Data and Information Flow Improvements in Manufacturing Systems

Abstract

The fourth industrial revolution, or Industry 4.0, is making its mark with a wave of efforts to digitize and digitalize data and information in manufacturing systems. Global efforts are being made to accelerate the adoption of advanced technologies in the manufacturing industry. Before Industry 4.0, continuous improvement efforts were focused on the efficient flow of physical products to shorten lead times, but that is no longer enough to remain viable in today’s digital environment. Accurate and efficient data and information will be the difference between companies that remain viable and those that become extinct. There is a significant amount of ambiguity surrounding Industry 4.0, other similar terms, its technologies, and its benefits that have caused confusion throughout the manufacturing industry. However, it is clear that the costs of poorly designed data and information flows have yet to be understood, and the opportunities for improvement are untapped, eating up costs that could be minimized and/or eliminated. Purposeful design of interoperable data and information flows to achieve value creation and a complete digital thread are critical to organization competitiveness, now and in the future. Currently, there is not a way for manufacturers to identify and eliminate data and information wastes and evaluate the impact on their organizations. This research begins to close this gap by uncovering, illuminating, and categorizing the non-value-added activities, or waste, in data and information flows in manufacturing systems. This is made possible by performing a deep dive into Lean literature to understand how Taiichi Ohno developed the 7 Wastes of the Toyota Production System (TPS) so that the success can be replicated in other domains, such as data and information flows. This work also presents the results of a quantitative simulation analysis that depicts the negative impacts that data and information wastes can have on manufacturing production operations.