Integrated analysis of productivity and machine condition degradation: Performance evaluation and bottleneck identification

Abstract

Machine condition degradation is widely observed in manufacturing systems. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in a worse operating stage are more likely to fail, thus causing more frequent down periods and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. In this article, we consider a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity.

Keywords:

• Machine condition degradation
• integration
• throughput analysis
• geometric machine

Additional information

Funding

This work is supported by the U.S. National Science Foundation under Grants CNS-1638213 and CMMI-1829238.

Notes on contributors

Yunyi Kang

Yunyi Kang is now working towards a Ph.D. at the School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ. He received a B.S. degree from the Department of Industrial Systems and Engineering, the Hong Kong Polytechnic University, Kowloon, Hong Kong, China, and an M.S. degree from the Department of Industrial Systems and Engineering, Rutgers University, New Brunswick, NJ, in 2013 and 2015, respectively. His research interests are in modeling, analysis and real-time control of production systems. He received a best student paper finalist award at the IEEE Conference on Automation Science and Engineering. He is a student member of the Institute of Industrial and Systems Engineers.

Feng Ju

Feng Ju is an assistant professor with the School of Computing, Informatics & Decision Systems Engineering, Arizona State University, Tempe, AZ. He received a B.S. degree from Shanghai Jiao Tong University, Shanghai, China, in 2010, and an M.S. degree in electrical and computer engineering and Ph.D. degree in industrial and systems engineering from the University of Wisconsin, Madison, WI, in 2011 and 2015, respectively. His current research interests include modeling, analysis, continuous improvement, and optimization of manufacturing systems. He is also a member of the Institute for Operations Research and the Management Sciences, Institute of Industrial and Systems Engineers, and Institute of Electrical and Electronics Engineers. He has been a recipient of multiple awards, including the best paper award in IFAC MIM and best student paper finalist in IEEE CASE and IFAC INCOM.