Multi-Variate Regression Analysis of Inventory Parameters in a Decentralized Multi-Echelon Supply Chain: A Simulation-Based Approach

Effective inventory management in decentralized multi-echelon supply chains (MESCs) is essential for minimizing costs and improving service levels. This study introduces a two-stage approach that combines discrete-event simulation and multi-variate regression analysis (MVRA) to analyze a three-echelon supply chain. The first stage simulates various inventory policies and demand scenarios across manufacturers, wholesalers, and retailers. The second stage uses MVRA to examine how inventory decisions at each echelon influence key performance indicators, including inventory cost and inventory level. The results identify the parameters that most significantly affect supply chain performance, offering practical guidance for optimizing policies in complex and decentralized systems.