A prediction-based supply chain recovery strategy under disruption risks

ABSTRACT

This paper proposes a prediction-based product change recovery strategy for the SC (supply chain) under long-term disruptions. A real-world case composed of multi-period planning and dynamic customer demand is considered. First, to forecast dynamic customer demand, a data-based demand predictive method with feedback errors is designed. Second, to schedule procurement and production in advance, based on the predicted demand, the selection of the supply portfolio is transformed into a bi-objective mixed integer programming problem incorporating product change. Furthermore, goods allocation and customer order fulfillment strategy is also designed to finish the transportation of goods and delivery of customer orders. To systematically synthesise and address the problems aforementioned, a three-stage heuristic method is further developed. Finally, a case study is presented to substantiate the reliability of the proposed strategy via an actual SC model of Dongsheng Electronics Co., Ltd. Based on the results obtained after one month, the proposed disruption recovery strategy can reduce the unit product cost and improve the service level, which outperforms the original method adopted by Dongsheng. Additionally, sensitivity analysis of unit product change cost is conducted to reveal the effect of different unit product change costs on SC performance.

KEYWORDS:

• Prediction-based optimisation
• disruption mitigation
• product change
• supply chain recovery
• supply chain resilience

Acknowledgments

The authors thank Associate Editor and two anonymous referees for their constructive and valuable comments which have significantly improved the quality of the manuscript.

Data availability statements

The data that support the findings of this study are available from Dongsheng Electronics Co., Ltd. Restrictions apply to the availability of these data, which are not available without permission of Dongsheng Electronics Co., Ltd.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 https://global.chinadaily.com.cn/a/202206/08/WS629fdf59a310fd2b29e613e9.html

Additional information

Funding

This work is supported by The National Key Research and Development Program No. 2020YFB1708200.

Notes on contributors

Yi Yang

Yi Yang was born in Jiangsu province, China, in 1995. He is currently pursuing the Ph.D. degree in control science and engineering with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interests include supply chain risk management, change control of product and supply chain system.

Chen Peng

Chen Peng received the B.Sc. and M.Sc. degrees in coal preparation, and the Ph.D. degree in control theory and control engineering from the Chinese University of Mining Technology, Xuzhou, China, in 1996, 1999, and 2002, respectively. From November 2004 to January 2005, he was a Research Associate with the University of Hong Kong, Hong Kong. From July 2006 to August 2007, he was a Visiting Scholar with the Queensland University of Technology, Brisbane, QLD, Australia. From July 2011 to August 2012, he was a Postdoctoral Research Fellow with Central Queensland University, Rockhampton, QLD, Australia. In 2012, he was appointed as an Eastern Scholar with the Municipal Commission of Education, Shanghai, China, and joined Shanghai University, Shanghai. His current research interests include networked control systems, distributed control systems, smart grid, and intelligent control systems. Dr. Peng is an Associate Editor of a number of international journals, including the IEEE Transactions on Industrial Informatics, Information Sciences, and Transactions of the Institute of Measurement and Control. He was named a Highly Cited Researcher from 2020 to 2022 by Clarivate Analytics.