Abstract for: A Real-World Case Study of Using an SD-Based Agent Simulation to Model Multi-Tier Supply Chains and Disruptions

Many software tools available do an excellent job showing companies the near-real-time or short-term consequences in operations due to changes in a supply chain. However, these tools only look at Tier 1 suppliers and do not cover longer timeframes (e.g., many months to several years). Most importantly, these tools do not capture the feedback mechanisms that cause companies throughout a supply chain to adapt and respond to these changes. SD has proven to be an excellent approach for simulating complex, adaptive systems like supply chains. Consider the traditional beer game and how the structure of a system generates its behavior. This concept can be applied at a much larger scale by using SD models as agents in an agent-based simulation (i.e., each entity in the supply chain is a SD model with inventory control feedback mechanisms, etc.). SD-based agent simulations have proven to provide meaningful and actionable insights into supply chain issues like disruptions, transportation constraints, and re-shoring or near-shoring initiatives. A real-world case study will be shared that involves the analysis of transitioning suppliers from one country to another and the analysis of operational impacts of disruptions at various suppliers within that supply chain. SD-based agent simulations helped provide valuable information about how different multi-tier structures of the supply chain adapt and respond. When near-shoring, it was shown that risk was reduced by bringing some suppliers closer to the OEM. With disruptions, it was shown that disruptions that are further away in the supply chain (e.g., Tier 1 vs. Tier 2) and geographically further away tend to have less operational impact on the OEM. Agentic AI (agent-based simulation)