Evaluating Inventory Risk Pooling Strategy for Multi-Echelon Distribution Network

Abstract

Rising inventory costs is an ongoing challenge for any firm. These costs are of special significance to retail firms like Coppel, whose inventory investments are typically high and margins are slim. Inventory risk pooling is a strategy that is often ignored but can help bring significant cost reduction without affecting service levels. Such inventory decisions are generally considered tactical and are often constrained by the strategic network design decisions that precede them. This siloed approach leads to sub-optimal decisions. The objective of this study is to integrate the two to overcome this inflexibility and evaluate whether the existing distribution network can be profitably reconfigured to introduce risk pooling. This paper develops a decision support model for Coppel to identify the best location for pooling inventory while minimizing total supply chain costs. Integrating inventory and location decisions represents a Location-Inventory Problem (LIP), which comes with inherent modeling and computational complexities due to increased problem size and non-linearity. Evolving solution techniques and improving computing power now make it feasible to solve LIPs efficiently. We develop a Mixed Integer Nonlinear Programming model that follows a Guaranteed Service Model approach to solve this integrated LIP in a multi-echelon multi-product supply chain environment. Due to the non-linear nature of the model, we deploy piecewise approximation methods to first linearize the function before solving. Our research demonstrates that reconfiguring the existing network to introduce risk pooling could reduce the supply chain costs of major product classes by 15%, without affecting their service levels. This is a common challenge across industries. Therefore, the benefits of this research extend beyond Coppel and retail industry.