Abstract:
The theory of constraints was proposed in the mid-1980s and has significant impact on the productivity improvement in manufacturing systems. While it is intuitive and easy to understand, its conclusions are mainly derived in deterministic settings or based on the first moment results. Since production systems are stochastic in general, some of its conclusions are not rigorous and have to be modified. In this study, we show that the process of ongoing improvement may lead to unfavorable outcomes and the throughput bottleneck should be planned on certain types of machines. Specifically, if a system has no bottleneck, every station will be a bottleneck. Furthermore, through capturing the dependence among stations, we show that improving the variability of a frontend machine in a production line can be more effective than improving the variability of a throughput bottleneck.

Bio:

Kan Wu is an assistant professor in the School of Mechanical & Aerospace Engineering at Nanyang Technological University. He received the B.S. degree from National Tsinghua University, M.S. degree from University of California at Berkeley, and Ph.D. degree in Industrial and Systems Engineering from Georgia Institute of Technology. He has ten years of experience in the semiconductor industry, from a consultant to an IE manager. Before joining NTU, he was the CTO and founding team member of a startup company in the US. His PhD dissertation was awarded the 3rd place for the IIE Pritsker Doctoral Dissertation Award. His current research interests are primarily in the areas of queueing theory, with applications in the performance evaluation of supply chains and manufacturing systems.