Optimizing production and maintenance for the service-oriented manufacturing supply chain
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Optimizing production and maintenance for the service-oriented manufacturing supply chain Zhong-Zhong Jiang1,2
· Na He1 · Xuwei Qin1,2 · Minghe Sun3 · Pengfei Wang1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This work investigates a service-oriented manufacturing supply chain in which a manufacturer and an operator make decisions about equipment quality and maintenance service. Both the manufacturer and the operator have to make tradeoffs between equipment quality and maintenance service to maximize their own profit, which can lead to supply chain conflict. Decision models under decentralized decisions are formulated first for the manufacturer and the operator to make their respective independent optimal decisions, and a decision model under centralized decisions is formulated to obtain optimal decisions for the supply chain. The results show that channel coordination is not achievable and an agreement cannot be reached with decentralized decisions. To address this issue, two, i.e., a cost-sharing and a performance-based, strategies are introduced to coordinate the supply chain. The results reveal that the manufacturer and the operator are motivated to find the optimal decisions to maximize the profit of the supply chain when the subsidy rate or the penalty rate is equal to the profit margin of the operator. The models and the coordination strategies are extended to the situation considering the learning behavior of the manufacturer. The results show that the learning behavior impacts the profit of the supply chain with coordination and the preferences of the coordination strategy in the supply chain. Keywords Service-oriented manufacturing · Equipment quality · Preventive and corrective maintenance · Learning behavior · Supply chain optimization and coordination
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Xuwei Qin [email protected]
1
School of Business Administration and Institute of Behavioral and Service Operations Management, Northeastern University, Shenyang 110167, China
2
Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110189, China
3
Department of Management Science and Statistics, College of Business, the University of Texas at San Antonio, San Antonio, TX 78249, USA
123
Annals of Operations Research
1 Introduction With the rapid advancement in information and communication technology, fully integrated and collaborative manufacturing, called smart manufacturing, systems have emerged to meet changing demands and manufacturing conditions, such as supply channels and operators’ needs (Lu and Weng 2018). Smart manufacturing refers to a number of automated systems integrating automated data exchange and manufacturing technologies, which can support effective and timely decision making. Based on smart manufacturing, service-oriented manufacturing (SOM) is an innovative business model that shifts from traditional manufacturing to servitization (Meier et al. 2011; Wang et al. 2015). Different from traditional manufacturing
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