line was presented to students. At this particular stage, students had no prior experience with any of the operations of the manufacturing cell that they had been assigned to. The objective for each group was to meet a production rate of 51 units/hour, implying a Takt time of 70 seconds. For this goal to be achieved students were required to use a combination of industrial engineering tools/techniques to establish system inadequacies and subsequently make changes that would achieve the desired goal. Assembly line balancing was found to be one among many other applicable IE tools relevant to the situation. Other IE tools considered included, value stream mapping, cell design strategies, ergonomic analysis applied to workstation design, and single minute exchange of dies. The two products used for assembly line balancing are Lego products and can be easily purchased from Lego on-line store. The products are shipped with an assembly instructions booklet that details the steps required for assembling the product. The assembly instructions are also available in PDF format loadable from the Lego on line store. It is important to note the sequence of steps presented in the original Lego assembly manual are in many cases not the most e cient way of assembling the product from a line balancing perspective . This inadequacy in the original Lego product assembly instruction was used to formulate a practical line balancing problem. The sequence of assembly steps were re-evaluated using a line balancing heuristics that were part of in-class learning. Figure 4.16 shows the original subset of steps (16% of total work) required to assemble the Speeder vehicle. To achieve the full assembly, 48 of these steps are required. The assembly steps are spread over three departments (Manufacturing cells) as discussed earlier. In assembly line balancing, the separate and distinct steps are referred as work elements. In most cases, it is the responsibility of IE to determine what should consist of work elements in any assembly operation. However, there are rules of thumb that can be followed in establishing the work elements associated with any assembly work. For this project it was decided that each of the original steps provided in the OEM assembly manual be considered 82 Figure 4.16: OEM assembly instructions a work element since most of the steps met the rule of thumb used for de ning elements. Taking this in consideration, it can be seen that Figure 4.16 shows elements 1 through 8. An important input to the assembly line balancing problem is the standard task time determined for each work element. This is the time it takes to complete each de ned work element. The students were therefore required to undertake a stopwatch time study to establish the standard times for each work element. 4.4.1 establishing standard times (Tek) for elements Denoting (Tek) to be the task time for element k, it is necessary to determine all task times through a stop watch time study. Students were presented with a video of an experienced operator performing assembly work at each of the respective stations (stations 1 through station 15). Verbal instructions as well as written instructions were provided, detailing how 83 stopwatch time study is done. Working in groups of two, students watched the video and used a stopwatch to establish the elemental times for associated tasks. This exercise was essential in demonstrating how time standards are established. A form used for recording the elemental times and computing the associated standard time for each station is provided in the appendix. 84 Figure 4.17: 85 4.4.2 Conducting the line balancing lab The line balancing lab is done in one lab session. This lab is is in the form of a structured lab where students were introduced to the line balancing algorithm associated with heuristics selected to taught in the lab. The actual line balancing was formulated in Excel, and will be discussed in more detail. The three heuristics considered for this exercise included the following, all of which are part of classroom learning material: Ranked positional weight method (RPW) Largest candidate rule (LCR) Kilbridge and Wester method (KWM) The above listed heuristics are part of classroom learning and are all subject to testing during scheduled quizzes and exams. The RPW heuristic was selected to be used in the lab based on its superiority performance compared to the other two listed heuristics. The RPW method combines both attributes of the LCR and KVM since it takes into account the service time at a station as well as position in the precedence diagram of an element. Step 1: Determine RPW values of each element/task in the precedence table The rst step in the RPW method is completing a precedence table. This table details the order in which elements/tasks precede each other in the sequence of operations. The precedence table is established by analyzing the assembly instruction (see gure 4.16) and determining the order in which parts are put together. It is important to note that only immediate predecessors are the only necessary relationship between elements that we are concerned with at this stage of the problem. In this particular case it can be seen that element 1 should precede 4 and element 4 should precede element 5. There is no need to 86 include element 1 as a predecessor of 5, as it is automatically implied. Figure 4.19 shows a partial table showing the precedence of tasks/elements for the speeder vehicle. In the lab, students were presented with an incomplete precedence table and they were expected to complete the table by analyzing the assembly instructions. The highlighted cells in gure 4.19 are left blank so that students can complete as part of lab tasks for that day. The complete table and assembly manual are given in appendix D. Figure 4.18: Partial Precedance table for Speeder vehicle The completion of the precedence table leads to the drawing of a precedence diagram. The precedence diagram is drawn from observing the relationships between elements in the precedence table. The precedence diagram speci es the order or sequence in which activities must be performed. Figure 4.19 shows a completed precedence table for manufacturing cell- 1. In the lab students were presented with an incomplete precedence diagram and their task was to establish the relationship between the remaining elements. Because we have 2 products, two precedence diagrams are required for each manufacturing cell. The completed precedence diagrams required for all cells are given in appendix D. 87 Figure 4.19: Precedence Diagram for Speeder vehicle Step 2: Determine RPW values of each element/task in the precedence table A complete precedence table allows for the calculation of RPW values of all elements/tasks in the precedence table. If we denote RPWk as the RPW value for element k then RPWk is calculated by summing Tek and all other times for elements that follow Tek in the arrow chain of the precedence diagram. With reference to gure 4.19, the RPW values of each element are calculated in the following manner RPW5 = Te5 + Te10 + Te11 + Te13 + Te15 + Te16 + Te17 + Te19 + Te20 + Te21 RPW6 = Te6 + Te7 + Te9 + Te12 + Te17 + Te18 + Te19 + Te20Te21 88 Because the above procedure of determining RPW values can can be tedious and error prone, an excel formulation was done to automate the process. A precedence matrix was formulated to calculate all RPW values for all elements in the precedence diagram (see Figure 4.1 . 89 Figure 4.20: Precedence matrix displaying the relationship between elements In gure 4.1, column ( C4:C24) and row (D3:X3) shows the tasks required to complete the assembly in Cell 1. The relationship of an element in column ( C4:C24) and any of the elements in row (D3:X3) is indicated by placing a 1 or 0 in the cell intersecting the two tasks. A 1 indicates the presence of a dependency relationship between two elements, while a 0 indicates that the two elements in question are independent of each other. Looking at gure 4.1 it can be deduced that tasks/elements f6,7,9,12,15,16,17,19 and 21g have a dependency relationship with element 1, i.e. task 1 must precede the the listed tasks. Column (Y4:Y24) shows the RPW values for each task. This value is obtained by multiplying the appropriate row in (D4:X24) by column (C4:24) plus appropriate task time ( Tek). RPW1 is shown in cell X and the formula used to calculate it , X4=B4+MMULT(D4:X4,Ti) shown in the formula bar. Applying this formula to all cells in column Y will yield the RPW values for all other tasks as indicated in gure 4.1. 90 Table 4.1: Precedence matrix displaying the relationship between elements Step 3: Assign elements to tasks in accordance to RPW criterion Table 4.2 shows results of sorting table 4.1 in descending order of RPW. Using table 4.1, students are then required to follow RPW criterion in assigning tasks to stations. As each element is assigned the appropriate row is crossed out, as shown in 4.2 to indicate it has been eliminated as a candidate element for the next assignment. An assignment table is used to aid the assignment process (see gure refAssignment). The iterative procedure for assigning elements to stations using RPW criterion is as follows: step I Starting with task with the largest RPW value, we assign it to the rst station, if it satis es the precedence constraint and does allow the total sum of Tek to exceed the 91 Table 4.2: RPW values sorted in descending order allowable Takt time. If an element is selected, cross it out from the list of available elements and consider an element with the next largest RPW value. stepII When no more elements can be selected, proceed to the next station. step III repeat step I and step II for the remainder of the stations until all have been assigned 92 Table 4.3 is used as a decision support system that aids in the decision process associated with assigning tasks to stations. The students are however expected to be aware of the rules governing the assignment of tasks. Using Column D, the students assign a task to a station and automatically the table populates the other cells in the same row with data, which the students can then interpret to make an informed decision regarding whether the current element is a feasible assignment. The immediate predecessor column shows what elements should precede the current assignment, while cumulative time shows the sum of Tek that have been assigned to that station. The unassigned column is a very important column as it indicates the maximum task time that can be accommodated at that station in the next assignment. 93 Table 4.3: Decision support table for assigning tasks to stations 94 It is also important to note that the objective of this line balancing problem is equaliza- tion of workload across workstation, thus the use of 65 seconds rather than the Takt time of 70 seconds as the target station cycle time at each station. Table 4.3 shows the complete line balance using the RPW method for speeder vehicle for Cell-1. In a similar manner, line balancing was carried out for all manufacturing cells for both the speeder and SUV vehicles and the results of these endeavors are presented in appendix LBSolution Evaluating the theoretical line balance solution Once a line balance solution has been obtained it is necessary to evaluate how good the solution is. In this project, two metrics were used, (1) line balance e ciency (EB) and (2) Line balance delay (ED) EB = TWCnT S (4.4) Ed = nTS TWcnT S (4.5) Where TWC is the total work content,e TS is the maximum service time among all stations in the cell, i.e. the bottleneck service time and n is the number of workstations in the cell. The closer EB is to 1, the better the line balancing solution is. ED indicates the percentage of time lost due to an unbalanced line. A good line balance solution is one with large value of EB and small value of ED. 95 4.5 Using computerized line balancing software for teaching line balancing Pro-planers ProBalance software is one of a few commercial ALB softwares available for assembly purposes. The software can be used for both single model and mixed model assem- bly lines. Pro-balance allows ease of handling of constraints compared to manual ALB. The software allows for the handling of more constraints than can possibly be done using manual assembly methods described earlier. Constraints such as work zones, resource oriented con- straints, multi-operator constraints, as well as ergonomic constraints can be handled more e ciently using a computerized method such as Probalance. To a ord students the opportu- nity to use a commercial line balancing software, Probalance software was used in the lab for line balancing. While stop watch standard time data was used as input to the manual line balancing method, predetermined standard task times were used with the Probalanced soft- ware. Both models (Speeder and SUV) were balanced using Probalance for all cells (Cell-1, Cell-2, and Cell-3) The rst stage of using Probalance software involves entering data on a task sheet. Each row in the task sheet is an activity ( or task). In each row in the task sheet a task ID and process time is entered. Other columns on this sheet are for additional information that the user could use as desired, table 4.4. The table shows the task sheet for the the assembly of Speeder vehicle in Cell-1 96 Table 4.4: stage 1: Probalance task sheet showing task ID and task times Table 4.4 shows the task entry for the speeder vehicle in Cell 1. As shown, the tasks are entered as a task ID and then the processing time for each station is entered. The work zone column allows us to de ne workzone constraints associated with a task. A good example would be a fuel tank located on the left side of vehicle , thus requiring that such a task be assigned a left zone working constraint. The task sheet also allows for de ning resource constraints. Resource could be recon gurable or monumental, implying that they are xed at a particular station. Once all the necessary entries have been entered, the user precedes with de ning precedence constraints using the precedence sheet shown in gure 4.21. In a similar manner to de ning precedence constraints using the manual method, the user de nes the immediate predecessor for each task. As the precedence for each task is inputed, the software automatically generates a precedence diagram shown in 4.21 . There may be cases in which it is required that particular tasks be installed at the same station. This is accomplished through a feature of the software that allows tasks grouping whose tab is shown in table 4.4 . 97 Figure 4.21: Stage 2: Establishing Precedance in Probalance software Figure4.21 shows precedence formulation in Probalance software. Minimizing the num- ber of stations was the stated objective used in the line balancing algorithm. Figure 4.22 shows the chart for cell 1. The line balance e ciency using Probalance EB was found to be equal to 91% , while the manual LB method discussed in section 4.4 on page 80 had a theoretical Lb e ciency of 89%. It is important to keep in mind that while manual line balancing problem used stop watch standard time data as input, the most predetermined Figure 4.22: Stage 3: Evaluated line balance using Probalance software 98 time standards were used as input to the Probalance software. While stopwatch time study data is stochastic, taking into consideration the presence of both within operator variation and between operator variation , predetermined time standards are deterministic. Because of these di erences, a research question thus arises with regard to the use of standard time in assembly line balancing: Does the method used for generating standard time data in assembly line bal- ancing have an impact on the quality of the line balancing solution. The quality of the line balancing solution in this instance refers to the desired line balance metrics (EB) obtained from actual data. To help answer the question a hypothesis was formulated as follows. Hypothesis Using Predetermined time standards (MOST) as input to the assembly line balancing problem will yield to a superior validated assembly line balancing solution than using operation standard times established from stop watch time study. (a) StopWatch Line balance solution metric (b) Predetermined line balance solution metrics Figure 4.23: Comparing stopwatch standard time data and PMTS standard time data as input to in assembly LB problem. 99 For each station shown in gure 4.32, two theoretical station cycle times EBStopwatch and EBPMTS were established. Taking station 1 in gure 4.33(a) as an example, the station cycle time can be considered to be 58.93 seconds if stopwatch data is considered and 50.98 seconds if PMTS standard data is considered. The question thus is, given a choice between the two standard time data outlined, which times should be selected to be input in an assembly Lb problem. Figure 4.33(a) shows the results of balancing the assembly line using stop watch standard time data as input, while gure 4.33(c) is the line balance solution using PMTS standard time data input. For each generated solution, the associated EBStopwatch and EBPMTS was established. From Figure 4.32 it is evident that balancing a line using stopwatch standard data as input yields favorable EB values for all cells, the associated EB values obtained with PMTS data is not always favorable as seen by not so desirable EB values of 0.78 for cell 3. The same can be said for line balance solutions generated using PMTS standard time data as input. Because of this con ict, thus the research question. 4.5.1 A comparison of stopwatch time study and Predetermined times in man- ual assembly task Assembly task times are an important input in assembly line balancing. These task times can be established by either stop watch time study or Predetermined motion and time study methods (PMTS), such as MOST, Modapts, MTM among a host of other predetermined time systems. It is the believed that the accuracy of assembly task times used in assembly line balancing problems may have a signi cant impact on the quality of the line balancing solution. In this section we compared assembly task times established through stopwatch time study, PMTS method and the actual service times taken during simulated production run. The task times established through either method were then used as input to an assembly line balancing problem (LB) to yield two di erent assembly line balancing solutions. 100 The two solutions were then implemented in live simulated production runs and results were compared to the theoretical line balance solution. Figure 4.24: Scatter plots for establishing correlations of standard times with actual service times for manual balanced assembly line Figure 4.24 shows the relationship between the standard times established using stopwatch time study and MOST Predetermined times standards with actual service times observed during simulated production runs. The biggest correlation was obtained with stopwatch time study (pearson correlation coe cient of 0.718) while PMTS standard times showed a low correlation (pearson correlation coe cient of 0.108). The correlation between PMTS and stopwatch study was low (pearson correlation coe cient of 0.315). Considering this low correlation between stopwatch time study and Predetermined motion time standard, it brings into question how the choice time standard selected to be input in line balancing problem a ects the accuracy of the results. 101 Figure 4.25: Comparison of stopwatch standard times Vs Predetermined times Figure 4.25 shows the comparison of standard times obtained using stopwatch time study and predetermined time study method (MOST) for three di erent assembly work instruc- tions. The correlation between two standard times was tested. Only the unbalanced work instructions showed high correlation while the balanced work instructions showed low corre- lation (< 0:5). 102 Production run-1 The rst production run (Production run-1) used the initial setup which simulated an unbalanced production run. This production run was meant to bring awareness of the problems that may exist as far as running the cell is concerned. Prior to this lab, students had participated in a time study lab during which they were required to calculate theoretical line balance metrics Eb and Ed using the data collected. Using standard time data collected from the time study lab, a theoretical line balance chart for each cell was constructed, gure ??. Figure 4.26: Unbalanced line line balance sheets Table 4.5 shows the theoretical line balance metrics for the three cells at Tiger Motors. It is evident that there is imbalance of work within each cell, as well as between cells. With the current setup, it is apparent that students assigned to cell-1 were expected to face di culties ensuring that the downstream cell- 2 is was not starved of parts. In a similar manner, if no restrictions on the build up of work in process (WIP) is put in 103 Table 4.5: theoretical initial line balance metrics place, then a buildup of WIP is expected between cell-2 and cell-3. This scenario presented a signi cant opportunity for demonstrating various concepts in manufacturing systems and lean manufacturing. This was the initial setup that was presented to students in the rst live simulated run. This initial setup was intentionally presented so that students participating in the lab would experience problems and thus fall short of their target output. In order for students to track their performance, it was essential that production related data be collected during the simulation run. 104 4.5.2 Data collection during the lab Data collection is an important aspect of the lab as it allows students to analyze the performance of the system after each live simulation run. For the INSY 3800 course, there was a need to collect data that would allow students to determine the throughput metrics as well as station related data. The station service time (Tsi) as well as non-value added (NVA) times at each station were important components as these would enable students to identify problem areas and determine line balance metrics. To facilitate ease of data collection, two data collection forms were created , namely: (1) throughput data capture form and (2) Value added/None value added form). Throughput data capture form This form was created to capture data related to the throughput of the system. Each manufacturing cell needs two of these forms. One form is used at the entry point (First station) of the cell to record a time stamp for each unit that begins to be processed in the cell. The other form is used at the Exit point (last station) of each cell to record the time that each completed unit leaves the station. Figure 4.6 shows an example of throughput data capture form. The full form and instructions on how this form is used is given in appendix C Table 4.6: Capturing throughput time data 105 Value added/None value added form This form is required to capture data at each station, Table 4.7. It is used to distinguish all value added time (VA) from none value added times (NVA) that occurs at each station . Each student assigned to a station is required to establish the VA and NVA by using the continuous timing method that requires the clock be started at the beginning of the simulation run and stopped at the end of the simulation. By using the lap feature of the stop watch the entire simulation run time can be demarcated into smaller intervals that represent VA and NVA times, gure 4.27. Figure 4.27: Establishing VA and NVA times at each station Figure 4.27 illustrates how the VA and NVA times at each station are established using a stopwatch. The numbers in gure 4.27 represent events. At the start of the simulation run (Event ST) each operator begins timing by pressing the start button on the stopwatch, and the end of processing of a unit (Event 1) the operator will use the lap button. The lap button allows the interval between the start and end of processing to be stored in memory as Lap 1 (L1), in gure 4.27). When the operator picks up a new part for processing (Event 2), he/she will once again press the lap button to indicate the beginning of processing of a new part. The interval between event 1 and event 2 is stored as Lap 2 in the memory of the stop watch. This process of lapping at each successive start and end point of the processing cycle is continued for the entire duration of the simulation time. As a result, a series of lap times are stored in memory. It is evident from gure 4.27, that odd lap times represent VA and even lap times represent NVA. The lap times can be recalled from memory at the end of the simulation run and recorded into a VA/NVA analysis sheet shown in table 4.7 106 Table 4.7: Value added Non-value time analysis Table 4.7 shows recorded values of VA/NVA added times that were retrieved from the stopwatch at the end of the simulation run. The values were then analyzed using a spread- sheet template provided to compute the average VA and average NVA times at each station (see appendix C, table C.1, and table C.2. The VA times represent the actual service time (processing time) while the NVA represent the idle time that resulted from blocking or starving at a station. A station was considered starved if processing at the station could not be done because work in process (WIP) from an upstream process was not available, and blocked if the station could not continue processing parts due to its output bu er being full. 107 4.5.3 Student?s Assembly line Balancing labs Students? line balancing lab consisted of a series of three labs, namely: Production run-1: Simulate the traditional production systems where WIP between stations is uncapped , and lot sizes are bigger. This simulation was designed so that students could identify inadequacies in the system, and earmark them for improvement in the subsequent production runs. Line balance metrics are calculated and compared to theoretical line balance metrics. Production run-2: Represents the rst attempt of re-balancing the line using line balancing methods discussed earlier. A total of four lab groups (Monday and Tuesday) were involved in the simulated runs. Two groups were assigned to the manual line balancing lab using Excel decision support system discussed earlier, while the other 2 groups (Wednesday and Friday) used the computer based line balancing software Probalance . This production run is also intended to expose any aws present in the line balance solution. Production run-3: Is the nal simulation run using the same line balancing solution presented in production run two, but with re nements facilitated through continuous improvement meeting among team members. Production run-1 results In the rst production run (Production run-1) student groups were given a target of 51units/hour. No additional instructions were given regarding how to run their cell. Without the students? knowledge, each cell was presented with an unbalanced line as shown in gure 4.26, pagerefUnbaLineBalaceCh. During this live simulation run, students were required 108 to record data using two data collection forms provided (Throughput data form and Value added/None value added form) 109 4.5.4 Results of assembly line line balancing labs Figure 4.28: Production run 1 results Figure4.28 shows the result of running the unbalanced line, which is representative of the traditional production system in which WIP is uncapped, and the lot sizes are much bigger. In gure 4.28, the yellow shaded bars represent actual service (Tc) time at a station during the production run, while the other bars represents the station cycle time established through stopwatch time study. It can be seen that the theoretical LB solution was close to the actual LB, indicating the stopwatch time study was a good predictor of the actual line balance solution. The theoretical throughput rate however appears to be slightly higher 110 than the actual throughput rate. From Figure 4.28, it would appear that actual times (Tsi) obtained during the production run in most were cases larger than the stopwatch data. Figure 4.29: Boxplot of stopwatch service time Vs Actual service times Figure 4.30: Paired t test for comparing Stopwatch data to Actual service times A paired t-test for comparing the stopwatch service times to the actual service times indi- cates that there is su cient evidence to suggest that stopwatch time study did underestimate the actual service time at each station for production run-1. 111 4.5.5 Results of running a balanced line ( Production run-2 and Production run-3 After the students had experienced the inadequacies of running a traditional assembly line characterized by unbalanced workstation service times (Tsi) and uncapped bu ers between stations and cells, new work instructions were created using line balancing solution generated from students line balancing lab. Two sets of line balancing solutions were generated and used for two groups of students. The rst line balance solution generated used the manual assembly line balancing method with time study data as input, while the second line balance solution used Pro-balance computerized line balancing method with MOST predetermined time study data as input. Figure 4.31 shows the variation of throughput rate with time for three successive production runs. (a) Monday group production run comparisons (b) Tuesday group production run comparisons Figure 4.31: Manual line balancing method with stop watch data input 112 Figure 4.31(b) shows the results three productions. Production run-1 represents the unbalanced production runs, while productions runs 2 and 3 were done after the implemen- tation a manual line balancing solution. Production runs 2 and 3 show a better throughput rate compared to production run-1. The results indicated that all production runs yielded signi cantly di erent throughput rates as indicated by One way ANOVA analysis. (a) Wednesday group production run comparisons (b) Friday group production run comparisons Figure 4.32: Probalance Computerized line balancing method with PMTS data input Figure 4.33 shows the result of comparing the performance of three production runs for all groups that participated in live simulation runs. The results indicate signi cant improvements in performance from the unbalanced production run-1. With each successive production run, performance of the systems improved, with the nal production run showing the best performance. Production run-2 resulted in an improvement of 17% from production 113 run-1 and Production-3 resulted in a 21% increase in throughput rate from production run-2. Production run-3 shows an overall improvement of 47% from the prior unbalanced state. (a) Box plots (b) One way Anova (c) HSU compare (d) Tukeys Figure 4.33: Production runs throughput rate comparisons Figure 4.33(a) shows the box plots of the means for the three production runs. The one way ANOVA analysis using simulation run number as the factor of interests and throughput rate as the dependent variable showed the mean throughput rate to be signi cantly di erent as indicated by an extremely small p-value. Hsu?s MCB (Multiple Comparisons with the Best) compares each mean with the best (largest) of the other means. Figure ?? clearly indicates production run-3 to be the best. 114 4.5.6 Comparison of Computerized line balancing method with Manual assem- bly method Figure 4.34: Simulation run production run results Figure 4.34 shows the results of running three simulated production runs. The Monday and Tuesday simulation runs used the manual assembly method with stop watch time study input while the Wednesday and Friday groups utilized computerized line balancing method with PMTS standard time data as input. The two methods were compared to determine which method would lead to a better performance. Figure 4.35: Throughput rate vs LB method by Cell Figure 4.36: LB vs Cell 2way Anova Figure ?? shows that for Cell-1 and Cell-2 the manual LB method with stopwatch input data had better performance than the computerized LB method with PMTS data . However, 115 cell 3 indicated a better performance for the computer LB solution compared to the manual LB method. Figure 4.36 shows the two way Anova analysis with Line balancing method as one factor at two levels (Computerized LB and Manual LB) and manufacturing cell as the other factor at three levels (Cell-1, Cell-2, and Cell-3. The throughput rate of each cell was used as the the response variable of interest. The p-value (p=0.615) for the LB method suggested that LB method, time standard combination had no signi cant impact on the output of the system. However, it should be pointed out that the manual LB method showed better output. Figure 4.37: Boxplot of Throughput rate by LB Method, Prod run 116 4.5.7 Discussion The purpose of engaging students in a hands-on learning experience was to provide stu- dents with a realistic learning experience that was as close as possible to real life work related problems. Tiger Motors simulated factory provided such an environment as students were engaged in a number of interrelated tasks, all of which were designed to accomplish a single objective, to build an e cient manufacturing system able to meet the throughput require- ments thus set. In order for the goal to be accomplished students had to put theories taught in class into practice. For instance students were required to learn how to establish time standards through stopwatch time study. The same time standards established by students were then used as input in a line balancing problem. Since students? overall performance was directly linked to the performance of their manufacturing cell with respect to through- put rate and the quality of product manufactured, it became apparent to students that those goals were dependent on how well individual hands-on activities such as, time study, line balancing, standardized work documentation, and group continuous improvement ac- tivities were done. Because of these interrelated tasks, all of which impacted the overall goal, students were motivated to take each individual lab seriously. The line balancing lab was especially challenging for students since it required students? involvement in all aspects related to the line balancing problem. Typical line balancing problems assigned to students are in book problems that provide the precedence constraints to students. However, the lab line balancing problem was unique in the sense that students where provided with a physical model of the product as well as the assembly drawing from which they were required to establish precedence of operations, culminating with the establishment of a precedence dia- gram. This was a vital step which the students found challenging, but a orded students the unique opportunity of developing a particularly important skill-set associated with realistic line balancing problems. The opportunity for students to interact with the physical model 117 was also important in emphasizing design of assembly principles, an important principle that is di cult to demonstrate in a classroom setting. Students were able to solve the line balancing problem using manual heuristic procedures discussed in class. Once a group consensus regarding a line balancing solution was reached, there was need to create new work instructions to re ect changes in the assignment of op- erations to stations. Students experienced the importance of creating proper standardized work documentation. This became apparent when quality problems arose attributed to poorly designed standardized work documentation. It became apparent how the integration of human factors design concepts may be a future hands-on learning activity needed to be in- tegrated into the lab, thus adding to the interdisciplinary learning activities the lab provides. The results of the live simulation runs indicated improvement in system performance with each successive simulation run, indicating that each hands-on activity prior to the simulation contributed to performance improvement. Students were able to see rst hand these improvements which likely increased their con dence and belief in their ability to use the tools taught in class and put into practice in the lab. While the purpose of the hands-on learning activities associated with line balancing lab o ered in the INSY3800 course was mainly to provide realistic problem solving experience to students, it also provided opportunities for answering pertinent research questions relevant in the practice of industrial engineering and manufacturing. Establishing accurate time standards are a vital component in the e cient design of many manufacturing system. The lab provided us with the opportunity to compare two methods for establishing time standards and assess the potential e ect on establishing a good line balancing solution. Stopwatch time study, which is a direct method, was compared with MOST, which is an indirect method. Stopwatch time standards were established by timing a competent worker working at a perceived normal speed. One limitation to this study was that although the time study 118 study standards established using a competent worker, the competency of each worker at the given station was not veri ed. Another limitation to the study of the hands-on labs setup was the inadequacy in exposing the presence of interaction e ects between the line balancing method used (Computerized LB and Manual LB) and the method used to establish time standards (Stopwatch time study and PMTS). Because of variation that exists assembly task times within the worker used in establishing standard times it was hypothesized that the use of PMTS time standard would yield a better line balance solution. The results indicated that no signi cant di erence between the two methods used for establishing time standards, thus implying that despite the method used, the actual line balance metrics are not signi cantly di erent. However, the performance of line balancing using stopwatch time standards appeared to have relatively better performance with respect to the throughput measurement. An additional limitation is that we assume the performance of each group is not hindered by incompetent group members whose task times may signi cantly di er from the expected task times. 119 4.5.8 Conclusion In this past section we investigated the e ect of the method used for established time standards on quality of the line balancing solutions. Because of the variability in the task times between di erent operators, as well the within operator variability, it had been it had been ascertained that using predetermined time standards as input to a line balancing solution would yield a better quality of a line balancing solution. Results indicated that predetermined time study underestimated the the actual time spent on an assembly task. Despite the di erence in task times between the two methods, the quality of the line balancing solution seemed una ected by the method used to establish the times standards. However, the solution derived from stopwatch time study yielded a better solution. In conclusion, we can con rm that predetermined time studies could be used interchangeably as input to line balancing solution. However, it should be indicated that one has to cognizant of the fact that material handling issues, particularly for very small and di cult to handle parts could lead inaccurate standard times when using predetermined time methods. 120 4.6 A hands-on Robotics lab for teaching introductory automation Students taking the INSY3800 course were introduced to a hands-on automation class. Automation is included as part of the classroom lecture material where students are intro- duced to the concepts regarding automation, such as the di erent roles that robots play industry. Students were presented with videos of di erent types of hands-on labs designed to reinforce the classroom material. In the classroom students typically participate as pas- sive learners, however the introduction of the hands on lab component allows students to become active learners. While engaging students in hands-on laboratory work, where they are encouraged to interact physically with the hardware is , it is not always possible due to number of limitations, such as: Lack of adequate hardware needed to perform the lab Lack of adequate sta ng for setting up the lab and maintaining the equipment required Safety associated with students interaction with equipment or machinery Virtual learning environments have been one way used for overcoming constraints listed . In virtual environments, students do not interact with the real equipment to obtain data, learn concepts, or develop skills, but rather make use of computer simulations of the labo- ratory with industrial equipment. In the most common approach, the virtual laboratory is used as an alternative mode and simulates a similar set of activities in the corresponding physical laboratory ( Korecky, 2011) . While virtual laboratories may sometimes be used as a replacement for physical laboratories , it is generally agreed that such laboratories are more e ectively used in conjunction with physical laboratories. Considering the constraints described which were relevant to our situation, a hybrid sys- tem consisting of a computer simulated lab module and physical equipment was developed 121 for the purposes of a ording students the opportunity to actively participate in an introduc- tory automation class. A robotics lab module was introduced as part of INSY3800 labs with the objective of giving students a general overview of application of industrial robotics in a manufacturing industry. In this lab students were introduced to basic robot architecture to included the di erent robot geometries and their suitability to particular applications in industry. A discussion was held in the lab to describe the various types of robots that are found in manufacturing industry. To illustrate an industrial robotic application, a semi- automated assembly operation was selected and developed for demonstration purposes. A semi-automated assembly station was proposed for integration into the Tiger Automotive As- sembly Plant. The semi-automated station allowed the Lab TA?s to give students a practical demonstration of an industrial application of robotic assembly. After preliminary discussion about industrial application of robots, the TA demonstrated to a small group (about 10 students) the various components of a robotic system and how the interaction among the various elements takes place. Because of the large number of students, safety was the primary concern considering that many of the students had no experience with large industrial equip- ment. However students were actively able to participate by developing a robot program required for automated assembly of Lego blocks on a base secured on a xture. Figure 4.38 shows the layout of the semi-automated robotic assembly station. Lego parts are automatically assembled at this station . The station is integrated with into model manufacturing system that includes mostly manual assembly stations. The integration of automated semi-automated stations a ords students to learn about the basics of automa- tion. The integration of the semi-automated station is accomplished in the following manner: Conveyor robot interfacing: 1. At position 1, material handler feeds a pallet containing parts that are required for auto- mated assembly. 122 Figure 4.38: Semi-Automated robotic assembly station 2. When pallet of raw material parts reaches position 2, conveyor stops and a signal is sent to the robot to indicate that the pallet in position. Each pallet may contain as many as 12 individual parts that are to be assembled onto the sub assembly. 3. The sub assembly is a manually fed by an operator at station 1 onto the robotic assembly station using conveyor line # 2. 4. On reaching position 4, pallet carrying sub Assembly stops. 5. The presence of both in-process parts at position 2 and sub assembly at position 4, as indicated by steps 2 and 4, activate the robot to begin the assembly process. 6. Robot picks up pallet (sub assembly) and places it in the xture. 7. Robot picks up individual parts in position 2 and attaches them to the subassembly positioned at 5 as required. 8. When robot picks up the last part from the raw material pallet, the conveyor is set in motion again and the empty pallet exits at the end of conveyor to be re-circulated back to the in feed position. 9. After the last piece is assembled, the robot picks the pallet and positions it at position 123 six where automated vision inspection is carried out. 10. If a part is found to be free of defects, it is placed on conveyor line # 7 which is auto- matically activated by presence of pallet, otherwise it is placed on conveyor line #8, which conveys defective sub-assemblies for rework. Designing and fabrication of tooling and xtures for Robot In any automated assembly tasks there is always a need to develop tooling and xtures needed to assist in the assembly task. This need provides a further learning opportunity for students to actively participate in the design and fabrication of tooling and xtures required. In the case of the automated assembly station, there was a need to develop a custom gripper that could handle two di erent dimensions of Lego blocks. Designing of the gripper and holding xture was done using auto-cad and the parts were fabricated using 3D printing. Figure 4.39: Cell Production run 1 Figure 4.40: Cell 2 Production run 2 124 4.7 A hands on programmable logic controller lab Programmable logic controllers (PLCs) are the cornerstone of automation in many indus- trial factory oors and are likely to remain predominant for some time to come. Most of this is because of the advantages they o er. The use of PLCs o er a number of advantages that include: 1)Cost e ectiveness in the control of complex systems, 2) Flexibility and ability to be reapplied to control other systems quickly and easily, 3)Computation abilities that al- low for sophisticated control, 4)trouble shooting that make programming easier and reduce downtime, and 5) Reliable components, making them likely to operate for years before failure. Basic understanding of PLCs is normally required in many industrial and manufacturing engineering curricula. However, many curricula do not o ers hands-on learning activities. In order to a ord the hands-on learning experience in basic automation and PLC concepts, a PLC trainer was built. The PLC trainer was built using aluminum pro le with a basic Siemens PLC and a human machine interface (HMI) attached to it. This con guration is shown in gure 4.41: Figure 4.41: PLC training station 125 The basic components of PLC include ; Processor, Memory unit, power supply, in- put/output modules and a programmable device. The processor and memory unit reside in the CPU as indicated in gure 4.41, while an external power supply is secured next to it. The inputs to a PLC can be provided by a number of di erent automation sensors such as limit switches, photoelectric electric switch, proximity switch etc. Output signals from the PLC are used to control output devices such as a signal light, and actuators such as motor starters and solenoid valves. The PLC training station shown in gure 4.41 has the ability to use both physical inputs from sensors as well as virtual inputs provided in PLC programming software (Siemens TIA portal software). The HMI screen shown in gure 4.41 had the ability to simulate a variety of sensor inputs and output normally found on the factory oor. PLC are able to control complex systems by making use of programming language called ladder logic. Students understanding of the basic interaction between sensor inputs, ladder logic programming for controlling output signals based on the inputs, was the major learning objective of the hands-on PLC programming lab. By making use of timers, counters, and other mathematical calculations, students were required to establish an industrial control system of their choice. 4.7.1 Students PLCs lab projects The PLC lab consisted of three lab session with each lab session lasting three hours. In the rst lab session an introduction of PLC was given to students and video of typical PLC use in an industrial factory oor setting was shown. A demonstration was then given to show the actual components of a PLCs as described in section 4.7 with lot of emphasis placed on the input states and output state signals. A demonstration on how to create a simple ladder logic using the TIA portal software was given, after which students were assigned a simple assignment which required them to create simple PLC program using the following steps: 126 1. Compiling 2. Debugging 3. Downloading to PLC and HMI The purpose of rst lab was to give familiarity to students on the concepts and operations of PLC. At the end of the rst lab, an assignment was given which would be due on at the beginning of next lab. The second lecture consisted of more complex PLC control utilizing counters, function blocks, as well as using HMI to animate input sensors and various out- puts. An open ended project was then assigned to students at the end of the PLC Lab. The project required that students create a PLC application of an industrial application that demonstrated the use of timers, counter, and any combination of sensor input and outputs. 127 Figure 4.42: PLC ladder logic for Machine batching project Figure 4.42 shows the ladder logic developed by one group for their project. The objective of the project was to use a PLC to control drilling machines, which became activated when ever the number of parts on the conveyor reached a preset value. They used lights on the HMI to represent whether the positions are occupied by parts. 128 4.8 Student perceptions on introductory manufacturing lab in enhancing stu- dent learning and interest The e ectiveness of students? hands-on learning can not be complete without careful assessment of student outcomes. Many di erent assessment methods are available for eval- uating the e ectiveness of hands-on learning labs. These methods include evaluations of student performance on tests, as well as using surveys to gather student feedback regard- ing their understanding of the subject. As part of evaluating the e ectiveness of the newly developed hands on learning activities described in earlier sections of this research, a sur- vey was created to gather students? feedback on how participating in labs impacted their understanding of the course material (see appendix E). Students? opinions regarding their participation in the lab was the primary source of data. A total of 80 students who took an INSY 3800 course in the spring of 2013 participated in the survey. Participants of the survey were composed of 1.25% juniors, 1.25% sophomore, 66% Junior, and 31% Seniors. 47% of the participants in the survey indicated interest in a career in manufacturing. One of the ABET engineering criterion requires that engineering faculty should involve students in explicit instruction in a workshop or cooperative learning format. 46% of the students taking the class had no prior internship experience, thus further emphasizing the need for learning that a ords realistic practical experiences. Prompting the employability skills of students is one of the goals of the developing hands-on learning labs. Since students, along with employers, and faculty are considered stakeholders of engineering education, their opin- ion regarding employability skills are equally important. Students were asked a series of questions regarding their experiences in the lab. In 80% of the of questions, students were asked about their level of agreement regarding a statement, while 10% of the questions were open ended questions where students could express their views in response to a question. A ve point Likert scale, with scale points ranging from "Not at important" to "Extremely 129 important", "strongly agree" to "strongly disagree", were on multi-choice and matrix type questions. 4.8.1 Students responses to survey questions Students were presented with a number of statements in which they were required to respond using a ve point likert scale where 1 is "Not at all important" and 5 is "Extremely important." Statement 1. Given the identi ed competencies relevant to manufacturing, how im- portant do you consider these competencies in preparing you for your future career. The identi ed competencies were listed as 1. Use of computer aided software CAD/CAM, 2. Knowledge of ergonomics and safety, 3. Lean manufacturing knowledge, 4. Operations research and Optimization . 5 MRP/inventory control, 6. Knowledge of manufacturing pro- cesses, 7. Statistical process control, 8. Automation knowledge , 9. Six Sigma knowledge, 10. Business knowledge and skills. Figure 4.43: Desired employability skills of students 130 Figure 4.43 is a summary of student response to statement 1. Knowledge of business was ranked the highest among all other competencies. Lean manufacturing and operations research were also indicated as important competencies for students. Interestingly, students ranked the use of Computer aided design/computer aided manufacturing, as well as knowl- edge of automation, the lowest among the competencies deemed important. Statement 2. Students were asked for their opinion on how they viewed hands-on labs in comparison to lecture only instruction in enhancing their learning ability. Student were asked to state their agreement/disagreement with the following statements: 1. I pay more attention when participating in labs than in lectures. 2. I tend to learn more in labs than lectures. 3. I learn better when I am part of a team. Figure 4.44: Students perception of their learning ability during lectures and in hands-on lab Figure 4.44 shows students? responses to statement 2. Students? responses indicated that more than 60% of students strongly agreed with statements 1 and 2, and around 55% agreed that working as part of a team bene ted their learning ability. However, around 20% of the students were more in favor of individual work than working in teams. 131 Statement 3. Because students were grouped di erently in each lab, both in terms of the number of students in a group, as well as personnel makeup, it was important to determine students opinion regarding what they thought was optimum group size. Depending on the lab activity and availability of equipment, group size ranged from two people in a group to ten people in a group. Based their lab experience, students were asked to select a group size they deemed e ective for team work. Student responses indicated that 70% of students favored group sizes of between two and four people in a group, with 25% of students in favor of no more than two people in a group. Statement4. Since the lab seeks to equip students with conceptual understanding of manufacturing concepts, as well as facilitating life long career skills, it therefore became important that labs should be designed around skills students are likely to use in industry yet are not readily practiced in an academic setting. Students were asked to indicate if they had any prior practical experience with any of elements taught in the lab. The lab elements included: 1. Stopwatch time study/Predetermined Motion and time studies, 2. Robotic programming/Automation with Programmable logic controllers, 3. Assembly line balancing , and 4. Computer Numerical Control (CNC) Figure 4.45: Students prior practical experience with lab elements 132 Figure 4.46 indicates responses to statement 4. The most prior practical experience stu- dents had with any of the lab elements taught were Stopwatch time study and Predetermined motion time studies. About 50% of the respondents indicated having prior practical expe- rience with stop watch time study. However, with other lab elements students appeared to lack any practical experience. For instance, around 70% of students indicated as having between no practical experience to some awareness with regards to practical assembly line balancing. In addition, 43% of the respondents had never participated in an internship, thus lacked the necessary practical experience. Statement5. This question was designed to gage how realistic and practical the lab elements were to students. Since the labs included a combination of physical simulation environment (Lego lab), as well as virtual learning environments in the form of computer simulators (Robotic Programming and CNC programming), students were asked to indicate their agreement regarding how realistic and practical each of the lab elements were. Figure 4.46: Students prior practical experience with lab elements Statement6. This question was designed to gage students? perceptions on the ef- fectiveness of the use of virtual learning environments in enhancing students? conceptual 133 understanding and skills development. Students participated in labs where they had to de- velop a CNC code in a virtual learning environment. CNC programming is used in many manufacturing processes, particularly in metal cutting operations. Students were also introduced to basic automation principles through their participation in lab where they were required to use a combination of physical automation hardware and a virtual PLC simulator. The setup allowed students to experiment with automation concepts used for controlling industrial machines and equipment normally found in many industrial settings. In addition, students were also engaged in a hands-on robotic programming in a virtual learning environment. Figure shows students? responses to how they viewed the use of virtual learning environ- ments. Overall students showed a positive attitude on the use of virtual learning environ- ments, with about 70% of students indicating that using the virtual learning environments enhanced their understanding of concepts. Figure 4.47: Students perception on virtual learning environments 134 4.8.2 Conclusion Students? perceptions on the value of hands-on learning supports the notion, that hands-on learning introduced in manufacturing curriculum not only motivates to students, but could be useful in developing particular skill sets valuable to employers. Hands-on labs develop students interdisciplinary skills as students of various backgrounds interact together with the sole purpose of solving the problem at hand. A combination of both physical simulations and virtual learning environments can be used to successfully reinforce classroom lecture. From the survey it was apparent that students appear more focused towards subject matter during hands-on learning activities than during lecture. 135 Chapter 5 A hands-on approach to enhancing student learning in Lean Production course 5.1 abstract A major challenge in lean systems as a continuous improvement tool is identifying the best strategy for implementation. Lean implementation is a long term process that carries high risks in fast changing industries. The success of many Japanese rms can be attributed to their strong lean culture, which is team driven and emphasizes both hard and soft skills. Despite many American companies attempts at lean transformation, outcomes of these en- deavors have not always been positive. It is our belief that in order for American companies to regain their competitive edge, there is a need to provide hands-on training in lean manu- facturing at the grassroots level (Academic level). In this project we discuss how hands-on lean training has been incorporated as part of the learning initiatives associated with courses in Lean manufacturing at Auburn University. Despite the emphasis on the importance of hands-on experience in manufacturing education, the issue regarding the e ectiveness of hands-on education has remained contentious among many. Four hands-on learning labora- tories have been designed to reinforce student learning of lean manufacturing concepts. The results of these live lean simulations led to the development of a computer simulation model, which will be used as an additional learning tool in both lean manufacturing and manufac- turing systems education. The computer simulation will provide students with an alternative method for analyzing many di erent experimental scenarios needed to understand how the system works. An assessment of the e ectiveness of the labs in enhancing students learning is also described. 136 5.2 Introduction Simply stated, Lean manufacturing is a system for total elimination of waste from an operation or process. It is a management philosophy evolved and adapted from the Toyota production system. The Toyota lean manufacturing philosophy of success can be attributed to a multifaceted number of factors that include strong cultural factors that emphasize team- work and commitment to quality throughout the ranks. However, because these underlying Japanese cultural traits di er from western values that emphasize individual achievement, independence, and short term goals, there is a need to emphasize to the American compa- nies the importance of integrating the hard skills with the soft skills. The hard skills are the generally accepted manufacturing/Industrial engineering tools (e.g. line balancing, value stream mapping, etc), while the softer skills are those that entail fostering behaviors, roles that are essential for a culture of continuous improvement (Badurdeen et al., 2009). lean manufacturing training requires learning of both soft and hard skills necessary for successful problem solving. By embracing the Toyota approach to learning, that allows people to learn from their mistakes, the manufacturing system lab hopes to foster the same cultural values that have allowed Toyota Motor Corporation to be successful. Toyota is viewed as a learning environment whose greatest resources are its people. A manager?s job at Toyota is viewed as that of a facilitator whose responsibility is to coach workers to know how to solve problems. It is on this basis that the Lean manufacturing lab is operated. The TA?s assigned for this class acted as managers while students played the role of empowered workers whose main responsibilities included nding solutions to problems in the system. 137 5.3 Background information in lean manufacturing training The goal of many lean manufacturing is to reduce cycle time and increase the ratio of value added to the total cycle time (Shannon, 1997). Lean manufacturing training has over the years become popular in industry. However, over the years a number of educational institutions have begun attempts to integrate lean manufacturing course modules on lean management philosophy with varied degrees of success. Most lean manufacturing teaching in educational institutions has been focused on classroom teaching with limited guidelines available on how to conduct hands-on lean manufacturing training to reinforce classroom learning. Based on a review on lean manufacturing training by Budaurdeen et al.,2009, there is a dearth of published information on simulation and games from some of the better universities and colleges that o er lean manufacturing. Furthermore, despite a huge Internet presence of companies and institutions that purport to o er lean manufacturing training, there is little information on the design and implementation of the simulation games. In addition to the use of live simulations in the training of lean manufacturing, there has been an increase in the use of computer assisted simulation (Feinsten, Mann, & Corsun, 2002). However, despite some noted bene ts of computer assisted simulation games (Wang, 2005), the e ectiveness of computer simulations as a substituted for live simulations still remains arguable. The limitations of computer simulations as alternative to live simulations in lean manufacturing training has been attributed to its inability to facilitate realistic interactivity and collaboration between team members (Rolfe & Hampson, 2003, Wang, 2005) Most of the simulations reviewed by Budaurdeen et al.,2009, focused on the use of lean tools in transforming a traditional push system into a pull production. The majority of these simulations involved short iterations usually done within constraints of time allowed for a classroom lecture, with few of the simulations being more intense and lasting much longer. The short iterations usually carried during a single lecture have been noted as limitation since students are not allowed enough time to re ect on the learning points and applications of lean 138 principles. It has been noted that the failure of some simulation training to achieve desired learning outcomes may be attributed to the confusion in the roles of teacher/instructor and student interactions. While it is expected that an instructor/teacher should play the role of teacher/coach or facilitator, there have been many observed cases in which the instructor ended up playing the role of team leader. The role of the facilitator is not teach solutions to the problem but rather to guide participants on how to achieve the desired learning outcomes using the appropriate lean manufacturing tools. A lack of realism is another noted problem found with many of the live simulation games available. It has been estimated that less than 5% of simulation games o ered presented realistic environments while less than half were tactile. Most live simulation games were found to lack enough complexity and sophistication normally found in real environments. It has been noted that despite the failures experienced by a large number of companies in their lean manufacturing attempts, many of the simulation games o ered as training for students fail to acknowledge that failure is part of lean transformation, as most of the simulations are designed in such a way as to represent success. A good review of simulation games used for training in both industry and academy can be found in (Bardudeen et al.,2009 and Verma, 2003). In surveys conducted to determine the use of simulation in lean manufacturing, it was found that a majority of simulation games used in industry were developed by National Institute of Standards (NIST), while many others are adaptations of the NIST simulation games. Most of the simulation training programs o ered cover a variety of simulation principles like 5S, setup reduction, value stream mapping, pull vs push production, and continuous improvement among many others. Most of simulation games are conducted over a number of iterations that can range from a little as an hour to a full day. 139 5.4 Methodolgy This study required the participation of students enrolled in the lean production course (INSY 6800) at Auburn University. This is a dual level course composed of both undergrad- uate and graduate students. Typical enrollment for the course is around sixty students, with a third of the students being graduate students. The INSY 6800 course was designed to be a lecture only course. However, the instructor for the course required that students acquire some hands-on lean manufacturing experience by engaging in laboratory activities. Because (INSY 6800) did not have a lab component,and since the lab is limited to a maximum of 30 students, it was di cult to accommodate all sixty students in one lab session. This problem was overcome by equally dividing the class into two larger groups, A and B. All students in a particular group attended lab at the same time. Each Lab group was further subdivided into three smaller groups (Grp 1, Grp2,and Grp 3) of no more than 10 members. It is important to note that students were randomly assigned to the groups and each group consisted of at least 3 graduates students. Each group was requested to nominate a team leader, whose responsibilities included coordinating all lab activities. On designated days either group A or B would be required to attend a lab session to participate in a designated activity, while the other group attended the lecture. Whatever group was designated for a lab activity could still participate in the lecture by watching a recording of the lecture. The event calender for the activities associated with INSY 6800 are shown in 5.1: 140 Figure 5.1: INSY 6800 calender of events and experimental design For students to grasp the intricacies of a manufacturing system they need to understand the relationship between the various processes that make up the system. To start with, students were exposed to a traditional manufacturing system that is based on a push MRP schedule. This system was designed with the following characteristics: Long and ine cient change over process Large batch sizes between changeovers from one model to the other Large amount of bu er stock between Cell-1 and Cell-1 No Limitations on the amount of WIP between stations No standardization on container quantities and transfer batches Individual misaligned MRP schedules at each Manufacturing Cell Unbalanced workload among stations 141 Individual groups were assigned to a particular manufacturing cell. The graduate students in the group were assigned team leader roles, with one graduate students selected by team members to be the overall team leader. With the teaching assistant playing the facilitator role, each group was required to schedule time outside of class in which team members were oriented to the assembly tasks required at each station. Orientation of team members included the following activities: Job training at each workstation Conducting a stopwatch time study to determine the standard operation time for each de ned operation. Only the undergraduate members of the group were required to do this. Graduate students used predetermined time method (MOST) to establish the standard times for each operation. The rst production run (Production run-1) was intended to expose problems in the system, with subsequent production runs being improvements. As shown in gure 5.1, individual groups were assigned to work on continuous improvement projects in between production runs. Each of these projects were intended to improve on the prior performance of the system. During each production run the following production related data was collected analyzing the performance of the system: Throughput rate for each manufacturing cell Number of Defects produced per station Work in process (WIP) at each station at the end of the production run required for value stream mapping Value added/None value added time at each workstation required for line balancing purposes 142 Stock in hand (Raw material stock) at each workstation required for value stream mapping purposes. 5.5 Individual group lean manufacturing project Three group projects were identi ed as shown in 5.1. As described earlier production run-1 was intentionally made to be ine cient and it showed in the performance as recorded. Students were not given any instruction on how to run their cells. Students in Cell-1 were subjected to an ine cient changeover process that lasted an average of 5 minutes to complete. Each production run was 45 minutes and the Takt was set at 70 seconds, implying that the expected throughput rate for each cell was 51 cars/ hour. Figure 5.2: Production run -1 results Figure 5.2 shows the results of Cell 1 during production run-1. It is clearly evident that students in this group ran a push based system as indicated by variation in the throughput 143 rate across stations. Despite the problems at stations 4 and 5, students at other stations kept on assembling products adding to the large amount of WIP at stations 4 and 5. This led to the rst group project, of identifying problems in the system. Value stream mapping was selected as the tool of choice. 144 5.5.1 Value stream mapping Tiger motors (VSM) One of the important analytic tools used in lean manufacturing is value stream mapping. Value stream is all actions, both value added and non-value added, needed to bring a product through the main ows ( Rother & Shook, 1999), and these could be: The Production ow from raw material into the arms of the customer The design ow from concept to launch A value stream mapping is a good tool for getting an entire perspective of the operations of an organization, rather than focusing on individual processes that most IE tools do. The Tiger Motors factory provided a foundation for students to learn and actively participate in hands-on, realistic value stream mapping exercise. While students are taught the basic of value stream mapping in a classroom lecture, students are a orded the opportunity to walk the Tiger Motors shop oor and collect data that will enable them to draw the cur- rent state value stream for Tiger Motors. By participating in this exercise students were expected to identify all the sources of waste inherent in the value stream. It was expected that by participating in the hands-on exercise students would get an understanding of what value stream mapping is, as well as acquire the skills needed to undertake a value stream mapping exercise. A current value stream map was important for identifying the ows of information and material needed to develop a future VSM. The current state map for Tiger Motors involved collecting production-1 related data and presenting it using a value stream map. Two groups were assigned to this project, (Groups A3 and B3). The current value stream created by the two groups was used as the basis for the development of a future state map. 145 The current value stream map exercise problem was stated as follows: Tiger Motors is an automotive assembly plant that is involved in the assembly of two models of vehicles, namely an SUV model and Speeder models. Currently the assembly plant is organized into three departments (under body assembly, cab assembly, nal assembly and trim). Switching between an SUV and Speeder requires a 6 minute change over. Management has determined that the forecasted demand for Speeder is 57 900 vehicles per year while the Demand for the SUV is 38 600 vehicles/. The line is expected to operate 50 weeks/year, 5 shifts /per week, 7.5hrs/shift. In addition, the following information from production control was made available: Tiger Motors Forecasts 90/60/ 30 day forecasts and enters them into an MRP Issues out a 4 week forecasts to its suppliers via MRP Secures Raw Material stock from its suppliers by weekly faxed order release to its Customers. Receives Raw materials from suppliers on Mondays and Wednesdays of each week. Generates weekly department requirements based on Customer orders, WIP inventory levels, Finished Goods inventory levels, anticipated scrap and downtime. Issues daily build schedules to Cell-1, Cell-2, Cell-3 Issues Daily shipping schedule to Shipping Department. The rst station of Cell receives Build schedule. When work at station is completed the Subassembly is transferred to the next station. Transfer of work between Cells is done in batches of ve. A material handler uses a hand driven cart to transfer completed work pieces between cells as well as replenish raw stock. 146 Process Information for all cells was collected and recorded by individual group members and recorded in forms provided (see Appendix DataCollection). Station cycle times, scrap rates associated with each station and inventory levels (stock at hand, and WIP) were made available. With this information, the two groups assigned to the VSM project were tasked with drawing a current VSM for Tiger Motors. Figure 5.3 shows the current VSM for tiger motors submitted by Group A3. It can be deduced from the VSM that quality problems exist in every cell as indicated by the scrap rates at particular stations. The VSM also indicates large WIP bu er sizes between cells. The large bu ers were arbitrarily selected for their size to protect upstream processes from being starved, due in part to large setup changes particularly at station 5. Of particular note are the information ows between production control and each manufacturing cell in the form of production schedules. Each manufacturing cell receives a production schedule which is used to sequence vehicles built in that cell. This creates potential for problems of synchronization if schedules are not matched correctly. This synchronization problem is a common problem in MRP based systems, and it was the intent to demonstrate this in the lab by providing slightly mismatched schedules to all three cells. The process ratio for this value stream map is 22%, implying that 78% of the time, a unit spends waiting in queues. 147 Tiger ? Motors ? Value ? St re am ? Mappi ng ? Exer cis e : ? ? WIP ??? ?? ?? 3 ? ? ? ? ? ? ? ? ??? ?? ?? ??? 0 ?? ?? ??? ?? ?? ? ??? ? 4 ? ??? ?? ?? ?? ??? ?? ?? ? ? 0 ?? ?? ??? ?? ?? ?? ??? ?? 17 ??? ?? ??? ?? ?? ?? ??? ?? ? 2 ??? ?? ?? ??? ?? ?? ?? 0 ? ? ? ? ? ? ??? ?? ?? ??? 3 ?? ?? ??? ?? ?? ? ??? ?? ?? 0 ? ? ? ? ? ? ? ??? ?? ?? ? ??? ?? 12 ??? ?? ?? ?? ??? ?? ? ? ? 1 ? ? ? ??? ?? ?? ?? ??? 3 ?? ? ??? ?? ?? ??? ?? ? 0 ? ??? ?? ?? ? ??? ?? ?? 2 ? ? ? ??? ?? ?? ??? ?? Su ppli e rs ?? ?? ?? ?? ??? ?? ?? ?? ?? ??? ?? ?? ??? ?? ?? ?? ??? ?? ?? ? ??? ?? ?? ??? ?? ?? ?? ??? ? ? ? ? ? ??? ?? ?? ??? ?? ?? ?? ??? ?? ?? ? ??? ?? ?? ??? ?? ?? ?? ??? ?? ?? ? ??? ?? ?? ??? ? ? ? ? ? ? ??? ?? ?? ? ??? ?? ?? ??? ?? ?? ?? ??? ?? ?? ? ??? ?? ?? ??? ?? ?? ?? ??? ?? ?? ? ? ? ? ? ? ? ? ??? ?? ?? ?? ??? ?? ?? ? ??? ?? ?? ??? ?? ?? ?? ??? ?? ?? ? ? ? Customer ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ???????? 20 9. 8 2 ?????? ??????? ??????? 0 ?????? ??????? ?????? 27 9. 76 ? ??????? ??????? ??? 0 ????? ?????????? ??????? ??? 118 8 . 98 ?????? ??????? ?????? 13 9. 8 8 ?????????? ?????? 0 ???? ??????? ???? ?????? 209 .8 2 ? ??????? ??????? ??? 0 ??? ??????? ??????? ??????? 839 .2 8 ? ??????? ??????? ???? 69. 94 ?? ??????? ??????? ? 2 09. 8 2 ?????????? ????? 0 ????? ??????? ???? ????? 1 39. 88 ? 70. 0 3 ? 33. 4 2 ? 64. 9 7 ???? ??????? ????? 62. 99 ?? ??????? ??????? ?? 66 .15 ? ?????????? ??????? ??????? ?? 59 .92 ?????? ??????? ?? 62 .19 ?????? ?????????? ? 64 .2 9 ??????? ????? ???? 82 .3 1 ??????? ??????? ???? 44. 0 5 ???? ??????? ??????? ????? 88. 8 4 ???? ??????? ??????? 78 .0 4 ?? ??????? ????? 92. 4 8 ??? ?? ?????????? ? 54 .8 6 ??????? ????? ??? 78 .3 6 ? ST ? 1 ? ???? 1 ? ST2 ? ??? 1 ? ST ? 3 ? ??? 1 ? ST ? 4 ? ???? 1 ? ST ? 5 ? ??? 1 ? ST6 ?? 1 ST7 ?? 1 ST ? 8 ?? 1 ST9 ? 1 ST ? 10 ?? 1 ? 11 ?? 1 ST ? 12 ?? 1 ST ? 13 ?? 1 ST ? 14 ?? 1 C/T: ? 2 ? 70. 03 ? C/0 ? 2 ?? 0 ? AVAL:100% ? Scrap:0 ? Bat c h: 0 ? C/T: ? 2 ? 33. 42 ? C/0 ? 2 ? 0 ? AVAL:100% ? Scra p: 8. 3% ? Bat c h: 0 ? C/T: ? 2 ? 64. 97 ? C/0 ? 2: ? 0 ? AVAL:100% ? Scrap:0 ? Bat c h: 0 ? C/T: ? 2 ? 62. 99 ? C/0 ? 2 ? 0 ? AVAL:100% ? Scrap:0 ? Bat c h: 0 ? C/T: ? 2 ? 66. 15 ? C/0 ? 2 ? 36 0 ? AVAL:100% ? scrap:4.2% ? Bat c h: 0 ? C/T: ? 2 ? 59. 92 C/0 ? 2 ? 0 AVAL:100%Scrap: ? 0 Bat c h: 0 C/T: ? 2 ? 62. 19 C/0 ? 2 ? 0 AVAL:100%Scra p: 4. 2% Bat c h: 0 C/T: ? 2 ? 64. 29 C/0 ? 2 ? 0 AVAL:100%Scrap:5 0 % Bat c h: 0 C/T: ? 2 ? 82. 31 C/0 ? 2 ? 0 AVAL:100%Scra p: 4. 2% Bat c h: 0 C/T: ? 2 ? 88. 84 C/0 ? 2 ? 0 AVAL:100%Scrap:0Bat c h: 0 C/T: ? 2 ? 44. 05 C/0 ? 2 ? 0 AVAL:100%Scrap:0Bat c h: 0 C/T: ? 2 ? 78. 04 C/0 ? 2 ? 0 AVAL:100%Scrap:0Bat c h: 0 C/T: ? 2 ? 92. 48 C/0 ? 2 ? 0 AVAL:100%Scrap:0Bat c h: 0 C/T: ? 2 ? 54. 86 C/0 ? 2 ? 0 AVAL:100%Scrap:0Bat c h: 0 C/T: ? 2 ? 78. 36 ? C/0 ? 2 ? 0 ? AVAL:100% ? Scrap:4 3 % ? Bat c h: 0 ? I I I I I I I I I I I I I I Tiger ? Motor s ? Value ? stream ? Mapping ? Exercise ? ST ? 15 ? ?? 1 INV : 3 9 ? INV : 1 1 8 ?? INV : 9 4 ? INV : 7 7 ? INV : 1 5 2 ? INV : 7 8 INV : 5 9 INV : 1 6 3 INV : 8 2 INV : 2 2 3 INV : 3 4 INV : 3 9 INV : 1 2 5 INV : 2 2 INV : 3 4 ? Production ? Le ad : ? 3287.18 ( Se c ) =54 . 78 ( min ) ?? ?? ?? ?? Processin g ? Lead ? Tim e : ? 1002.9sec = 16.715 ? ( Min ) ?? Production ? Co n t r o l ? MRP 90 /6 0/ 30 ? day ???? ? Fore cast 90 /6 0/ 30 ? day ???? Fore cast ? Wee k l y ? Or d e r s 4 ? we ek ? Fo recast Weekly ? Sch e dule ? Wee k l y ? Fax ? Shippi ng ? Daily ? schedule ? Daily ? schedule ? Daily ? schedule ? Daily ? shippin g ? sche dule ? Daily ? deman d ? 386 ? Cars ? Every ? MON ? an d ? WE D ? receive ? raw ? materials ? Figure 5.3: Tiger Motors curren t v alue stream map 148 Figure 5.4: Tiger Motors Future Value stream map Figure 5.4 shows a proposed future value stream map for Tiger Motors. It can be seen from this gure that MRP system was to be replaced with a pull based system where production is scheduled at one point in the value stream (station 15) . This point is referred to as the pacemaker process, because production is controlled at this point and it sets the pace for all upstream processes. As a way of promoting continuous ow, the bu er size within each cell it was proposed to cap the bu er size between stations at 1. However because of the di culty of maintaining continuous ow between manufacturing cells, a supermarket bu er was proposed . The key decision that had to be made was regarding the size of the supermarket. The size of the supermarket is a function of the bottleneck station and variability in the process time. The future value stream map shown in gure 5.4 shows a proposed supermarket bu er size of 5 between cells. Since it was evident that there was imbalance between stations, it was necessary to use line balancing to ensure that they were 149 below takt time. In order to reduce the size of the bu er between cells, it was necessary to reduce the amount of change over time at station 5 (bottleneck station). It is evident that the reduction in bu er size was expected to have the greatest impact in reducing the manufacturing lead time. By signi cantly reducing the bu er size, the manufacturing lead time was expected to cut by almost 2/3 of the original value. Figure 5.4 proposed the use of a 2 card Kanban system for controlling ow in the system. Upstream and downstream process are linked by using withdrawal and production Kanbans. A withdrawal Kanban is used by a downstream process to request material from a downstream process, while authorization of production at an upstream process is done using a production Kanban. 5.5.2 Lab implementation of pull based 2 Card Kanban Production Control with production leveling Figure 5.4on page 149 shows an example of a solution presented by one of the student that particpated in an INSY6800/5800 class. To provide student with more concrete experience, the solution to the problem had to be implemented on the Tiger Motors shop oor. A 2 card Kanban pull system was developed and substituted the push production control system that had previously been in place. The main objectives of implementing this live simulation of pull production system complete with production leveling were: 1. How to implement load leveling using a Heijunka board 2. How a two bin kanban systems for material replenishment works 3. How Kanban system can be used for controlling replenishment of raw material at the workstation. A Two bin system will be used for ensuring that while one bin is sent back for replenishment, the workstation is not starved of parts. The decision is how much raw material should each bin hold. 150 4. How a ow of Work in process (WIP) in the manufacturing system is regulated by Withdrawal and Production Kanbans. Figure 5.5: Pull Production Kanban loops Figure 5.5 shows the implemented Kanban loops on Tiger Motor?s Production oor. It can be noticed that production and withdrawal Kanban post have been located at the beginning of each cell. An inbound bu er storage area was located at the rst station of each cell, while an outbound bu er storage is located close to the last station of each cell. In this particular case the outbound bu er storage is referred to as a supermarket. The number of units of WIP at each inbound storage bu er and supermarket is determined by the number of Kanban cards. The number of Kanban cards in the system determines the total amount of WIP in the system. The number of Kanban cards required in each Kanban loop depicted in Figure 5.5 is a function of replenishment lead time container quantity, as well as the demand rate for that particular product or raw material. This number can be mathematically determined using equation 4.1 given on page 4.1. However, students that took part in this course were not required to establish the number of Kanban cards required in the system, rather the goal was for them to understand how the system worked in controlling WIP in the system. It is also important to make a distinction between the fact that the two card system described is meant for work in process inventory rather than raw stock inventory which uses a di erent strategy than described here. Each WIP container in the inbound bu er has withdrawal 151 Kanban attached to it while each full container of WIP at the supermarket has a production Kanban attached to it. Figure 5.6: implementation of a load lev- eling using a heinjunka board Figure 5.7: Supermarket bu er at the end of Tiger Motors manufacturing Cell Figure 5.6 shows a Heijunka board used for leveling the load at Tiger motors. The board is divided into equal increments of time (5 minutes) called a pitch. The pitch determines how many of each vehicle should be produced within that period. This time includes the time required to produce parts, changeover time, and expected downtimes. At every interval a material handler retrieves these cards and delivers to the pacemaker process (WK-11). 5.5.3 Setup reduction using single minute exchange of dies Because of the changeover problem, large batch sizes had to be maintained so that "Cell 1" could keep up with demand requirements of "Cell 2". The e ect of setups can be better understood by the equation: Capacity given batch size = Batch sizeSetup time + Batch size time per unit (5.1) In the presence of large setup times, equation 5.1 makes it clear that large batch sizes are necessary to maintain high capacity . However, high batch sizes are undesirable where maintaining ow and meeting customer delivery dates are key objectives. Because of the relatively large setup change relative to the length of simulation run, a change over problem existed. A changeover problem was assigned to two groups (A1 and B1) . The objective for 152 each group was to use SMED analysis tools to establish the inadequacies that existed in the change over setup. After the SMED analysis, with the Lab TA playing the facilitator role, each group was required to develop a solution . Each group was awarded $50 for the purchase of any material needed for implementing the solution. Both teams worked independently to develop the solution to the problem. SMED analysis of the original setup In the original setup, the tools and xtures required to do the setup are located on a shadow board about 30 feet away. Two wrenches and the xture to be installed are collected from the shadow board. The steps for the change over process are as outlined in table below. Table 5.1: Pre SMED Analyis The changeover process was fraught with di culties that included the bolts being too long, which thus required too many turns of the nuts to insecure the xture. The task required two people, and the once the new xture was put in place it was di cult to align the xture in the correct position. Table 5.1 shows the breakdown of tasks involved in the the changeover. A key in any change over analysis is the ability to separate external and internal task. It is desirable to increase the portion of time consumed by external task. This is normally accomplished by attempting to change some of the internal changeover tasks into 153 external tasks. It is also clear that the potential for improving the changeover process lay in the removal and installation of the xture to the table since this consumed almost half of the entire changeover time. 154 Implemented solution to the SMED problem B DETAIL B SCALE 1 : 1 This hole is a poka-yoke Figure 5.8: SMEDSolution Proposed SMED Solutiong In one of the solutions the larger xture that is used for the speeder is secured to the work surface as a base, as indicated in Figure 5.8. The second xture was set on top the rst xture when needed. This was done by drilling several holes in the second xture to t properly over the rst. This design ensured that alignment using a ruler was eliminated. The resultant change over process was reduced to few seconds since all that was required was removal of the top xture and putting back in place. The poke yoke hole at bottom ensured that the xture was always oriented correctly. 155 5.6 Development of a Simulation tool for assisting with Lean production train- ing In this section we describe the development of a computer simulation model to be used in conjunction with live simulation to assist students with learning both manufacturing systems concepts (INSY 3800) and lean manufacturing concepts (INSY 6800/5800). The main motivation behind the development of the simulation tool is to provide students with alternative analysis and decision making tool to assist them with understanding how the real system works. Because of the large class sizes normally enrolled in the course, it is often not possible to involve all students in all the di erent hands-on on activities required to reinforce students? understanding of lean manufacturing concepts. For instance, in the lean manufacturing class at Auburn University only a subset of students were assigned to a particular project such as SMED, Cell design, or value stream mapping. As a result, student bemoaned their lack of participation in other activities and often pointed to it as the reason for not grasping the system view of manufacturing system. Computer simulation thus o ers an additional learning tool for enhancing student conceptual understanding of the taught concept. With the simulation model developed students will make the following analysis of the system: Establish the impact of change over time on system performance Investigate the e ect of bu ers between stations as well as between cells on system performance. The model allows students to answer what if questions. for instance how would increasing the the bu er quantity from 1 to 2 on the system performance. Depending on the change over time at station what bu er size would be adequate for ensuring system throughput is met. Determine the impact of changing a system from a push based MRP system to a push system. 156 Analyze a particular line balance solution as well as establish a suitable assignment of operators to stations, taking into consideration variability task times between operators Based on data on the model, create a value stream map and evaluate future value stream map During the physical Lego lab simulation several performance measures were taken in to account to determine how well a group of students performed in the Cell that they had been assigned to. The Primary performance measures was the throughput of at each cell. The throughput of each Cell was determined by the number of vehicles produced during the production interval. The second performance measure used was the Throughput time for each Cell. The Throughput times was determined by taking note of the times the vehicle entered the system and the time it exited the system. The The di erence between the two times is the system throughput time. In addition, Station utilization was determined by measuring the value added time and none value added time at each station. Using the value added data gathered during the simulation run, Line balance metrics, "Line balance e ciency" , "Balance delay" were calculated. The Simulation model can provide students with what if scenarios. A simulation model for Tiger Motors lab was developed to provide what if scenarios analysis to help students visualize the impact of certain design decisions related to the manufacturing Cell they had been assigned to during labs. As and example, the computer models should provide insight on the e ect of varying station bu er sizes, Supermarket quantities between cells, production batch sizes, and changeover times on system performance. The availability of this model allows students to make educated decisions about how to control production related variables withing their control in-order to maximize system performance. The simulation models was developed are a true re ection of the Tiger Motors shop oor operations. The data gathered during the hands-on physical simulation runs was used for model veri cation and validation. During running the live simulation runs it was apparent that there was variation in the task 157 times between di erent operator at each Cell. It thus became apparent that for a group to maximize system performance, it was important that they assigned their personnel to work stations to match the abilities of the individual to the task complexity of that particular workstation. However the groups found it di cult predicting system performance, thus the the development of this simulation model. The labs associated with the Lean Production course (INSY 5800/5800) involved running three iterations of the Tiger Motors Lab, starting of with the traditional manufacturing environment which is based on a push production (MRP) control strategy . Progressive iteration involved introduction of lean concepts such as batch size reduction, single minute exchange of dies, and use of predetermined supermarkets quantities between cells. The development of the lab allows student to experiment with these control variables and noting impact on system performance before actual implementing the changes in hands-on labs simulations. 5.6.1 Developing a computer simulations model to mimic the production oper- ation at Tiger Motors Two computer based simulation models were developed using Simio simulation software. The rst model represented the traditional manufacturing environment based on push MRP production control strategy. The second Simio model created represents the improved lean manufacturing environment in which a number of lean methodologies have been imple- mented. The traditional manufacturing environments are characterized by the following: Large batch sizes. Long change over times. Unlimited WIP between stations. Absence of standardized work leading to large process variation. 158 Absence of cross training among the workers. Lean manufacturing principles are used to reduce the amount of waste in a manufactur- ing system. Waste is anything that adds cost but no value to the product. Some of key lean manufacturing methodologies essential to lean transformation include: 1) value stream mapping, 2) Set-up reduction through, 3) load leveling Heijunka, 4) Kanban pull strate- gies among many others. While researchers in lean manufacturing training have pointed the added bene ts of physical simulations (Wang, 2005, Bardurdeen et al.,2001, Cudney et.,2010), lean computer assisted lean manufacturing has the ability another dimension to lean training. In this section we describe the develop of a computer based lean training tool, that we be used in conjunction with the physical lean simulation lab described earlier. 5.6.2 Simulating a traditional push based manufacturing system using Tiger Motors oor layout Lean training started with introducing students to the traditional manufacturing envi- ronment that is based on push (MRP) system. A good understanding of the inadequacies of the traditional manufacturing contrasted against expected bene ts of implementing lean manufacturing strategies was the focal point for developing lean simulation training tool. In order for this new tool to be e ective in imparting lean learning to students, it was im- portant to assume that all students taking part in the lean production course are novices in computer simulation, despite that computer simulation is o ered as an elective course in industrial engineering at AU. It was important that the simulation models developed be easy for students to use, and require minimal training on the actual use of the software. This goal was accomplished by using Excel as the user interface for inputting data required by the simulation models. All the parameters and variables needed to be run if the scenarios are input through an Excel user interface and the results of the simulation are fed back to the Excel le for the user to analyze. 159 Figure 5.9: Tiger Motors oor layout Figure shows the layout of Tiger Motors. The manufacturing system is composed of 3 Cells (Cell-1, Cell-2, and Cell 3) as indicated. The ow of material is from Cell-1 to Cell-2 and nally through Cell-3, which is the nal assembly Cell. In modeling the traditional push (MRP) system, it was assumed that material is always available at station 1, which is the most upstream of all work stations i.e. this where all production starts. Tiger Motors man- ufacturing system represents a mixed model assembly line. In the traditional manufacturing environment, WIP between stations is normally uncapped, implying that no restrictions were put on the amount of WIP that can build between workstations. To demonstrate the impact of large change overs, an ine cient change over process was introduced at station 5. Because of the di erent ow rates within each manufacturing cell, it became necessary to locate a decoupling bu er inventory between the Cells. The simulation model developed al- lows for the analyst to change all these variables in order to evaluate their impact on systems performance. Figure shows the Excel worksheet used of inputing work station standard task times needed by the Simio model. It is important to note that the task times at each station can thus be changed to match the speci c abilities personnel assigned to a particular station. Each individual can thus be viewed as having their own speci c stochastic task distribution, 160 Figure 5.10: Excel input for station cycle times which is established through time studies. This arrangement allows for the judicious assign- ment of personnel that match each individuals? ability with the complexity of the task. The Simio environment used for developing the simulation model is shown in Figure 5.11, while its 3D representation of the model which allows students to observe the product as it ows through the system is shown in Figure ??. Figure 5.11: Simio development user interface-Push system 161 Figure 5.12: 3D-Simio representation of Tiger Motors shop oor Setting up Simio experiments to enhance students conceptual understanding of the e ect of system variable on system performance (a) Simo experimental setup (b) Simio expermental responses Figure 5.13: Simio experimental setup for investigating the e ects . Figure 5.13 shows the experimental setup for investigating the e ect of input batch sizes and change over time on the performance of the system. In addition, the experimental setup allows students to investigate the e ect of varying the quantity of bu er material kept between cells that is needed to support interrupted production in Cells 2 and 3 given a pro- duction run length. The experimental controls which include change over time(changeover 162 matrix), batch size (entity arrival table), and bu er size are shown in Figure . The experi- mental setup shown is for the largest change over time (SMED3) across all scenarios, while the batch size was increased with each incremented scenario. The main responses include the throughput, throughput time, and system delays for each cell as indicated in Figure . Delays in this particular case were used as a measure of the system?s ability to meet custom orders in a timely manner. Figure 5.14: Batch sequence levels used for investigating in uence of batching on system performance Figure 5.14 represents the levels of batching that were used for demonstrating the e ects of batching on system performance. The batch sequences used in the computer simulation was matched to the batch sequences used during the actual hands on simulations done during students? labs. Sequence 1 represents the lowest level of batching (small batches) while sequence 3 (large batches) represents the highest level of batching. Simio model experimental results for Tiger Motors MRP based system Figure 5.15: Cell -1 Throughput Figure 5.16: Cell-1 Throughput time Figures 5.15 and 5.16 shows the results of the experimental setup shown in Figure 5.13 for investigating the e ects of changeovers, production run batch sizes, and bu ers between stations. The setup shown was used to demonstrate the e ect of varying the batch size in 163 the presence of large changeover times. The batch size is increased with each incremental scenario. Figure 5.15 indicates that the smaller the batch size, the less the throughput while throughput time is at its largest as indicated in Figure 5.16.This experimental set-up pro- vides a good teaching tool that can be demonstrated by the mathematical concepts related to the determination of optimal batch sizes given change over time . By changing experi- mental controls , the relationship between the variables (batch size, change over time, and station bu ers) and their response variables (throughput, throughput time, and Lateness) can be investigated promoting a better understanding of the theoretical concepts . A good example is the relationship between batch sizes, change over time, bu er quantities, system throughput, throughput time, and Lateness. Lateness in this particular case was de ned as the di erence between the time when a customer places and order and when the order is met. Comparisons may also be made between the two models, one representing the a push MRP system and the other representing a pull based lean manufacturing production strategy. Figure 5.17: E ect of batching with large change over times exist Figure 5.18: E ect of batching with small change over times exist Figure 5.17 shows the e ect of batching in the presence of large changeover times. Small batches resulted in more late orders and this is attributed to the reduced throughput rate in the system. On the other hand, Figure 5.18 shows the impact of signi cantly reducing the changeover time. The smaller the batch size, the less tardy orders are, as shown in Figure 5.18. 164 5.6.3 Simulating a Lean based production manufacturing system using Tiger Motors oor layout The lab component of the lean production course (INSY 6800/5800) involved a number of iterations in which students? lab assignments began with a production system charac- terized the inadequacies normally associated with such systems as described earlier. The implementation of lean manufacturing methodologies is intended to overcome the inherent inadequacies of the tradition manufacturing. By developing a second simulation model that integrates some of the lean manufacturing philosophies, we are able to demonstrate and quantify the bene ts of lean manufacturing approach in a virtual environment to the bene t of the student learner. A second model representing a pull production system was developed . The major di erences from the push based MRP model developed earlier were as follows: 1. bu ers between stations is capped. The input bu er status of downstream process establishes processing capability of its next upstream process. 2. A supermarket bu er was introduced to decouple downstream cells from up stream cells. 3. A two bin Kanban system was introduced to control the ow of material between cells. 4. A load leveling strategy (Heijunka) was implemented to smoothen out the uctuations in demand over the predetermined time period (lab time). Implementation of a pull production control system for Tiger Motors using Simio In order to cap WIP between adjacent upstream and down stream station it was necessary to model severs (workstations) that could communicate with each other. To accomplish this, each workstation was modeled to shut down or become blocked when there was no 165 room in the downstream bu er. Two properties were introduced to each sever, Max bu er size (Maxbu erSize) and Minimum bu er size (Minbu erSize) . The Maximum bu er property determines the maximum size of the WIP allowed in the downstream bu er, while the minimum bu er property established the minimum amount of WIP that had to be reached to unlock the server. Figure 5.19: Simio model of Tiger motors pull based production control system Figure shows the pull kanban based implementation of Tiger Motors shop oor using Simio simulation software. Di erences between the pull system ( Figure 5.19 ) and the push MRP system (Figure ??) are evident from the two gures representing each system. The absence of a heijunka in Figure 5.12 is the major di erence between the two systems. In order to model a heijunka box, a source was used that sequenced production Kanbans. A (Source simio object) was used for producing these sequenced production Kanbans. Entity arrivals at the source is done using a reference table, which contains the leveled production entities representing the 2 models of vehicles, the SP and SUV respectively. Workstation 11 (WK11) 166 was selected as the pacemaker process in this particular case. Production Kanban cards from the SP and SUV Heijunka sources are queued at the pacemaker process. Using a Combiner simio object, each Kanban card at the source is matched with the appropriate model of vehicle, thus controlling the sequence of vehicles produced at WK-11. This implementation results in a leveled production (Heijunka). Two Seperator Simio objects were used at the end of Cell-5 to create signals authoriz- ing the replenishment of material to WK-1. Whenever an vehicle (entity) is withdrawn from the Supermarket, a copy of the entity is created by the Seperator. This copy of the entity serves as signal for the source to replenish a similar entity to WK-1, thus accomplishing the pull. A transporter is used to transfer material from the supermarket to the input bu er queues of the rst station of the downstream cell. Each input bu er queue is for a particular vehicle and can only hold a predetermined number of vehicles (entities). A Monitor ele- ment was used to monitor the size of the queue . Based on the predetermined minimum and maximum size of each queue, the transporter is disabled or enabled for pickup of vehicles (entities) from the upstream supermarket, thus accomplishing capping of WIP at the input bu er of rst workstation in the downstream Cell. Tiger Motors pull system model veri cation and results in Simio As a way of verifying the model, an experiment was setup in which the control variables included the change over time, maximum/minimum bu er size between adjacent worksta- tions, and the size of the supermarket at Cell-1. Three change over times (Scenario 1:40 seconds, Scenario 2: 120 seconds, Scenario 3: 300 seconds) were investigated. 167 Figure 5.20: Cell-1 throughput Figure 5.21: System Lateness response re- sults Figures 5.20 and 5.21 shows the throughput of Cell-1 and system lateness respectively. The results generated were as expected, thus verifying the e cacy of the model. When the changeover time is high, the throughput of Cell-1 is low, implying that without an adequate supermarket quantity at the end of Cell-1, downstream processes in Cell-2 and Cell-3 are starved of input material, and consequently customer orders are not met in time as indicated in Figure 5.21. In addition, the use of computer simulation for modeling the two production strategies, push and pull can be used for comparing the two strategies under the same conditions. The lean production strategy resulted in less tardiness of orders when compared to the traditional push strategy under the same condition (see appendix G) 5.6.4 Discussion and Conclusion Virtual learning o ers an alternative and less costly alternative to hands-on training of lean manufacturing concepts. While live simulations are normally time consuming and are team oriented exercises, the use of computer simulation can be an added bene t as students can experiment with di erent ways in which to set up the lab prior to attending the live sessions, thus reducing confusion normally associated with introduction to labs. By making 168 the computer simulation available to students prior to attending the lab, a structured assign- ments could be given in which students would required to input predetermined parameters as input to the simulation model. Students would then be able to relate system performance to experimental variables used. It is from this individual experiments that conceptual un- derstanding begins and is further reinforced through live simulations during labs. Such an undertaking is equivalent to a pre-lab which is intended to prepare students for more rigorous lab work involving live simulations. Although the two computer simulations models appear to be valid representation of Tiger Motors operations based on simulation results, there is need to carry out a usability analysis of of the two models before they are deemed appropriate as a learning tool for students. A survey will need to be developed to capture students? perspective relating to the use of the two models as a learning tool for enhancing lean manufacturing conceptual understanding. It should also be emphasized that the use of computer simulation can not solely be used as an e ective method for lean manufacturing training but should used in combination with live simulation for a more comprehensive understanding of lean manufacturing concepts. 169 5.7 An Assessment of the e ectiveness of hands-on laboratory participation in enhancing student learning 5.7.1 Methodology One of the important goals of this research was to establish whether integrating man- ufacturing laboratories in manufacturing curriculum does enhance students? learning, and thus a student?s body of knowledge. If this can be proven true, then this knowledge can be used as motivation for development of appropriate laboratory exercises in manufacturing education. This is particularly important for those topics in manufacturing that have lacked the hands-on component to reinforce learning. Hands-on manufacturing labs designed to support Manufacturing Systems course (INSY 3800) and Lean Production courses (INSY 6800/5800) at Auburn University were evaluated to determine their e ectiveness in enhanc- ing students conceptual understanding of the subject matter. Students in INSY 3800 courses were undergraduates in their Junior and Senior level of their studies. The INSY 6800 course is o ered every Fall semester and typical enrollment for the course is around sixty students, while INSY 3800 is o ered in Spring and has thus an average enrollment of ninety students. The INSY 3800 consists of a lecture as well as 10 compulsory labs sessions designed to re- inforce lecture material. Students in the course were compelled to attend all lab sessions but were allowed two excused absences without being penalized. The INSY 6800 course was designed to be a lecture only course. However, the instructor for the course required that students acquire some hands-on lean manufacturing experience by engaging in laboratory activities. Because INSY 6800 did not have a lab component, and the lab can only accom- modate a maximum of 30 students at any one time, it was di cult to accommodate all sixty students. This problem was overcome by equally dividing the class into two larger Groups that A and B. A and B are the lab groups that attend the lab at the same time. The groups were further subdivided into three smaller groups (Grp 1, Grp2,and Grp 3) of no more than 170 ten members. It is important to note that students were randomly assigned to the groups and each group consisted of at least three graduates students. Each group was requested to nominate a team leader, whose responsibilities included coordinating all lab activities. On designated days either group A or B would be required to attend a lab session to participate in a designated activity, while the other group attended the lecture. Whatever group was designated for a lab activity could still participate in the lecture by watching a recording of the lecture. The event calender for the activities associated with INSY 6800 are shown in 5.1 on page 141. 5.7.2 Evaluation of student outcomes through written test assessment In order to evaluate the e ectiveness of hands-on labs on students learning, an experimen- tal design was conducted. The experimental design required that students be divided into groups as depicted in 5.1 for participation in predetermined hands-on learning activities re- lated to the classroom lecture. All groups participated in three simulated factory production runs (run1, run 2, run 3) which essentially are live simulations of an assembly production line as already discussed in an earlier section of this project. The production runs are de- signed to demonstrate the incremental improvements that can be made to an ine ciently designed manufacturing system as it evolves from a classical/traditional push manufacturing systems towards a much more e cient leaner manufacturing system. In-between production runs, a student group as depicted in Figure 5.1 was assigned to a lab project that had to be completed before the next production run. Each of the lab projects assigned were intended to add value to the system by improving it through the use of industrial engineering tools. A total of three di erent lab projects were identi ed and for each lab project, two groups out of six groups were assigned. A written test covering the topics that included the lab project was then given at the end of the period to assess students? understanding of the topic and developed skills 5.1. 171 5.7.3 A survey to assess students attitudes and perceptions towards lean man- ufacturing hands-on laboratory learning At the end of the Fall 2012 semester a survey was carried out to determine students? per- ception of learning associated lean hands-on activities associated to the lean manufacturing class (INSY 3800). A total of fty students out of a possible sixty students were able to participate. The data used in this analysis represents students who answered all survey ques- tions and also signed an informed consent statement that was approved by the Institutional review board at Auburn University. Student?s responses were anonymous and the results were analyzed after the completion of the course. The survey instrument used was Qualtrics on-line software. 80 % of the questions required students to respond using a Likert scale to indicate their agreement with particular statements in the question. Of the students that participated in this study, 50% were undergraduate seniors and 50%were graduate students. 76% of the students were male while 24% were female. All students that participated in the study were 20 years and above. Figure 5.22: Current job positions and expected career paths About 50% of students participating in the survey were either already employed in the manufacturing industry or hope to nd a job in manufacturing. Question 7: Students? perception of how the lean manufacturing hands-on activities helped them grasp lean manufacturing concepts taught in the lecture were solicited. Using 172 a Likert scale with 1 being strongly disagree and 5 being, strongly agree, participants were required to indicate their agreement with 5 statements regarding the perceived e ect of the hands on lab activities on their learning of lean manufacturing concepts. In this question students were asked to indicate their agreement with the following statement: statement 1: feel that participating in the hands-on individual Lean Production Labs helped my understanding of the following Lean concepts better than traditional classroom lecture alone would have done. (a) perceived learning (b) Percieved learning Figure 5.23: Students perceived learning when participating in hands-on lab activities Figure 5.23 shows student?s response to statement 1. Students perceived the continuous improvement of hands on activity as one that o ered the most bene ts with respect to 173 learning when compared to lecture alone. Students also found the labs helpful in enhancing their understanding and grasp of concepts related to the distinction between pull and push systems. However, students had ranked the load leveling as the concept that the lab did the least to enhance their understanding, despite the fact that more than 70% of the students did agree that they felt that particular lab was helpful in enhancing their understanding. Question 8:This question sought to get feedback on how important each hands-on lab activity was as viewed by students. Using a 7 point Likert Scale ( 1: Not at all important and 7: extremely important), students had to respond to the question: From experience with the Lean Production Course you just participated in, provide a per- spective as to how necessary it is to include the following hands-on activities to supplement classroom lectures for deeper learning and understanding to occur. Figure 5.24: Perceived importance of lean lab elements, Q8a Responses indicated that continuous improvement hands-on lab was considered to be the most important of the hands-on learning activities, followed by Single Minute Exchange of Die(SMED) hands on lab, implementation of Kanban system, and simulating push and pull system ( Figure 5.25). 174 Figure 5.25: Perceived importance of lean lab element, Q8b Question 9:From the following list of hands-on activities associated with the Lean Pro- duction course, rank each activity according to which o ered you the best learning experience with regards to enhancing your understanding of Lean Manufacturing concepts. Figure 5.26 shows the responses for this question which indicated that continuous improvement lab and SMED were viewed as o ering the most learning of all activities. Figure 5.26: Ranking of hands on lab activities according to the best learning experience o ered . Question 14: In this question, respondents had to state their level of agreement with three statements regarding how they perceived their participation in hands-on lab activities 175 and if they were bene ted in respect to raising their interest level in the topic taught in the lecture as well as helping them relate classroom theory to practice. Figure 5.27 shows the results of students? responses to the statements. The results showed that a majority of the students (around 80%) felt that participating in hands-on labs helped raise their interest level in the subject being taught in addition to helping them relate taught theory to practice. Figure 5.27: bene ts of hands-on lab participation with respect to interest level Question 11: The lean manufacturing class was o ered to outreach students who were unable to attend to any hands-on lab activities. As a means of getting outreach students? participation in labs, all hands-on labs were video recorded. This enabled the outreach students to participate in the labs by watching the videos and later responding to questions posted as part of their lab assignment. To assess the e ectiveness of this method of involving outreach students, a question was posed to outreach students in which they had to state their agreement with a particular statement (see gure 5.28). Outreach students? responses were positive, with a majority of the students indicating that they were able to clearly follow the live simulated production runs. Students also indicated that the live production runs also provided a good learning experience that related well to the topic taught in class. 176 Figure 5.28: Long distance lab participation through video streaming 5.7.4 Discussion By gathering feedback from students about hands-on lean manufacturing exercises that they participated in, we were able to identify areas that bene ted the students the most and also identi ed areas that needed improvement. In the lab, students had the chance to work in a team environment which allowed to them solve problems as a group, it was thus not surprising to note that students perceived continuous improvement lean manufacturing hands-on leaning activity as the one they felt bene ted them the most with regards to learning (Figure 5.25). Students also reported relatively high scores for perceived learning in relation to understanding the di erence between MRP push based and pull based production control systems. However it was interesting to note the relatively lower perceived bene t to learning associated with Heijunka load leveling and single minute of exchange of dies aspects of the lab. The relatively low score associated with Hiejunka load leveling may be attributed to the less than adequate involvement of the students in the implementation and installation of the Heijunka load leveling system. Heijunka load leveling was demonstrated to the students physically, but it is possible that its bene ts on system performance to a large extent went unnoticed by students. Therefore, while the Heijunka system was in place 177 and students knowingly or unknowingly interacted with it, it is possible that not much may have been done to showcase its bene ts on system performance. The survey response to question 1 may indicate the presence of a positive correlation between students? perceived bene t of a hands-on topic and students? active participation related to the topic. It appears that the more students are actively involved through hands-on learning activities, the more likely they view that topic as bene ting their overall understanding. This is evident by the low scores that the SMED and Heijunka hands-on aspects of the lab received by students ( gure 5.25). With more educational institutions o ering outreach courses, it?s always a challenge to o er laboratory hands-on learning activities to outreach students. In this lean manufacturing course, outreach students successfully participated in hands-on activities through watching videos. Although the outreach students did not actively participate in the hands on labs, the lab video enabled them to participate in all the lab assignment that regular full time students were assigned. 5.7.5 Conclusion Students perceptions show that laboratory hands-on activities are viewed positively by students. Hands-on labs can o er valuable learning to students by providing an alternative viewpoint from that o ered in the classroom. By o ering hands-on labs students not only learn about the tools taught in class but get the opportunity to put into practice, thus helping them develop particular problem solving skills as well as improving their con dence in the application of appropriate industrial engineering tools. Indications from students? surveys were a strong indicator that hands-on learning is a necessary activity required to close the competency gaps of manufacturing engineering students. 178 5.8 Evaluating the e ect of hands-on laboratory participation on students con- ceptual understanding through written tests Besides students? perceptions on the value of laboratory participation on their concep- tual understanding, testing students through tests and quizzes o ers one way in which the value of having students participate in labs can be evaluated. To evaluate the e ectiveness of laboratory as an add on for reinforcing classroom learning, periodic test were given to students as discussed in section and depicted in Figure 5.1 on page 141. Since students were divided into six project groups with two groups participating in hands on projects related to topic that was been taught at the time, the objective of the exercise was to determine if students that participated in projects had better understanding of the topic compared to those that only received instruction through lecture alone. Three di erent projects related to Lean Manufacturing were selected and assigned to groups as outlined in Figure 5.1. . A control treatment experimental approach was taken in which the treatment group partic- ipated in both the lecture and project.The objective of this experiment was to determine if involving students in hands-on learning contributed to better understanding of the concepts taught in class. The rationale being that the more active students are involved through projects the deeper learning occurs, and if this deeper learning does indeed occur it should be evident in students? assessment. 5.8.1 Evaluating the performance of treatment group(SMED Lab participation) against control group(None participation in SMED lab Value stream mapping was the rst topic that was covered in classroom lecture and as- signed as a project to the two groups. The details of this project were outlined in section 5.5.1 on page 145. After completion of classroom lecture, as well as the hands on value 179 stream mapping exercise (VSM), assessing students? understanding of the topic was con- ducted by giving out a test. The test consisted of a total of eighteen questions with seven questions related to Value stream mapping. The composition of questions used to assess students understanding is provided in appendix F.1. Aggregate scores for each group were then determined and compared using the generalized linear model ANOVA analysis. Fig- ure 5.29(a) shows the average performance of each group with respect to a value stream map test question. It?s apparent from the graphs that scores for groups A3 and B3 appear greater than those of other groups. To determine how signi cant this di erence is requires the analysis of variance study to be performed. A general linear model with two factor at 2 levels each was used for the analysis. The rst factor (treatment) represents lab partic- ipation coded 1 for non participation and 2 for participation. The second factor (student type) represents the level of each student, coded as 1 for undergraduate student and 2 for graduate student. A xed factor crossed GLM experimental design was thus used to assess the e ect of lab participation on test scores. Figure 5.29(b) shows the results of ANOVA analysis using question 18 as the response variable and lab participation as the factor at two levels. The two levels considered were none participation in hands-on VSM lab as one level and participation in hands-on VSM lab as other level. Since the number of participants at at each level were di erent a general linear model (GLM) was used for analysis. 180 (a) Group performance Comparison with respect to VSM test questions (b) Analysis of variance for Q18 Figure 5.29: Comparing group performance with respect to speci c VSM test questions 181 The ANOVA F-test indicates that with respect to Q18, there is signi cant evidence for hands-on lab participation (treatment) e ects (p-value =0.023). The con dence interval for di erence in means between the treatment and control group does exclude zero, thus indicating a signi cant di erence between the treatment group and control group means. Dunnet?s con dence interval comparisons indicates that the treatment group mean is higher than the control group. Figure 5.30: Group performance comparison wrt VSM using General linear model (GLM) 5.8.2 Evaluating the performance of treatment group(SMED Lab participation) against control group(None participation in SMED lab In this analysis the performance of all three groups was done to determine if the treatment group (Participation) in SMED hands-on lab translated to better conceptual understand- ing. It was hypothesized that participation of hands-on lab leads to a better conceptual understanding, and if this was the case it was expected that the group participating in the SMED hands-on lab would score signi cantly better than the none participating groups. Two out of 6 groups participated in a hands-on lab project described earlier in section 5.5.3 on page 152. All groups participated in class every single minute of exchange lecture. After the conclusion of this lecture and lab project, an in-class written test was given to estab- lish student comprehension of the SMED concept. A total of seven questions composed of a combination of multiple choice and ll in the blank type questions. The questions used 182 to assess students conceptual understanding are provided in the appendix F.2. Figure 5.32 shows the aggregated scores with respect to each SMED test question. Figure 5.31: Group performance Comparison with respect to SMED test questions Groups A1 and B1 participated in both the SMED classroom lectures in addition to hands- on SMED project. The performance of each group was assessed through a written test whose scores are summarized in Figure ??. While the total scores for groups A1 and A2 appear to be relatively larger, the same cannot be said with respect to all individual questions. From Figure ??, it is apparent that both Groups A1 and B1 appeared to perform relatively better for questions Q11, Q16, and Q19.Iin order to reach a conclusion on the signi cance in the di erence in test scores it was necessary that a general linear model (GLM) be applied. A GLM was relevant since the design was unbalanced. A General Linear Model with two factors at 2 levels each was used for the analysis. The rst factor (Smedlab) represents lab participation coded as 1 for non participation and 2 for participation. The second factor (student type) represents the level of each student, coded as 1 for undergraduate student 183 and 2 for graduate student. A xed factor crossed GLM experimental design was thus used to assess the e ect of lab participation on test scores. Figure 5.32: Signi cantly Figure 5.32 shows the results of GLM ANOVA analysis for only those questions that were found to show signi cant di erences between the performance of the treatment group (Grp A1 and B1 that participated in SMED project) and the control group (None par- ticipation in SMED project). Out of a total of seven questions assessed Figure ?? shows that questions Q11 and Q19 indicated a signi cant di erence in performance between the groups, with p-values of 0.009 and 0.045 at 95% con dence interval. The p-values obtained for other questions indicated no signi cant di erence in performance between treatment and control. However, a signi cant di erence between the groups was obtained for the total score (Tot SMED) with a p-value of 0.002. The signi cant di erence in the total score indi- cates that despite the absence of signi cant di erence on some test questions, the treatment group still performed relatively better on some questions such as to in uence the overall performance in favor of the treatment group. 184 5.8.3 Discussion Assessing student?s assessment is an important aspect for evaluating the e ectiveness of intervention strategies in education. In the case of hands-on lab assessment at Auburn University, a treatment control experimental design was used in which the treatment group was subjected to both hands-on learning activities through lab work and classroom lectures. The control group, was only subjected to the lecture. While the performance comparisons for the individual questions did not show any conclusive di erence (p-values > 0.05) among the treatment groups and control, the overall performance score for the treatment group indicated signi cant di erence between the control and the treatment groups for the two sets of experiments. In the rst experiment that involved assessing the e ectiveness of value stream mapping hands-on exercise on students conceptual understanding, an ANOVA analysis indicated that students that participated in the value stream mapping hands-on exercise performed signi cantly better with respect to the overall score. Comparisons made using at 95% con dence interval yielded a p-value of 0.023 indicating signi cance di erence between the control and treatment group. With respect for the value stream mapping questions, students that participated in this lab had a greater group aggregated score than their counterparts. Comparisons at individual question levels yielded only two questions that showed a signi cant di erence in performance between the groups. The treatment groups appear to have signi cantly performed better on questions 12 and 18 with p-values of 0.0032 and 0.0023 respectively. It would appear that strong performance on question 18 for the control group can be attributed to similarities with hands-on lab assignment (see section gure 5.3, on page 148) and appendix F.1 . The hands-on experience with value stream mapping appears to have had strong in uence on the performance of the control group with respect to question 18. The main di erence between question 18 and the rest of question is that it involves the use of an analytical tool rather than relying of student recall of facts. It would thus appear that because the treatment group may have performed better on this 185 question because they used the tool in a practical setting as opposed to their counterparts that were taught about the tool, but may not have been actively putting it to use. The results of the second experiment in which the treatment group participated in the single minute of exchange of dies (SMED) hands-on lab described in section 5.5.3 showed similar results to the rst experiment. Out of a total of seven questions tested, the treat- ment group (hand-on lab participation) performed better in only 2 questions. However, the treatment group scored signi cantly better with respect to the overall test score, thus indi- cating that the treatment group must have performed better on most questions, although not signi cantly di erent at the individual questions level as indicated by p-values of less than 0.05. In a similar manner, it was interesting to note that signi cant di erences in per- formance between the groups was obtained for the analytical type questions (question 18, see appendix F.2), rather than the recall type of questions in which students were required to state the facts related to the material taught in class. 5.8.4 Conclusion The data used in the study showed statistically signi cant results for the overall score and in some cases individual questions. The statistical analysis indicates that students? participation in hands-on learning contributes to students? development in the use of speci c skills that are subject to the lab. While there there were no statistical signi cant di erences in performance between the control group and treatment group for the majority of questions tested, signi cant di erence were found for questions that required analytical and problem solving skills related to speci c tools used that were part of the lab. While students in the control group (non lab group) may have practiced using the tools tested in exam on an individual basis, the success of lab group participants with respect to analytical problems may have attributed to a number of factors that include the bene ts of collaborations during labs, thus leading to increased learning occurring at the aggregated level. It is possible that weaker 186 students may have bene ted more from this collaboration thus raising the aggregated score of the treatment group compared to the control group. The results shows strong evidence that the use of hands-on labs is bene cial to student learning, particularly when attempting to develop speci c skill-sets related to the use of industrial engineering tools. The result of students? surveys also indicated that the students who participated in the hands-on labs associated with manufacturing related courses believed that the hands-on labs bene ted their learning experience, further reinforcing the truth regarding the hypothesis that hands-on labs add value to students learning. 187 Chapter 6 Summary, Conclusions, and Future Work The motivation for this dissertation was to contribute a potential methodology that can be used in manufacturing curriculum to bridge the competency gaps of manufacturing stu- dents. This is an important contribution considering the stark projections in the shortfall of competent skilled professionals in the manufacturing sector. The American Society of Engi- neering Education has previously reported that educational institutions are not in line with the country?s increasing demand. Manufacturing as a career option has su ered an image problem as well , with progressively less and less students enrolling in manufacturing related elds with each passing year. The over reliance of the lecture as a predominant method of instruction in technical courses is largely to blame for decreased interest and understanding of what manufacturing entails. The image of manufacturing as a potential career path needs to be resuscitated. In this dissertation we established the important elements necessary for e ective manufac- turing education by conducting a meta-analysis of existing research relevant to manufactur- ing education as well as conducting stake-holder surveys. Findings indicate that among the many elements that are part of manufacturing education, a subset of these lend themselves well to hands-on instruction and thus the importance of integrating them as part of hands- on learning activities in an e ective manufacturing curricula. Survey results indicated that industry considered problem solving skills, teamwork, and written and communication skills as the attributes most desired among many others that are the most sought after attributes of future Manufacturing Engineers. 188 Prioritization of the important elements needed for an e ective manufacturing curricula was imperative as it can serve as a guideline for colleges with manufacturing curriculum on what element they should integrate focus on in order to better prepare manufacturing student for careers in manufacturing. Not only is knowing what elements are to include an e ective manufacturing curriculum enough, but knowing what level of instruction each of the identi ed elements should be taught is essential. Survey results indicate that teaching that integrates scaled down equipment and industrial grade equipment as part of hands- on learning is the most bene cial to students. Live simulation that imitates real world manufacturing environments appears to be bene cial for a majority of students surveyed. However it should be acknowledged that it is not always possible to conduct live simulation for some elements of manufacturing education due to any number of possible reasons, such as safety concerns for students, prohibitive cost of equipment and lack of adequate sta ng levels among many other possible reasons. As an alternative to live simulations and use of physical hardware, computer simulations should be considered. We have followed this methodology in this dissertation to assess the possible bene ts to students learning. A realistic simulated factory (Tiger Motors) was designed to be used as testbed for a number of interdisciplinary manufacturing hands-on activities. Tiger Motors was designed to be mixed model assembly line with the ability to accommodate three models of vehicles. Students were able to work on number of hands-on activities, all of which were driven by one common goal, to improve system performance in a similar manner to what you would nd in real industry. To assess the e ectiveness of hands-on labs on students learning, students surveys were conducted to gather students? perceptions on the value of the introduced hands-on activities. In addition, a post test experimental design in which the performance of a control group was assessed against that of treatment group. Results of both the students? surveys and students? performance on written test indicate that not only do hands-on labs increase students interest level in the subject matter, but bene ts student learning with respect to speci c skill sets that are considered vital competencies in many manufacturing careers. 189 6.0.5 Future Research The ndings of this dissertation support the hypothesis that hands-on learning can be bene cial to students? learning. While the research goals were met, a number of potential areas for further research were identi ed. The two years experience of working with students in the lab revealed a number of potential research opportunities. Student collaborations are an important part of student learning. While students in most hands-on labs are normally randomly assigned to groups, there is a need to investigate the impact of team dynamics on students learning. In a number of labs associated with the ndings of this research, students were often required to work in groups as large as ten. A team leader was normally assigned by group consensus and other team roles were decided among the members of the group. It was apparent that the success of each group, in many cases depended on the organizational skills of the team leader assigned as well as the ability of the group to work well as a unit. The groups that appeared to have good leadership seemed to to be more successful, with team members reporting satisfaction with work done in the group. However, this was not the case for the groups that lacked good communication, which led to disharmony among team members and confusion on the role of each team member. Observing these di erent team dynamics was interesting, taking into account the alleged competency gaps. The industry survey indicated that team work and communication are highly sought competencies in manufacturing. An investigation on how team composition a ects overall team performance, as well as impacts students? learning in a group setting, may be an important research area that can help us learn how to form successful collaborative teams based on team member?s learning styles and abilities. Establishing students? learning styles using any of the the proven peda- gogical learning theories, such as Kolb?s learning theory or Blooms taxonomy, could be used as a basis for grouping students in ways that bene t all team members. 190 The lab needs to be interdisciplinary in nature, providing an opportunity of testing various theories covered in manufacturing related courses. The lab should provide students with the platform to put into practice other tools taught in di erent classes. It is always helpful for students to see real application of particular tools where tangible results can be observed. Taking into consideration that a large number of students that participated in the lab had previously taken courses in linear programming, quality control, and ergonomics classes as part of their degree requirements, the opportunity of participating in a manufacturing lab was an opportunity to apply put some of the theories and concepts taught in those courses into practice. The lab provides a realistic platform for students to apply some of the tools taught in the respective courses. A good example would be the application of human factors design principle in evaluating workstation design. This aspect could be incorporated as one of the required lab elements for a fuller interdisciplinary learning. Incorporating human factors would be helpful in reducing the number of errors associated with assembly tasks due to inadequately designed work instruction and poorly laid out work stations. Such skills are important for manufacturing engineers. Making use of an interdisciplinary group according to prior classes taken could be helpful in forming e ective teams that will bene t all team members. A number of quality related issues were apparent in many of the live simulation runs conducted in the lab. While the students did their best to eliminate the occurrence of errors in successive runs, there wasn?t much consideration for the use of any quality control tools or any scienti c quality methods for reducing the number of defects occurring in the the system. Design for assembly and manufacturability was another potentially important lab element that needed to be considered in future labs, especially in assembly operations. Integrating design for manufacturing and assembly guidelines in these labs could be useful in helping students get a rm gripper of the concepts. The failure to take into consideration 191 these design guidelines was apparent in various assembly con gurations reached by various student groups. An interdisciplinary lab like Tiger Motors may be the opportunity for faculty members responsible for the teaching of various courses to develop hands-on modules that could be used as typical teaching references for in-class discussion. For instance, the lab would provide a good reference for simulation classes. Historical production data has been collected over the two year period that Tiger Motors lab has been in existence. While in many cases, students in the simulation class are required to undertake simulation projects, Tiger motors provides a test bed for students wishing to test the simulation skills using Data that can be veri ed and validated. Data related to production runs collected over a period of a year the lab has been operating, can be useful to students taking the simulation class. The sentiment has already been echoed by some distance learning students that suggested that, the existence of a simulation model of the production runs could be a good add on especially for outreach students. The lab also provides a platform for testing out newer advanced technologies that may be an integral aspect for 21st century new age manufacturing. The use of radio frequency identi cation technology is slowly gaining popularity in manufacturing and many other elds. There is need for students to have a conceptual understanding of how it works so as to equip students with the ability of prescribing solutions that utilize new age technology. The material replenishment at Tiger Motors utilizes physical kanban cards which could be substituted with a computerized kanban system that utilizes RFID. Another opportunity lies in use of information technology. It is envisaged the 21st manu- facturing will be paperless. The use of video based work instructions as well real time data collection and reporting are some challenges faced by future engineers. In simulated produc- tion runs most of data was collected manually using forms provided in the appendix. This 192 process proved too tedious and error prone. 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American Journal of Physics, 65(10), 987. doi:10.1119/1.18702 199 Appendices 200 Appendix A An Industry perspective on important elements required for manufacturing education 201 A.1 Student perceptions on introductory manufacturing lab in enhancing stu- dent learning and interest 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 1/10 Default Question Block Informed Consent FormIntroduction This Survey seeks industrialist's perspective on what elements/components should be included in an effectivemanufacturing teaching laboratory designed with the purpose of bridging the competency gap of graduating manufacturing students. This study is being conducted by Yamkelani Moyo, PhD candidate in the Industrial and Systems underthe Direction of Dr Richard Sesek, Assistant Professor in the Industrial and Systems Engineering Department at Auburn University. We hope to use the information you provide as input in the development of an effective taxonomy formanufacturing education. Procedures You will be asked to answer a series of questions based on your own experience as a direct/indirect employee oremployer in the manufacturing industry. It should not take more than 20 minutes to complete this survey . Questions are designed to determine what elements/componets you would expect an effective manufacturing curriculum designed with thegoal of bridging the competency gaps in manufacturing to have. Your views regarding on how manufacturing students should be trained at University level to improve their employability skills is important. This questionnaire is being conductedusing Qualtrics online survey software. Risks/Discomforts Risks are minimal for involvement in this study. Although we do not expect any harm to come upon any participants due toelectronic malfunction of the computer, it is possible though extremely rare and uncommon. Benefits There are no direct benefits for participants. However, it is hoped that through your participation, researchers/educators willgain valuable knowledge on how to streamline manufacturing curriculum to fit the dynamic nature of today's manufacturing industry. The results of this survey together with perspectives of educators will provide valuable information required todevelop an effective taxonomy for manufacturing education. This taxonomy could thus serve as basis for developing consensus guidelines for an effective manufacturing curriculum required to revamp the US manufacturingindustry. Confidentiality All data obtained from participants will be kept confidential and will only be reported in an aggregate format (by reporting onlycombined results and never reporting individual ones). All questionnaires will be concealed, and no one other than then primary investigator and assistant researches listed below will have access to them. The data collected will be stored in theHIPPA-compliant, Qualtrics-secure database until it has been deleted by the primary investigator. Compensation There is no direct compensation, rather than the satisfaction one may get for making a contribution intended to revamp themanufacturing education and thus indirectly contribute towards revitalizing the manufacturing sector. ParticipationParticipation in this research study is completely voluntary. You have the right to withdraw at anytime or refuse to participate entirely. If you desire to withdraw, please close your Internet browser and notify the principal investigator at this email: yzm 0005@auburn.edu. Questions about the Research If you have questions and you do not feel comfortable asking the researcher, you may contact Auburn Universities UniversityOffice of Human Subjects Research or Institutional Review by phone (334)-844-5966 or email at hsubjec@auburn.edu or IRBChiar@auburn.edu. s I have read, understood, and printed a copy of the above consent form and desire on my ownfree will to participate in this study. 202 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 2/10 Yes No Executive Upper Management e.g. Production Manager, Plant Manager, Quality Assurance Professional e.g. Quality Engineer, Safety Engineer, Mechanical Engineer Technician e.g. Drafters, PLC programmer, Other PhD Masters Bachelors Associate High School Yes No Industrial Engineering Manufacturing engineering Electrical engineering Chemical Engineering Safety Engineering Other What position do you hold in your Company? What is the highest level of education you've attained Are you an Engineering Degree holder What type of Engineering degree did you study for 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 3/10 Fabricated Metal Products, e.g. Automobiles, Air craft, machine building Metal processing. e.g. Steel, aluminum etc Ceramics and none metal processing Chemicals, Coal, petroleum, plastics and rubber, pharmaceuticals Paper, paper products, printing, publishing Wood and wood products Other manufactured products Less than 50 Between 50 and 100 Between 100 and 200 Between 200 and 1000 More than 1000 High Volume, Low variety manufacturing activities Low Volume, High Variety manufacturing activities Low Volume, Low Variety High volume, High variety Select from the list given below, the category that best describes the manufacturing activities of yourorganization: What is the number of full time employees directly employed in your organization? What type of Manufacturing would you consider your company to be engaged in:Low variety manufacturing firms produce a select number of products over a number of years before switching to a different product. Competency Gaps in Manufacturing: Taking into perspective your own experience as an entry level professional and any interactions you mayhave had with other entry level professionals in manufacturing related jobs, please indicate your agreement with the following statement: Introducing hands-on approach to teaching the the given topics in a manufacturing curriculum at collegelevel would be beneficial in addressing the competency gap in manufacturing. Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 4/10 Written and Oral Communication Specific manufacturing process Knowledge Manufacturing process Control Product and Process Design Business knowledge/Skills Project Management Teamwork/ work effectively with others International perspectives Manufacturing Systems knowledge Quality Systems knowledge Materials knowledge Problem solving skills Supply Chain Management Entry Level Competence Using a scale of 1 to 10, with 1 for least competent and 10 most competent, Indicate your perspectiveon the competency of newly graduated manufacturing/Industrial engineers with regards to competency and effectiveness in the manufacturing environment. Competency Competency Gaps in Manufacturing: Rank the the given competency gaps according to how important they are. Importance in this senseimplies that addressing the said competency gap through changes in manufacturing curriculum will benefit manufacturing industry. Least Important: 1 2 3 4 Most Important 5 Business knowledge and skills Project Management Lean Manufacturing and Six 0 1 2 3 4 5 6 7 8 9 10 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 5/10 Sigma knowledge Environment, occupational health and Safety knowledge and competency in production machinery operations Supply chain management Knowledge raw materials Manufacturing process control Written and and Oral Communication Skills Product and Process Design Quality Systems knowledge Specific Manufacturing process knowledge Technical drawing Manufacturing Control: Following the example shown below, please fill out the table below to best describe your belief andviews on what elements of manufacturing control need to be included in manufacturing education to address the competency gap that may exist in this category: Key to teaching Levels: 0. Not required1. Conceptual (Theory, Mathematical Models) 2. Computer Simulation and Gaming3. Physical Simulation Modeling and Games (e.g. Using Lean simulation games e.g. Lego factory) 4. Table top (Scaled down equipment)-(e.g. table top manufacturing processes equipment)5. Industrial Grade Equipment/Commercial Software Example:Using the Key to teaching levels shown above, complete the table as shown below if your believe that: Process Monitoring should be an Optional topic in manufacturing curriculum that needs to be taught with the aid ofComputer Simulation (2) Process Control is Useful and needs to be taught using industrial Grade Equipment (5). Complete the Table Given below following the Example above: Necessary Useful Optional Not needed Process Monitoring (Statistical process control, real time process monitoring) Process control 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 6/10 Extremely Important Very Important Neither Important nor Unimportant Very Unimportant Not at all Important Shop floor control Computer Aided Inspection Maintenance Management Product Design:Product design is the process of creating a new product to be sold by a business or enterprise to its customers. it is concerned with efficient and effective generation and development of ideas through aprocess that leads to new products. Product designers conceptualize and evaluate ideas, making them tangible through a systematic approach. their role is to combine science, art and technology tocreate three dimensional goods. This evolving role has been facilitated by digital tools that allow designers to communicate, visualize and analyze ideas in a way that would have taken greaterresource in the past. How important is it for Manufacturing/Industrial Engineers to acquire knowledge in Product Designthrough manufacturing education: Product Design:Following the example given below, complete the table to best describe your view on what elements of product design need to be included in the manufacturing curriculum to address the manufacturingcompetency gap? Key to teaching Levels: 0. Not required1. Conceptual (Theory, Mathematical Models) 2. Computer Simulation and Gaming3. Physical Simulation Modeling and Games (e.g. Using Lego to demonstrate concepts in Lean and Six Sigma) 4. Table top (Scaled down equipment)-(e.g. table top manufacturing processes equipment)5. Industrial Grade Equipment/Commercial Software Example:Using the Key to teaching levels above, complete the table as shown below if you believe that: Computer aided Design/Drafting is necessary topic in manufacturing curriculum that needs to be taught withindustrial grade equipment (5). Computer aided engineering is Useful and needs to be taught using Industrial Grade Equipment (5). 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 7/10 Complete the Table Given below following the Example Given above: Necessary Useful Optional Not Needed Computer Aided Design/Drafting Computer Aided Engineering Process Design Facility Design/Plant layout Design for Manufacture/Design for Assembly Group Technology Any other important component Product Design:Indicate your views regarding the capability of manufacturing engineers with regards to their competency in the use of any of the following product or process design tools:Design specification generation 2 dimensional modeling tools: eg. Auto cad, Auto-desk inventor3 dimensional modeling tools: e.g. Solid works, Auto-cad, Catia, Solid Edge Rapid PrototypingValue Engineering Design for Manufacture/Design for Assembly Design Specification generation 2 D Modeling Software 3 D Modeling Software Value Engineering Design for Manufacture (DFM)/Design for Assembly (DFA) Should be able to Use Should have basic knowledgeable and have ability to interpret Not necessary Does not Apply Manufacturing process Automation and technologies:Following the example shown below, please complete the table to best describe your view on what elements of manufacturing automation and technologies need to be part of manufacturing educationto address the competency gap in manufacturing. Key to teaching Levels: 0. Not required1. Conceptual (Theory, Mathematical Models) 2. Computer Simulation and Gaming3. Physical Simulation Modeling and Games (e.g. Using Lego to demonstrate concepts in Lean and Six Sigma) 4. Table top (Scaled down equipment)-(e.g. table top manufacturing processes equipment) 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 8/10 Not at all Important Very Unimportant Neither Important nor Unimportant Very Important Extremely Important 5. Industrial Grade Equipment/Commercial Software Example:Using the Key to teaching levels above, complete the table as shown below if you believe that: Automated material handling is necessary topic in manufacturing educations that needs to be taught with industrialgrade equipment (5). Automated packaging is useful and can be taught using physical simulation models (3). Complete the Table Given below following the Example Given above: Necessary Useful Optional Not Needed Automated Material Handling Automated Packaging Automated Storage and Retrieval Systems Numerical Control Computer Numerical Control Programmable logic Controllers Direct/Distributed Numerical Control Adaptive Control Flexible Manufacturing Cells Machine Vision Radio Frequency identification applications (RFID) Metrology Using Automated Inspection Methods Business Function in manufacturing eduction How important is it for Manufacturing/Industrial Engineers to acquire knowledge and skills related tobusiness side of manufacturing 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 9/10 Business Functions in Manufacturing:A list of elements that are potential components of a manufacturing curriculum are provided. From this list of elements provide your view to indicate whether any of these elements should be made anintegral part of manufacturing education curriculum to close the competency gap that exist in manufacturing. Following the example given below please complete the table below to best describe your beliefs and views on what Business function elements should be made and integral part of manufacturing education? Key to teaching Levels: 0. Not required1. Conceptual (Theory, Mathematical Models) 2. Computer Simulation and Gaming3. Physical Simulation Modeling and Games (e.g. Using Lego to demonstrate concepts in Lean and Six Sigma) 4. Table top (Scaled down equipment)-(e.g. table top manufacturing processes equipment)5. Industrial Grade Equipment/Commercial Software Example:Using the Key to teaching levels above, complete the table as shown below if you believe that: Demand forecasting is a necessary topic in manufacturing education that need to be taught using Industrial gradeequipment or software (5) Order entry should be made an optional component of manufacturing curriculum and teaching it at theconceptual/theoretical (1) level should be the minimum teaching requirement. Complete the Table Given below following the Example Given above: Necessary Useful Optional Not Needed Demand Forecasting Order Entry Customer Billing Payroll Accounting Manufacturing Planning:Manufacturing Planning encompasses all planned activities involved in determining the most efficient way of producing a product. It requires planning both manpower and machinery. Following the example given below, please complete the table to best describe your view on what 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 10/10 elements of manufacturing planning need to be made an integral part of manufacturing educationcurriculum to address the competency gap that exist in manufacturing education. Key to teaching Levels: 0. =Not required1. =Conceptual (Theory, Mathematical Models) 2.= Computer Simulation and Gaming3. =Physical Simulation Modeling and Games (e.g. Using Lego to demonstrate concepts in Lean and Six Sigma) 4. =Table top (Scaled down equipment)-(e.g. table top manufacturing processes equipment)5.= Industrial Grade Equipment/Commercial Software Example:Using the Key to teaching levels above, complete the table as shown below if you believe that: Material Requirements planning (MRP) is necessary topic in manufacturing curriculum that needs to be taught withindustrial grade equipment (5). Capacity Requirements planning is Useful and can be taught using Computer Simulation and Gaming (2). Complete the Table Given below following the Example Given above: Necessary Useful Optional Not Needed Materials Requirement planning (MRP) Capacity requirements planning (CRP) Computer aided process planning (CAD) Established standard Time Data (Stop time studies and Predetermined time studies) Scheduling Assembly line Balancing Bill of Material Processor Cost Estimating Appendix B Unbalanced work instructions 212 R & D P RO D Pr o cess: T L Setu p S T 1 A S S Y A U P / N : A ll A U P a rt s A ve Seq No Operation Element P a rt # S erial # K ey p oint T1 T2 T3 T4 T5 - 7 3 - 244526 #D I V / 0 ! - 5 4 - 4124067 #D I V / 0 ! -4 0 -4 1 - 383221 - 4506961 #D I V / 0 ! - 7 1 - 303226 #D I V / 0 ! -8 5 -3 8 - 4211445 - 447721 #D I V / 0 ! - 2 - 300401 Op e r a t io n s w o rk s t a n d a rd s h e e t S t a nda r d Ti m e ( s ) 1 2 3 54 2 3 4 5 6 1 -3 4 -7 3 - 302221 - 244526 #D I V / 0 ! -1 6 -8 5 - 4157223 - 4211445 #D I V / 0 ! - 8 2 - 4211529 #D I V / 0 ! # D IV /0 ! REMARKS Stati o n C y cl e ti me B B B B B Q u a lit y Safety 6 7 8 7 8 B 36 37 13 16 10 8 54 40 41 71 8 38 2 34 16 82 S p eed er B SUV 73 S R & D P RO D Pr o cess: T L Setu p S T 2 A S S Y A U P / N : A ll D o w P a rt s A ve Seq No Operation Element P a rt # S erial # K ey p oint T1 T2 T3 T4 T5 - 8 3 - 4211385 #D I V / 0 ! -1 3 -5 6 -5 0 - 300421 - 362226 - 4205058 #D I V / 0 ! -9 1 -3 0 -6 4 -6 5 - 4210631 - 302321 - 307026 - 243126 #D I V / 0 ! -5 1 -1 3 -1 7 - 4528357 - 300421 - 621521 #D I V / 0 ! Op e r a t io n s w o rk s t a n d a rd s h e e t S t a nda r d Ti m e ( s ) 9 10 11 12 9 10 11 12 # D IV /0 ! REMARKS Stati o n C y cl e ti me S B Qu al i ty Safety 7 68 43 44 23 4 83 56 64 91 50 94 30 13 17 B B 13 65 51 S p eed er SUV R & D P RO D Pr o cess: T L Set u p S T 3 A SSY A U P/N : A ll A U Par t s Av e Se q No O p eratio n E l em en t P a rt # S erial # K ey p oi nt T1 T2 T3 T4 T5 -1 6 -9 0 - 4157223 - 4211060 # D IV/0 ! -1 0 -1 4 -1 5 - 306201 - 245821 - 362221 # D IV/0 ! - 4 0 - 383221 -1 0 -2 7 -8 5 - 306201 - 4121934 - 4211445 -6 8 -5 2 - 371026 - 4159553 # D IV/0 ! - 9 5 - 4249040 # D IV/0 ! S t a nda r d Ti m e (s ) Operations work standard sheet 13 14 SA1- 1 SA1- 2 13 14 SA1- 1 SA1- 3 SA1- 2 - 2 0 - 4251162 # D IV/0 ! -2 1 -4 5 - 243221 - 3000840 # D IV/0 ! -fr o n t bum pe r # D IV/0 ! #D IV/0! St at io n C y cle t i m e REMARK S Q u a lit y S a fe ty SA1- 3 SA1- 4 15 SA1- 4 15 74 72 23 83 51 20 16 10 14 90 68 50 13 85 15 40 45 27 95 21 52 B B B B B S p eeder SU V S R & D P RO D ST 4 A SSY A U P/N : A ll A U Part s A ve S e q No O p era ti o n E l em ent P a rt # S eri a l # K ey po in t T1 T2 T3 T4 T5 -5 6 -7 0 - 4124067 - 4243819 # D IV/0 ! -5 9 -6 3 - 302326 - 4227684 # D IV/0 ! -r ea r - bum pe r # D IV/0 ! - 346026 - 4161329 - 4160866 - 4504379 - 4211525 - 302221 - 243121 -6 9 -4 3 -4 4 -2 6 -8 9 -3 4 -2 3 Pr o cess: T L Set u p Operations work standard sheet #D I V / 0 ! S t a nda rd Ti me (s ) 16 17 18- SA 1- 5 18 16 17 18 18- SA 1- 5 #D IV/0! St at io n C ycle t i m e REMARK S Q u a lit y S a fe ty 85 54 28 14 23 63 43 44 26 69 34 59 B 70 54 89 B B S p eeder SU V S R & D P RO D ST 5 A SSY A U P/N : A ll A U Part s Av e S e q No O p era ti o n E l em ent P a rt # S eri a l # K ey po in t T1 T2 T3 T4 T5 -7 4 -7 2 - 302226 - 303426 # D IV/0 ! -5 9 -6 7 -7 4 - 302326 - 362326 - 302226 # D IV/0 ! -8 1 -8 4 - 486526 - 4211549 # D IV/0 ! # D IV/0 ! S t a nda r d Ti m e (s ) Operations work standard sheet Pr o cess: T L Set u p 19 20 21 19 20 21 #D IV/0! St at io n C ycle t i m e REMARK S Q u a lit y S a fe ty 13 12 26 16 83 36 74 72 59 67 8 24 69 94 81 84 B B B B S p eeder SU V S R & D P ROD P r ocess: T L S e tup S T 1 A S S Y A U P / N: A ll A U P a r t s Av e Op No Opera t i o n Elem ent Pa r t # Qt y Seria l # Keypoint T1 T2 T3 T4 T5 201 73 2 24526 202 40 73 1 1 24526 383221 203 40 1 383221 204 36 2 302021 205 37 13 2 2 371021300421 206 16 10 89 1 4 4 4157223306201421525 Operations w o rk standard sheet S t a nda r d T i m e ( s ) 203 201 202 204 205 REM ARKS Qu a lity S a fety 206 82 36 37 13 16 10 89 73 54 40 41 71 85 38 2 34 65 R & D P RO D Pro cess: T L Set u p S T 2 A SSY A U P/N: A ll Do w Part s Ave Se q No Op erati on El em en t Pa r t # Qt y S eri al # K ey poi n t T1 T2 T3 T4 T5 207 71 2 303226 208- S A - 1 68 1 371026 208- S A - 2 43 44 1 1 4161329 4160866 208- S A - 3 23 45 94 1 2 2 243121 3000840 4542673 Ope r a t ions w o rk s t a nda rd s h e e t S t a nda r d T i me (s ) 208-S A -1 208-S A -2 207 REM ARK S Q ual i t y S a fe ty 208-S A -3 208 71 68 43 44 23 45 83 13 56 50 30 6494 65 91 51 17 R & D P RO D Pro cess: T L Set u p S T 3 A SSY A U P/N: A ll A U Part s Ave Se q No Op erati on El em en t Pa r t # Qt y S eri al # K ey poi n t T1 T2 T3 T4 T5 209 74 72 2 1 302226 303426 210 23 50 51 28 2 1 1 2 24312142050584528357 366021 211 50 49 1 4 42050584179833 Ope r a t ions w o rk s t a nda rd s h e e t S t a nda r d T i me (s ) 210 209 REM ARK S Q ual i t y S a fe ty 211 74 72 23 51 28 16 90 10 14 15 40 49 27 85 68 52 50 95 20 21 45 R & D P RO D ST 4 A S SY A U P/N : A ll A U Part s Av e S e q No O p era ti o n E l em en t Pa r t # Qt y S eri a l # K ey po int T1 T2 T3 T4 T5 28 14 54 218217 4124067 70 69 52 4243819 346026 4159553 S t a nda rd T i m e (s ) 216 Pro cess: T L Set u p Operations w o rk standard sheet 4 4 1 1 1 2 366021245821 21 6 217 REMARK S Q u a lit y S a fe ty 28 14 74 72 59 67 81 84 21 8 54 70 69 52 R & D P RO D ST 5 A SSY A U P/N: A ll A U Part s Ave Seq No Opera tio n E l ement Pa r t # Qt y Seria l # K ey po int T1 T2 T3 T4 T5 212 13 12 26 2 2 2 3004214558886 4504379 S t a nda r d T i me (s ) 213 214 16 83 1 1 2 1 1 Ope r a t ions w o rk s t a nda rd s h e e t Pro cess: T L Set u p 215 85 54 4211445412406741572234211385 36 8 24 302121366601 416221 2 2 21 2 21 3 214 REM ARK S Q ual i t y S a fe ty 13 12 26 16 83 36 70 59 63 69 43 8 24 44 26 21 5 85 54 89 34 23 Appendix C Data Collection forms 223 Team?Roles Station?Operators Material?Handler Quality?Controller 12345678910 allowances?Summary Personnel?needs 4 Basic?Fatigue 5 Variable?Fatigue Total?Allowance 9 R:?Rating?????????????????????OT:?Observed?time????????BT:?Basic?Time????????NT:?Normal?time????????????????? Special Product/Part: NT Department:? Plant/Machine: Manager Team?Leaders Tools?and?Guages: DWG?No: Operator?Assigned Operator?1 Operator?2 Station? Number Designated?Area?/Responsibility Operative: Total OT Ave? rage OT R Clock?Number: Studied?by: Date: Checked?by: Observed?time Quality: Station: Model: Operation: El. No. El?Description Study?No: Sheet?No: Time?on: Time?off: Elapsed?Time: BT Instruction for using Throughput Data Capture Sheet This table includes two sub-tables, one sub-table would be completed at the first station of each cell, and the other one is completed at the last station of each cell. The table at the first station records the entering/start time of each car, and the other one at the last station records the departure/end time of each car. Example: When a car numbered 7 is entering station 11, which is the first station of cell-3 at 10:05am, one the operators at station 3 should record car-7?s ID ?7? into the car number column, and then put the time 10:05am behind the car?s ID in the time column. After this step, car-11 will be processed in cell-3, when it completed by the operators of station 15, the last station of cell-3, at 10:07am, one the operators should put the car ID ?7? in the table, and record the completion time 10:07am. ? Instructions for using Time Study Sheet The times recorded in this sheet include two categories, one is valid time (VA), and the other is idle time (ID). This sheet will be used at every station to determine the utilization of each station as well as the % value and none value added time for each station. The VA time is the period while operators are working, and the ID time is the period when the operators are not working, for example, when the operators are waiting for the products come from upstream station, they are in idle period. Example: Car numbered ?9? is entering station ?3?, the operator puts ?9? in the Car Number column, then other operator begin working on car-9, the recorder also use stop watch to record the VA time. N.B. When an operator begins working on the first car, he will press the start button to start the clock, thereafter, the operator will press the lap button on the stop watch at the end of the assembly task and press the lap button again when he/she begin working on new subassembly. At the end of the shift the operator will press the lap button followed by stop button to finally stop time recording. Using the Recall button on the stop watch, the operator will record all the lap times. If no errors were made, then the odd lap times should represent the Value added times while the even lap times represent non-value added times. When the operators completed car-9 in station-3, and they spent 60 seconds in working, the record put 60 in the time column. Please make sure this value is VA time. After they completed car-9, they wait 30 seconds for the other car, so the recorder put 30 in the time column too. Please make sure it belongs to the ID time. Figure C.1: throughput Data capture sheet 227 Figure C.2: Established value added and Non- value added times 228 Table C.1: Value added/ Non- value added excel template 229 Appendix D Line Balancing solutions Using Excel spreadsheet template D.1 Cell 1 Line Line balancing solution 230 Ope # (T ek ) Work element Description Serial # Part # Quntity Tek(seconds) Must be Preceded by Operation # Station Station Cycle Time (s) 1 place the two parts side by side 244526 73 2 - 2 Assembly part as shown in in 2 4124067 54 2 1 3 Assembly part as shown in in 3 383221 40 1 4506961 41 1 4 Assembly part as shown in in 4 303226 71 2 1 5 Flip Assembly and attach parts as shown in 4211445 85 1 447721 38 1 6 Assembly part as shown in in 6 300401 2 1 302221 34 1 244526 73 1 7 Assembly part as shown in in 7 4157223 16 2 4211445 85 1 8 Assembly part as shown in in 8 4211529 82 2 4 9 Assembly part as shown in in 9 4205058 83 1 6 10 Assembly part as shown in in 10 300421 51 2 362226 13 2 4205058 17 2 11 Assembly part as shown in in 11 4210631 91 1 307026 30 2 302321 64 2 243126 65 2 12 Assembly part as shown in in 12 4528357 51 1 300421 13 1 621521 17 1 13 Assembly part as shown in in 13 4157223 16 1 4211060 90 1 14 Assembly part as shown in in 14 306201 10 4 245821 14 2 362221 15 3 SA-1-1 Prep Subassembly as shown in SA-1-1 383221 40 1 306201 10 1 4121934 27 1 4211445 85 1 SA-1-2 Prep Subassembly as shown in SA-1-2 371026 68 1 3000840 52 2 SA-1-3 Prep Subassembly as shown in SA-1-3 4210631 95 2 4251162 20 2 SA-1-4 Prep Subassembly as shown in SA-1-4 243221 21 2 3000840 45 2 15 Attach bumper to 14 frt-bumper 1 13,14 16 Assembly parts as shown 4124067 56 2 4243819 70 1 17 Assembly parts as shown 302326 59 2 4227684 63 2 18-SA Prepare Sub Assembly 346026 69 1 4161329 43 1 4160866 44 1 4504379 26 4 4211527 89 4 302221 34 1 243121 23 1 18 Attach rear bumper ar-bumper SA 1 7 19 Assembly part as shown in 19 302226 74 1 303426 72 2 20 Assembly part as shown in 20 302326 59 2 362326 67 2 302226 74 2 21 Assembly part as shown in 21 486526 81 3 4211549 84 1 Total 96 6,7,8,10 5 7,10,18 19 4 12,13 - 13,14,15 SA-1-2 SA-1-1,SA-1-2 4,11 3 2,3,4 - SA-1-1 4,6 2 3 3,4,5 3,6 Cell?1:?Speeder?Precedance?Table 1 1 4 2,3,4 Ope # (T ek ) Work element Description Part Number Serial Number Quantity T ek (seconds) Must be Preceded by Station Station Cycle Time (s) 201 Place?the?two?parts?side?by?side 73 24526 2 - 40 24526 1 73 383221 1 203 Assembly part as shown in in 203 40 383221 1 201, 202 204 Assembly part as shown in in 204 36 303226 2 201, 202 37 371021 2 13 300421 2 16 4157223 1 10 306201 4 89 421525 4 207 Assembly part as shown in in 207 71 303226 2 202, 203, 205 8-SA-1 Assembly part as shown in in 208-SA-1 68 371026 1 - 43 4161329 1 44 4160866 1 23 243121 1 93 3000841 2 94 4542673 2 208 Assembly part as shown in in 208 Rear bumper SA 1 206 74 302226 2 72 303426 1 23 243121 2 50 4205058 1 51 4528357 1 28 366021 2 50 4205058 1 49 4179833 4 85 4211445 2 54 4124067 2 28 366021 4 14 245821 4 217 Assembly part as shown in in 217 54 4124067 4 216 13 300421 2 12 4558886 2 26 4504379 2 16 4157223 1 83 4211385 1 36 302121 2 8 366601 1 24 416221 1 70 4243819 1 69 346026 1 52 4159553 2 Total 77 Cell?1:?SUV?Prcecedence?table 1 202 Assembly part as shown in in 202 201 205 Assembly part as shown in in 205 201, 202, 203, 204 206 Assembly part as shown in in 206 202 ,203, 204 2 8-SA-2 Assembly part as shown in in 208-SA-2 208-SA-1 8-SA-3 Assembly part as shown in in 208-SA-3 208-SA-2 209 Assembly part as shown in in 209 205, 206, 208 3 210 Assembly part as shown in in 210 206, 207 211 Assembly part as shown in in 211 207 4 216 Assembly part as shown in in 216 201, 202 215 Assembly part as shown in in 215 204,207 212 Assembly part as shown in in 212 202, 203, 207 5 213 Assembly part as shown in in 213 202, 203, 204 214 Assembly part as shown in in 214 212, 213 218 Assembly part as shown in in 218 207, 209, 210 Figure D.1: Cell 1 Precedence diagrams 233 Tek?(sec) OP 1234567 8910112131415161718192021RPW 1 3.90 1 0 111111 11111111111111 317 2 6.52 4 0000111 1 1 111 111111 1 1 1 295 3 8.28 3 0000011 0 1 0 11111111 1 0 1 245 4 9.06 2 0000011 01001 0 0 1111111 220 5 8.50 5 0000000 00110101110111 189 6 12.89 6 0000001 0 1 001 00001111 1 133 7 19.60 11 0000000 000001 0 1 100000 107 8 8.81 13 0000000 000000011000 0 0 87 9 65.86 15 0000000 00000000100000 78 10 15.80 10 0000000 0000000001011 1 74 11 10.09 7 0000000 00000000001101 71 12 36.92 18 0000000 00000000000000 37 13 19.16 14 0000000 00000000000001 32 14 5.73 8 0000000 00000000000101 30 15 11.56 19 0000000 0000000000 0001 24 16 11.06 12 0000000 00000000010000 23 17 22.42 20 0000000 00000000000000 22 18 12.65 21 0000000 0000000000 0 000 13 19 12.40 16 0000000 00000000000000 12 20 11.76 17 0000000 00000000000000 12 21 3.61 9 0000000 00000000000000 4 316.58 70 63.3 65 ?5 Station?(j) Op??(k) RPW T ek 1 1 316.6 3.90 4 66 1 4 295.3 6.52 10.5 59 1 3 244.7 8.28 18.8 51 4 295.3 6.52 25.3 45 5 189.4 8.50 33.8 36 6 133 12.89 46.7 23 11 106.7 19.60 66.3 4 2 7 71.22 10.09 10.1 60 2 8 29.94 5.73 15.8 54 2 9 3.61 3.61 19.4 51 2 10 74.19 15.80 35.2 35 2 13 87.07 8.81 44 26 2 14 31.81 19.16 63.2 7 3 15 78.26 65.86 65.9 4 4 12 22.82 11.06 11.1 59 4 18 36.92 36.92 48 22 4 19 24.21 11.56 59.5 10 5 16 12.4 12.40 12.4 58 5 17 11.76 11.76 24.2 46 5 20 22.42 22.42 46.6 23 5 21 12.65 12.65 59.2 11 314 E b = 0.947 Where?E B?i s?the?balance?efficiency?of?the?line Ed= 0.053 Where?Ed?is?the?balance?delay T WC 3 4,11 2,3,4 3,6 7 6,7,8,10 12,13 7,10,18 13,14,15 19 1 ? 1 1 4 RPW?Values?sorted?in?Descending?Order Immediate?predecessor Cumulative time?(sec) Unassigned time?(sec) Remarks Assigment?of?Operations?to?Work?Stations 2,3,4 4,6 4 6 13,14 3,4,5 Takt?time?(sec) Ideal?Cycle?(sec) Set?Cycletime?(sec) Allowable?variance Takt?Time 70 Cycle?time 65 Ti?(s) OP 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 RPW 1 5.57 201 0 11111111111111111266 2 7.55 202 001111111111111111260 3 6.62 203 000011111111110001 188 4 6.64 204 000011111110 111001 180 5 10.99 205 0000001 0 11110 11001 136 6 8.62 207 0000000001 0 1 0 11001 100 7 30.34 206 000000011100000001 99 8 28.04 216 00000000000000001 0 41 9 25.48 212 00000000000001 000039 10 20.83 210 000000000000000001 35 11 24.87 208 000000001 00000000033 12 7.22 213 00000000000001 000021 13 17.34 215 00000000000000000017 14 16.73 211 00000000000000000017 15 14.27 218 00000000000000000014 16 13.60 214 00000000000000000014 17 12.75 217 00000000000000000013 18 8.20 209 0000000000000000008 T wc 266 TAKT?TIME 70 This?time?is?dependent?on?Customer?Demand? Te(ideal) 53.1 This?is?the?time?time?at?each?station?needed?to?meet?perfect?balance Cycle?time 60 This?is?time? Variance ?10 Station?(j) Op??(i) RPW T ij 1 201 266 5.57 4 56 1 202 260 7.55 11.6 48 1 203 188 6.62 18.2 42 1 204 180 6.64 24.8 35 1 205 136 11 35.8 24 1 207 100 8.62 44.4 16 1 213 20.8 7.22 51.6 8 2 206 98.5 30.3 30.3 30 2 216 40.8 28 58.4 2 3 212 39.1 25.5 25.5 35 210 20.8 46.3 14 215 17.3 63.7 ?4 4 208 33.1 24.9 24.9 35 4 211 16.7 16.7 41.6 18 4 218 14.3 14.3 55.9 4 5 214 13.6 13.6 13.6 46 5 217 12.8 12.8 26.4 34 5 209 8.2 8.2 34.6 25 266 E b = 0.835 Where?E B?i s?the?balance?efficiency?of?the?line Ed= 0.165 Where?Ed?is?the?balance?delay Remarks ? 216 201,?202 201,?202 201,?202,?203,?204 202?,203,?204 201,?202 206,?207 Assigment?of?Operations?to?Work?Stations 202,?203,?207 202,?203,?204 201 202,?203,?205 Immediate?predecessor Cumulative Unassigned 206 207 207,?209,?210 205,?206,?208 212,?213 204,207 Sort?table?in??desceding?order?of?RPW No (T ek ) Work element Description Serial Number Part Number Quantity Tek(sec) Must be Preceded by Station Station Cycle Time (s) 4124096 61 2 4210631 91 2 4211469 88 2 4211525 89 4 4124096 61 2 4528357 51 2 4210631 91 2 4211469 88 2 4211525 89 4 4179833 49 2 300421 13 2 4558886 12 2 3000841 93 2 366021 28 4 4542673 94 2 4558886 12 2 26 Assembly part as shown in in 26 303421 39 2 25 302121 35 2 4160886 44 1 4161329 43 1 4179833 49 2 4504379 26 4 4540386 92 2 300901 3 1 366601 8 1 29-SA-2 Assembly part as shown in 29-SA-2 4277932 55 2 29-SA-1 29-SA-3 Attach wind screen 4129534 47 1 29-SA-2 29-SA-4 Assembly screen reinforcements as shown in 29-SA-4 300501 1 6 29-SA-3 29-SA-5 Assmbly as shown in 29-SA-5 654101 11 2 29-SA-4 29-SA-6 Assmbly as shown in 29-SA-6 371026 68 1 29-SA-5 29-SA-7 Assmbly as shown in 29-SA-7 663626 66 1 29-SA-6 4504379 26 2 29-SA-3 245821 14 2 29-SA-5 393721 33 2 29-SA-2 393826 60 2 29-SA-2 29-SA-10 Assmbly as shown in 29-SA-10 416221 24 1 29-SA-9 29 Attach Rear door Assy 1 22,23, 27 371021 37 1 609121 18 2 242026 57 2 302326 59 1 4504382 77 2 243126 65 1 3302301 7 2 4211445 85 1 4124067 54 1 Tot number of parts 80 12,16,17 Cell 2-Precedance Table 31 Assy as shown in 31 30 10 59 32 Assy as shown in 32 31 33 Assy as shown in 33 961 29-SA-8 Assmbly as shown in 29-SA-8 29-SA-9 Assmbly as shown in 29-SA-9 30 Assy as shown in 30 26,28 859 29-SA-1 Assembly parts as shown in 29-SA-1 - 27 Assembly as shown in 27 26 28 Assembly parts as shown in 28 24,27 791 25 Assembly part as shown in 25 20 24 Assembly part as shown in in 24 19,20 67123 Assemby part as shown in 23 9, 20 22 Assembly part as shown in 22 19 Ope # Work element Description Part Number Serial number Quantity Tek(second s) Must be Preceded by Station Station Cycle Time (s) 57 242026 2 68 371026 2 3 300901 1 1 300501 3 90 4211060 1 16 4157223 2 56 362226 2 2 300401 1 1 300501 3 27 4211934 2 9 4504379 8 26 4244362 2 35 302121 2 54 4124067 1 13 300421 2 47 4129534 1 65 243126 1 59 302126 1 75 302326 2 74 302226 2 51 4528357 2 55 4277932 2 21 243221 2 15 366021 2 28 362221 2 21 302221 2 32 4515365 2 64 307026 2 89 4211525 2 65 243126 1 16 4157223 1 89 4211525 2 79 4153044 1 91 4210631 2 59 362326 2 88 4211469 2 67 302326 2 29 302321 2 65 243126 1 22 307021 2 30 408521 2 Tot number of parts 79 10 231 Assembly part 228, 230 230 Assembly part 212, 214 9 229 Assembly part 228 228 Assembly part 211, 214 8 226 Assembly parts 225 227 Assembly part 223, 224, 226 225 Assembly parts 224 7 224 Assembly part 209, 221, 222 223 Assembly part 222 222 Assemby part a 218, 219 6 220 Assemby part a 218, 219 221 Assembly part 218, 219 219 Assemby part a 209, 210 No Work element Description Serial Number Part Number Quntity T ek (seconds) Must be Preceded by Station Station Cycle Time (s) 4211525 89 2 408521 29 2 243126 65 1 371026 68 1 4571181 42 1 4124067 54 2 4157223 16 1 4249112 5 2 366021 28 4 408521 29 2 4244362 46 2 4542673 94 1 346026 69 2 362326 67 1 4159553 52 2 30202 36 2 4210631 91 1 4155708 53 1 4153044 54 1 4124067 79 1 303301 7 2 4225201 62 2 4515365 32 2 3710121 37 1 4226876 31 1 4278359 78 1 302126 75 1 4560179 25 1 302126 36 1 371021 37 1 4226876 31 1 4278359 78 1 302126 75 1 4560179 25 1 302126 36 1 42 Attach doors 1 34,36,39 307021 22 2 243121 23 2 302026 76 1 4504369 46 4 4160866 44 1 4161329 43 1 4518992 4 2 4517925 58 2 4520782 19 4 302326 59 1 4211395 86 1 302021 36 1 45 Secure hood as shown in 45 hood 1 40 4211525 89 2 4542700 48 1 303226 71 1 4244362 9 2 408501 6 2 3000840 45 2 4299119 87 4 4550937 80 4 Tot Part count 92 Cell 3 Precedance Table 47 Assy parts as shown in 47 40,46 15 48 Assy wheels as shown in 48 47 43 46 Assy part as shown in 46 31,44 - 44 Assy parts as shown in 44 33,35,36,37,40,43 45-SA 41-SA-1-5 Assy door Sub Assy 34,36 42-SA-1-5 Assy door Sub Assy 34,36 11 39 40 Assy part as shown in 40 37 16,33 17,33,34 34 35 36 Assy parts as shown in 43 Assy as shown in 34 Assy as shown in 35 Assemby part as shown in 36 Assy Hood as shown in 45 37 Assemby part as shown in 37 38 Assemby part as shown in 38 Assemby part as shown in 39 17,31,35 17,35,36 36 35 12 13 38,40 14 Appendix E Assessing the e ectiveness of hands-on labs through student surveys 239 E.1 Student perceptions on introductory manufacturing lab in enhancing stu- dent learning and interest 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 1/7 English Default Question Block Q1. Informed Consent IntroductionThis Survey seeks students perspective on what elements/components of the student's hands-on manufacturing lab enhanced their understanding of manufacturing related concepts taught in class. You have been selectedbecause you participated in either manufacturing systems labs (INSY 3800) or Lean Production (INSY 3800). Your feedback is important for the development of future hands-on manufacturing labs designed with the purpose ofbridging the competency gaps of graduating manufacturing students. This study is being conducted by Yamkelani Moyo, PhD candidate under the direction of Dr. Richard Sesek, Assistant Professor in the Industrial and Systems Engineering Department.We hope to use the information you provide as input for developing a taxonomy for manufacturing education. Procedures: You will be asked to answer a series of questions based on your own experience as student that participated in hands-onmanufacturing related laboratory activities . The questions asked will not not take more than 20 minutes to complete. Questions are designed to determine what aspects of the lab you found helpful in enhancing your learning of the concepts. Inaddition, you will be asked to suggest recommendations on how to improve manufacturing hands-on learning activities associated with manufacturing courses. These hands on learning activities are intended to bridge the gap betweenmanufacturing industry desired skill-sets and Manufacturing education expected deliverables. This questionnaire will be conducted with an on-line Quartics-online survey. Risks/DiscomfortsRisks are minimal for involvement in this study. This is an anonymous survey. Benefits There are no direct benefits for participants. However, it is hoped that through your participation, researchers/educators willgain valuable knowledge on how to streamline manufacturing curriculum to fit the dynamic nature of today's manufacturing industry. The results of this survey together with perspectives of educators will provide valuable information required todevelop an effective taxonomy for manufacturing education. This taxonomy could thus serve as basis for developing consensus guidelines for an effective manufacturing curriculum required to revamp the US manufacturingindustry. Confidentiality All data obtained from participants will be kept confidential and will only be reported in an aggregate format (by reporting onlycombined results and never reporting individual ones). All questionnaires will be concealed, and no one other than then primary investigator and assistant researches listed below will have access to them. The data collected will be stored in theHIPPA-compliant, Qualtrics-secure database until it has been deleted by the primary investigator. Compensation There is no direct compensation, rather than the satisfaction one may get for making a contribution intended to revamp themanufacturing education and thus indirectly contribute towards revitalizing the manufacturing sector. Participation Participation in this research study is completely voluntary. You have the right to withdraw at anytime or refuse to participateentirely. If you desire to withdraw, please close your Internet browser and notify the principal investigator at this email: yzm00055@auburn.edu Questions about the Research If you have questions regarding this study, you may contact (Yamkelani Moyo,513-886-0160) Questions about your Rights as Research Participants: If you have questions you do not feel comfortable asking the researcher, you may contact Auburn Universities University Officeof Human Subjects Research or Institutional Review by phone (334)-844-5966 or email at hsubjec@auburn.edu or IRBChiar@auburn.edu. s 240 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 2/7 Yes No Above 19 years of age Below 19 years of age Freshman Sophomore Junior Senior I Hope to Find a job in manufacturing I would rather work in different field other than manufacturing I prefer a job in service industry Q2. I have read, understood, and printed a copy of the above consent form and desire on my own free will toparticipate in this study. Q3. How old are you Q4. What level are you right now in college? Q5. With respect to Career path, what aspiration do have with respect to a career in Manufacturing ? Q6. If there is Particular career that you aspire to get into, What would that be? Q7. Indicate if you have had participated internship while been a student at Auburn. 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 3/7 Yes No Q8. What type of industry(s) have you Interned in? Q9. Employability skills are an important attribute that many manufacturing employers are interested in. Of the followingidentified competencies, how important/relevant do you consider these competencies are in preparing you for your future career. Not at all Important Very Unimportant Neither Important nor Unimportant Very Important Extremely Important Use of Computer aided software CAD/CAM Knowledge of Ergonomic and Safety Lean Manufacturing knowledge Operations research and Optimization MRP/Inventory Control knowledge of manufacturing processes Statistical process control (SPC) Automation (knowledge of PLC and Robotics) Six Sigma knowledge Business knowledge skills Q10. Employability skills are an important attribute that many manufacturing employers are interested in. Of the followingidentified competencies, how important/relevant do you consider these competencies are in preparing you for your future career. Not at all Important Very Unimportant Neither Important nor Unimportant Very Important Extremely Important Use of Computer aided software CAD/CAM Knowledge of Ergonomic and Safety Lean Manufacturing knowledge Operations research and Optimization MRP/Inventory Control knowledge of manufacturing processes 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 4/7 Statistical process control (SPC) Automation (knowledge of PLC and Robotics) Six Sigma knowledge Business knowledge skills Q11. Please indicate your agreement/disagreement with the following statement. Participating in the below listed labsenhanced my understanding of taught concept than a classroom lecture alone would have. Strongly Agree Agree Somewhat Agree Neither Agree nor Disagree Somewhat Disagree Computer Number Control m(CNC) Programmable Logic Controller (PLCs) Manufacturing Planing and Control (Lego Lab) Time Study Lab Understanding of manufacturing terms e.g. Bottleneck process, throughput time, line balancing Q12. As part of lab component of INSY 3800, you were exposed to a number practical experience. Please indicate if you hadany previous practical experience either during internship or any other you may have had associated with a different course. No prior experience Somewhat knowledgeable Aware of Agree Strongly Agree Stop watch time study Robotics programming and automation Line Balancing Programmable logic controls (PLC) Computer Numerical Control (CNC) Predetermined time and motion studies Q13. This question seeks to determine the usefulness of labs in enhancing learning when compared to classroom learningalone. Using the scale shown below: 1: Least confident in concept learned 5: Most confident in concept learned In your opinion how did participating in Lab improve your confidence in the concepts taught. Contrast this with the confidenceyou would have in concepts taught if all concepts are taught in classroom lecture alone without laboratory reinforcement. Participating in Labs in addition to lecture Participating in Lecture alone Least confident 2 3 4 Very confident Least confident 2 3 4 Most confident 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 5/7 CNC Lab (Use of G codes Computer Numerical control) PLC lab (Inputs,Outputs,ladder logic, Timers, Counter etc) Stop watch time Study Line Balancing (Throughput time, bottleneck, Value added/None value added) Q14. Please indicate your agreement/disagreement with the following statement Strongly Disagree Disagree Somewhat Disagree Neither Agree nor Disagree Somewhat Agree Agree Strongly Agree I found the Lego lab to realistic representation of real life assembly plant I found the Lego lab a useful for learning how to work effectively in teams The Lego enhanced my my understanding some the theoretical concepts presented in the lecture Students are better to learn and remember theories, ideas and concepts when applied to real situations and when they have concrete experience of the way industry works Working with the ACE robotic simulator increased my confidence in the knowledge of how the real system works Participating in the Lego lab enabled me see how other topics (e.g. Ergonomics, human factors, Operations research can be integrated) to improves system performance Q15. If there is any way you feel the following labs can be improved, please indicate so in the space provided: -Computer Numerical Control: Q16. If there is a any way you feel the following lab can be improved please indicate so, in the space provided: -Programmable logic controllers lab: 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 6/7 I prefer structured labs and Learn more this way I prefer open ended labs and Learn more this way A combination structured labs and open labs is good for learning Q17. If there is a any way you feel the following lab can be improved pleased indicate so, in the space provided: -Lego Manufacturing Systems lab (Lego Labs): Q18. There are two kinds of labs, structured and unstructured. Structured involve following elaborate lab procedures ( e.g.Line balancing) while unstructured tend to be open ended (PLC project). Indicate your preference between the two kinds of labs, please select the appropriate from the choices below: Q19. In comparison to lecture only courses, do feel labs enhance your learning ability and interest in particular subject strongly agree Agree Neither Agree nor Disagree Disagree Strongly Disagree I pay more attention to labs than in lectures I tend to learn more in labs than lectures I learn better when I am part of a team Q20. This Question relates to the use of simulation software and Emulation Software: Strongly agree Agree Neither Agree nor Disagree Disagree Strongly Disagree Using Robotic Emulation software enhanced my appreciation of robotic Programming: Using CNC software increased my Understanding of Computer Numerical control programming 7/3/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=33uPqk 7/7 2 people between 2 and 4 people between 4 and 6 people between 6 and 10 people Q21. In the labs you were often required to work in groups, what would say would be an effective group size? Q22. If there is a any way you feel the following lab can be improved please indicate so in the space provided: -Programmable logic controllers (PLC): E.2 Student perceptions on introductory manufacturing lab in enhancing stu- dent learning and interest 9/10/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=2Kt3SD 1/5 Freshman Sophomore Junior Senior Graduate Female Male 18-20 years 20-25 years 26-30 years 30-35 years > 35 years I am presently working in manufacturing related job I Hope to Find a job in manufacturing I would rather work in different field other than manufacturing I prefer a job in service industry Default Question Block Q1. The Following Questions will give you an opportunity to contribute to the Development of the Auburn UniversityManufacturing Systems Interdisciplinary laboratory. Your honest and genuine contribution to this cause is appreciated. We hope you participate in this endeavor. You may Choose not to participate if so wish. Q2. What level are you right now in college? Q3. what is your Gender Q4. Select the appropriate age group in which you belong: Q5. With respect to Career path, what aspiration do have with respect to a career in Manufacturing ? Q6. If there is Particular career that you aspire to get into, What would that be? 247 9/10/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=2Kt3SD 2/5 Value stream Mapping Single Minute exchange of Dies (SMED) Cell Design and Manning strategies Outreach student Q7. Select the Individual Group Labs that You participated in: Q8. Indicate to what extent your agree with the following statement:I feel that participating in the hands-on individual Lean Production Labs helped my understanding of the following Lean concepts better than traditional classroom lecture alone would. Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree Understand the drawback of MRP Push systems Pull single Piece flow Heijunka (Load leveling) 2 card Kanban production flow control Use of Super Markets Single minute exchange of dies Kaizen (Continuous improvement) Q9. From experience with the Lean Production Course you just participated in. Provide a perspective as to how necessary it isto include the following hands-on activities to supplement classroom lectures for deeper learning and understanding to occur. Not at all Important Very Unimportant Somewhat Unimportant Neither Important nor Unimportant Somewhat Important Very Important Extremely Important Push(MRP, Production run 1) Vs Pull (Kanban-Production run2, and 3) Single Piece Flow Heijunka (Load leveling) 2 card Kanban production control Use of Super Markets Single Minute exchange of Dies(SMED) Kaizen (Continuous improvement) 9/10/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=2Kt3SD 3/5 Q10.From following list of hands-on activities associated with the Lean Production course, rank each according to which offered you the best learning experience with regards to enhancing your understanding of Lean manufacturing concepts, using thescale shown below 1-Least learning experience 8- Best learning experience : Push(MRP, Production run 1) Vs Pull (Kanban-Production run2, and 3) Single Piece Flow Heijunka (Load leveling) 2 card Kanban production control Use of Super Markets Single Minute exchange of Dies(SMED) Kaizen (Continuous improvement) Value stream Mapping Q11. Team Work Using the Scale provided indicate to what extent you agree with the following statement regarding the lab: Strongly Disagree Disagree Somewhat Disagree Neither Agree nor Disagree Somewhat Agree Agree Strongly Agree I feel working in team enhances my learning experience My participation as a team member was a good experience on how to work in a team I was generally Happy with level of participation and contribution of my team members Report writing should be done in smaller groups to encourage participation Use of peer evaluation is necessary when working in larger groups to encourage participation. Q12. This Question applies only to out reach students: Using the Scale provide indicate if you agree with the following statements: Strongly Disagree Disagree Somewhat Disagree Neither Agree nor Disagree Somewhat Agree Agree Strongly Agree Watching the videos of the Live Production runs provide a good learning experience: I was able to follow what was happening from the Video presentation of the production runs 9/10/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=2Kt3SD 4/5 Q13. Course Composition Grade Distribution: applies only to Graduate students Indicate what you feel would be fair distribution of grades associated with each elements of this Class: Q14. What suggestions do have to improve team performance and cooperation during Lean production hands-on activities. Q15. With respect to hands-on lean production activities and Classroom lecture indicate to what extent you agree with followingstatement: Strongly Agree Agree Somewhat Agree Neither Agree nor Disagree Somewhat Disagree Disagree Strongly Disagree I find that lean production hands-on activities enhances my interest in the Lean Production subject Matter Participating in hands-on Lean manufacturing activities helped me relate theory to practice. I learn better when I am part of a team Q16. If there is a any way you feel the following lab can be improved please indicate so, in the space provided: In Class Quizzes (currently 54%) 0 Hands-on Lab activities (currently 17%) 0 Kaizen Paper (currently 17%) 0 Book Write up (Currently 12%) 0 Total 0 9/10/13 Qualtrics Survey Software https://auburn.qualtrics.com/ControlPanel/Ajax.php?action=GetSurveyPrintPreview&T=2Kt3SD 5/5 2 people between 2 and 4 people between 4 and 6 people between 6 and 10 people Q17. In the labs you were often required to work in groups, what would say would be an effective group size? Appendix F Assessment through written test F.1 Midterm exam INSY 5800/6800/6806 ? Mid Term Exam??? Page?2?of?4? ? 8) From the example above, how many jobs are required given a target of 70% Utilization? (4 points) 40+15+20+15+25=115 115/.7/55=2.98 or 3 jobs 9) How do you indicate an area of focus for improvement on a future state value stream map? (2 points) Kaizen Burst 10) If batching in one cell feeds the next cells continuous flow, how would you create a pull between the two cells? (2 points) Supermarket or Heijunka Box 11) Select from the following choices, a statement that is not a true indicator of the purpose for creating a current value stream map. (1 point) a) Value stream map is a tool that helps you to understand the flow of material and information as the product makes its way through the value stream. b) Value stream is a tool for optimizing flow through the value stream c) In any value stream map information flows should be shown to indicate how communication between a Customer and supplier takes place. d) A Value stream can be extended to cover external operations of suppliers in any value stream. 12) Name any three pieces of Information that are critical for developing a Value Stream map (3 points) a) _____________________________________ b) _____________________________________ c) _____________________________________ 13) Which of the following inventory items in a value stream influences the overall lead time? (1 point) a) Work In Process (WIP) b) Raw material stock (Stock at Hand) 252 INSY 5800/6800/6806 ? Mid Term Exam??? Page?3?of?4? ? 14) What is the best way of drawing an initial value stream map? (1 point) a) Use of graphical software such as Power point or Micro-Soft Visio b) Pencil and Paper 15) Indicate if the following tasks are Value Added or Non Value Added tasks. Use ?VA? for value added and ?NVA? for non-value added. (3 points) a) An Operator at Station 1 in a manufacturing cell picks a part from a bin and assembles to a sub assembly:_____________ b) An operator takes one minute to inspect the part before passing it on to a downstream station:________________ c) An operator at Station 2, on realizing that an error in assembly has occurred, takes one minute to correct the problem before passing it to a downstream station:_____________ 16) State whether the following statement is true or false as they relate to value stream mapping. T/F. (5 points) a) Value stream Mapping should only be limited to the operations of one department in a manufacturing plant:____________ b) Information flows, showing the communication between functional areas of a plant are an integral part of value stream mapping:___________ c) Increasing the WIP between Processes and Department results in the reduction of total Lead time:______________ d) The Change Over time should be indicated on the value stream map:___________ e) The Cycle time of a particular process in the manufacturing system can be larger than the Takt time if the Customer demand is to be met:________________ 17) What is your understanding of Production Lead time as it relates to Value stream mapping: (2 points) 18) The Figure below shows a Value stream map for a 5 station manufacturing Cell used to manufacture a small toy car. Given the following information about the Manufacturing system: Daily demand for the toy car is 460 units/day and available working time per day is 27 600 seconds/day. Using this information it can be determined that the takt time is 60 seconds. Using this information and WIP indicated below the triangle in Figure 1: INSY 5800/6800/6806 ? Mid Term Exam??? Page?4?of?4? ? i) Fill out the cycle times and Inventory Lead times in the boxes provided on the time line in Figure 1. (9 points) ii) Determine the Production lead time and Processing time by filling in the boxes in Figure 1. (4 points) C/T:?50s? C/0?:? AVAL:100%? Scrap:0? Batch:1? C/T:?40s? C/0?:? AVAL:90?%? Scrap:8.3%? Batch:5? C/T:?62s C/0?2?0 AVAL:80 % Scrap:0 Batch:1 C/T:?75s C/0?2?360 AVAL:90% scrap:4.2% Batch:1 I? RM?:39? RM:118?? RM:77 RM:152 ST?4 ? 1 ST?1? ? 1? ST?2? ? 1? ST?3 ? 1 ST?5 ? 1 ?Production?Control? MRP Customer???? ??? 2? I 5? I 8? I? 7? I? Supplier????XYZ???? ? ? ? ? ? ? ? ? ? 90?day? Production? Lead?time:? Processing? time:? C/T:?65s C/0?:?0 AVAL:95 % Scrap:?5% Batch:1 RM:118 Figure 1 Value stream map Key:?C/T=Cycle?time,?C/O=?change?Over,?RM=Raw?Material?inventory? WIP F.2 Quiz 3 INSY 5800/6800 Quiz #3 ? Page?2?of?4? ? 8. If the process is changed to bring the process under the threshold, what factor of the Risk Priority Number does not cannot change? (1 point) 9. What are the two pillars of the Toyota Production System? (2 points) 10. What method is used to accomplish delivery of complex components to a mixed model Final Assembly line? (2 points) 11. What do you understand by SMED? (2 points) 12. Name two categories that are the basis for SMED (2 points): ? ________________________________ ? ________________________________ Select the best answer: 13. SMED is important to Companies because it (1 point) a. Helps reduces defects b. Helps companies meet customer needs with less waste by allowing smaller lot production c. It encourages team work among workers 14. Based on what you know on SMED, list three ways in which SMED may benefit a company. (3 points): a. ____________________________________________________________________ b. ____________________________________________________________________ c. ____________________________________________________________________ 15. Which of the following is the first stage of implementing SMED? (1 point) a. Converting internal setup activities into External setup activities b. Streamlining all aspects of setup operation c. Separating external and external setup activities 255 INSY 5800/6800 Quiz #3 ? Page?3?of?4? ? 16. Answer True or False to the following statements. (4points) a. A checklist can be used to determine the tools required for carrying a SMED operation. T / F b. Function checks to determine if parts are in perfect working condition are an integral part of SMED. T / F c. A rabbit chase is a Manning strategy used in U cells T / F d. The time taken to produce a new part and checking it after changeover process s is considered as part of the total changeover time T / F. 17. Give three advantages of Operating a U shaped Cell as opposed to the traditional straight line (3 points). a. _____________________________________________________________________ b. _____________________________________________________________________ c. _____________________________________________________________________ 18. Suppose that you have U shaped Cell with six stations as shown below. At peak demand, the Cell is manned with 6 workers. Suppose that demand for the product dropped to a 1/3. State the number of workers you would require to run the Cell and the manning strategy you would employ. Illustrate the movement of each worker in cell in using arrows in the figure below (3 points). a. Number of Workers required______________________ 2? 5? 3? 4? 1? 6? Worker? ? 19. T Befo 1. 2. 3. 4. The circle in wh estim Com Acti 1 2 3 4 he Figure b re?Improvem After?the?ma from?machin A?crane?hoist and?carries?it A?crane?then area?and?tran The?new?die? for?productio changeover s indicate t at category ate of the t plete the ta vity # In elow illustra ent:? chine?is?stopp e?onto?a?mov ?the?old?die?f ?to?the?storag ?hoist?the?new sports?it?to?t is?mounted?a n? process illu he activities each of the ime taken fo ble below: Before Imp ternal/Exter IN tes a SMED ed,?old?die?is ing?bolster.? rom?the?mov e?area?? ?die?from??th he?moving?bo nd?machine?s strated abov done to acc activities fo r an activity rovement nal T 60 120 120 60 SY 5800 P improveme ?extracted? ing?bolster? e?storage? lster? tarted?again e shows th omplish the r the before in the ?Time ime taken (sec) /6800 Q age?4?of?4? nt at Y indu ? After?Im 1. Befo the?n 2. The? stop bolst and?t 3. Next bolst start 4. After hoist e before and changeove /after improv taken colu Activi 1 2 3 4 uiz #3 stries. ( 5 p provement: re?the?machi ew?die?and?p machine?finis ped.?The?old? er.?The?crane then?set?it?do ?the?crane?ho er.?The?new? ed?up.? ?the?machine ed?the?old?di after impro r process. vement scen mn?. A ty # Int oints) ne?was?shut?d laced?it?next hed?the?prev die?was?remo ?hoisted?the wn?near?the? isted?the?new die?was?mou ?began?the?n e?and?return vement sce Indicate with narios fall in fter Improv ernal/Extern own,?the?cra ?to?the?mach ious?operatio ved?onto?the e?old?die?from machine.? ?die?onto?th nted?and?the ew?operatio ed?it?to?the?st narios. The (I: internal, to. Also, giv ement al Ti ne?brought? ine.? n?and?was? ?moving? ?the?bolster,? e?moving? ?machine? n,?the?crane? orage?area. numbered E: external e a logical me taken (sec) ? ) Appendix G Computer simulation results for Tiger Motors shop oor Tiger?Motors?push?MRP?productions?strategy? Change?over?time? 60sec Batch?size? SP? 3 SUV? 2 Supermarket?size? N/A ? ? ? ? ? ?? 258 Tiger?Motors?push?MRP?productions?strategy? Change?Over?time? 300 Batch?size? SP? 3 SUV? 2 Supermarket?size? N/A ? ? ? ? ? ?? Tiger?Motors?push?MRP?productions?strategy? Change?over?time? 300 sec Batch?size? SP? 9 SUV 6 Supermarket?size? N/A ? ? ? ?? Tiger?Motors?Lean?Pull?production?strategy? Change?over?time? 60sec Batch?size? SP? 3 SUV? 2 Supermarket?size? 8 ? ? ? ?? Tiger?Motors?Lean?Pull?production?strategy? Change?over?time? 300sec Batch?size? SP? N/A? SUV? Supermarket?size? 8 ? ? ?