An Optimization-Enhanced Dynamic Approach for Supply Chain Risk Analysis

摘要

Globalization brings opportunities and challenges for supply chain involved companies. Supply chains may be fragile when sudden business environment fluctuations occur. Various quantitative analysis models are built to understand and mitigate supply chain risks. Dynamic Flow Bayesian Networks (DFBNs) are created by integrating Dynamic Bayesian Networks and System Dynamics to demonstrate the feedback flows of a supply chain with stochastic risks considered. However, it has limited ability in suggesting straightforward solutions for mitigating the risks. An Optimized Dynamic Flow Bayesian Network (ODFBN) incorporates mathematical optimization with the original DFBN. An ODFBN is a tool that offers business performance improvement strategies for supply chains by establishing objectives of a supply chain and constraints on the flows. Optimization-enhanced risk-influenced dynamic flow variables provide supply chain practitioners with a more effective reference for their business strategy. This paper presents an application of the ODFBN for a two-stage supply chain. Comparison between the ODFBN and the DFBN is illustrated with a discussion of preliminary modeling results.