制造商产能变化对JIT供应物流的影响及稳健响应机制研究——以汽车行业为例

随着市场竞争日益激烈，制造商产能变化在汽车在内的众多行业频繁发生，这对实施JIT供应物流的3PL形成了巨大压力，3PL如何应对来保证JIT运作显得尤为迫切和重要。但是，以往的研究更多关注的是制造商面对不确定市场时自身产能的调整过程，而对如何影响与之紧密相关的供应链成员及活动的研究则很少涉及，尤其是起日益重要作用的物流的影响研究则更少。本项目首先通过实证分析，研究物流始端供应商在产能变化后供应频率等指标的变化特征，识别出显著性指标；然后，在Milk-run和越库集成的离散仿真平台上，探析受产能变化影响的供应商显著性指标对JIT物流绩效的影响，确定变化幅度大的绩效，并进一步探讨对Milk-run、越库中心和越库配送三个运作环节影响的方式和程度。最后，提出针对所研究及类似复杂系统的两阶段稳健优化方法，并设计出相应的稳健响应机制策略。本项目既有理论创新也为3PL应对制造商产能变化事件提供实践指导。

As the competition of today’s global market becomes increasingly fierce, manufacturers in many industries such as automobile frequently adjust their capacity to match variable demand. However, this adjustment would pose a substantial pressure on their third-party logistics provider (3PL), which tends to practice just-in-time (JIT) logistics mandated by their manufacturers in parts delivery. Consequently, how to respond to meet the requirements of the JIT operations becomes the leading concern among 3PL. Nevertheless, previous research mainly investigates how a manufacturer adjusts its own capacity under uncertainty, but little effort is made to analyze this impact on other members that closely related to the manufacturer in the supply chain. Moreover, none of these papers touch on the influential analysis to logistics which becomes more and more indispensable to manufacturing. . In this research project, we first empirically examine the variation of logistics factors, such as replenishment frequencies of suppliers, produced by capacity adjustment of their manufacturers, and identify those significant factors that contribute to change of the performance. Next, we develop a comprehensive discrete simulation model that integrates both milk-run and cross-docking operations. This simulation serves as a platform to explore the impact of the resulting significant factors on JIT performance, as well as three key logistics operations, including milk-run, cross-docking center, and cross-docking distribution; in addition, discern those JIT performance measurements that have remarkable changes. Finally, we propose a newly two-stage robust simulation optimization method for the system under study, which also well accommodates optimization issues for a similar complex system. The proposed method enables us to design a corresponding responsively robust mechanism to minimize the impact of capacity adjustment. This project will not just enrich related theories, but provide theoretical and practical guidance for 3PL to take care of capacity adjustment of manufacturers..