Supply Chain Talent Management: Decision-Making in Industry 4.0

Abstract

Industry 4.0 (I4.0), also known as the Fourth Industrial Revolution, is characterized by various intelligent technologies connected through the internet that harness the power of artificial intelligence and machine learning. These technologies provide the ability for managers to communicate directly with machines, or for machine-to-machine communication. Their capabilities permit the sharing of large streams of data and the potential for autonomous and decentralized decision-making. These technological advancements will have a dramatic effect on global supply chains. The capabilities of I4.0 technologies will have tremendous impacts on the skills and competencies required by supply chain managers, as well as how they are managed within their organizations to maximize decision-making speed and quality. This dissertation reviews the state of theory and practice in talent management in an I4.0 context through a systematic literature review. Our review discusses multiple themes and future research streams in this timely area. Our second study explores the recruitment of new talent through internship conversions to full-time employees. Using self-determination theory, we examine how interns consider their internship experience when selecting a full-time employment position through a mixed-methods approach. Our study uses computer-aided text analysis to examine a sample of 111 supply chain internship journals. We follow this study with a thematic analysis of a sample of focus groups and writing assignments from 67 student-interns. Our themes include intern perceptions of their firms, intern behaviors during internship, and intern firm conversion behaviors targeted at converting interns to full-time employees. Finally, our third study explores talent development of middle managers working in I4.0. We analyze a sample of 16 semi-structured interviews with middle managers and report results through a thematic analysis, discussing themes that include decision-making effectiveness and executive investment.