In recent years, there has been an increasing demand for appropriate changes in supply chains. However, given the complexity of these systems, it is difficult to predict the propagation of the effects of changes, which hinders decision making. The objective of this study was to support decision-making for change by describing supply chain elements at an analyzable granularity and clarifying the mechanism of impact propagation of a change in an element on the entire supply chain. In addition to the three elements of performance, capacity, and characteristics, and the macro and micro layers of modeling that enable the structural understanding and tracking of impact propagation, a method was developed to support change decision making by indexing the impact of the change on the entire system. In addition to numerical elements, such as the capacity of production facilities, structural elements were included, such as the BOM and plant layout. The relationships among elements were established on a simulation basis for quantitative discussions. Case studies were conducted in a hypothetical supply chain to confirm the effectiveness of the method in supporting the selection of reasonable change alternatives.
Sato et al. (Wed,) studied this question.