A generalization of the Theory of Constraints: Choosing the optimal improvement option with consideration of variability and costs

Abstract

The Theory of Constraints (TOC) was proposed in the mid-1980s and has significantly impacted productivity improvement in manufacturing systems. Although it is intuitive and easy to understand, its conclusions are mainly derived from deterministic settings or based on mean values. This article generalizes the concept of TOC to stochastic settings through the performance analysis of queueing systems and simulation studies. We show that, in stochastic settings, the conventional TOC may not be optimal, and a throughput bottleneck should be considered in certain types of machines at the planning stage. Incorporating the system variability and improvement costs, the Generalized Process Of OnGoing Improvement (GPOOGI) is developed in this study. It shows that improving a frontend machine in a production line can be more effective than improving the throughput bottleneck. The findings indicate that we should consider the dependence among stations and the cost of improvement options during productivity improvement and should not simply improve the system bottleneck according to the conventional TOC. According to the GPOOGI, the managers of production systems would be able to make optimal decision during the continuous improvement process.

Keywords:

• Theory of Constraints
• productivity improvement
• simulation
• queueing theory

Additional information

Funding

This research was supported by the National Natural Science Foundation of China under grant no. 71802130.

Notes on contributors

Kan Wu

Kan Wu is an assistant professor in the School of Mechanical & Aerospace Engineering at Nanyang Technological University. He received a B.S. degree from the National Tsinghua University, M.S. degree from the University of California at Berkeley, and Ph.D. degree in industrial and systems engineering from Georgia Institute of Technology. He has 10 years of experience in the semiconductor industry, from a consultant to an IE manager. He has guided the development of scheduling and dispatching systems at the Taiwan Semiconductor Manufacturing Company and ramped up a 300 mm DRAM fab for Inotera Memories. Before joining NTU, he was the CTO and founding team member of a startup company in the US. His Ph.D. dissertation was awarded third place for the IIE Pritsker Doctoral Dissertation Award in 2010. His research interests are primarily in the areas of queueing theory, with applications in the performance evaluation of supply chains and manufacturing systems. He currently owns four license agreements and six US patents in the field of semiconductor manufacturing.

Meimei Zheng

Meimei Zheng is an assistant professor in the Division of Industrial Engineering and Management, Mechanical Engineering, Shanghai Jiao Tong University. Her major research interests are supply chain management, service-oriented manufacturing and Industry 4.0. She received her B.S. degree and M.S. degree from Shanghai Jiao Tong University and Ph.D. degree from Nanyang Technological University.

Yichi Shen

Yichi Shen is currently a postdoctoral research fellow in the Department of Industrial Systems Engineering & Management, National University of Singapore. Dr. Shen obtained his Ph.D. in industrial and system management from Nanyang Technological University, Singapore and holds M.Sc. and B.Sc. degrees in mathematics from Nanjing University, China. His current research focuses on developing efficient and cost-effective algorithms for the computational optimization of stochastic expensive black-box functions and he is passionate about applying the research to solving real-life problems. His research interests also cover operations research, data analysis, stochastic models and smart manufacturing.