Title: Predicting bottlenecks in manufacturing shops through capacity and demand observations from multiple perspectives

Authors: Juan Tang; Bang-yi Li; Zhi Liu

Addresses: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, No. 169 Shengtai West Road, Nanjing 210006, Jiangsu Province, China; College of Management Engineering, Anhui Polytechnic University, No. 8 Beijing Middle Road, Wuhu 241000, Anhui Province, China ' College of Economics and Management, Nanjing University of Aeronautics and Astronautics, No. 169 Shengtai West Road, Nanjing 210006, Jiangsu Province, China ' College of Economics and Management, Nanjing University of Aeronautics and Astronautics, No. 169 Shengtai West Road, Nanjing 210006, Jiangsu Province, China; College of Management Engineering, Anhui Polytechnic University, No. 8 Beijing Middle Road, Wuhu 241000, Anhui Province, China

Abstract: Uncertain factors in modern multi-variety and small-lot manufacturing make it extremely challenging to optimise and control the production process. Researchers propose a bottleneck-based optimisation method to reduce perplexity and enhance optimisation. Detecting bottlenecks is a crucial first step in this method and its accuracy has great impacts on production optimisation. This study proposes an independent bottleneck degree to describe the probability of a manufacturing cell becoming a system bottleneck, and model it using capacity and demand observations from the perspectives of capability, quality, and cost. Based on the independent bottleneck degree, we design a closed-loop multi-bottleneck prediction method, which can solve the responsibility cognisance problem resulting from correlation among manufacturing cells. Therefore, it can predict bottlenecks, especially multiple bottlenecks, accurately compared to existing methods.

Keywords: bottleneck; independent bottleneck degree; bottleneck responsibility; closed-loop prediction.

DOI: 10.1504/IJMTM.2018.093352

International Journal of Manufacturing Technology and Management, 2018 Vol.32 No.4/5, pp.358 - 380

Received: 23 Sep 2016
Accepted: 15 Mar 2017
Published online: 25 Jul 2018 *

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