Multi-retailer Sales Model under Uncertain Demand in a Pharmaceutical Two-Echelon Supply Chain with Vendor Managed Inventory System

Abstract

In recent years, the Vendor Managed Inventory (VMI) system, in which the vendor manages its own and its retailers’ inventories, has been studied to improve the performance of two-echelon supply chains. However, most of these studies consider the pattern demands of the retailers as deterministic, which is very unlikely in practice where variability is significant. Thus, VMI systems based on deterministic demand patterns can lead to inefficient results, compromising the benefits of this system. Particularly within the pharmaceutical industry, an efficient supply chain through VMI is vital. The present work contributes within this context by proposing a multi-retailer VMI model to maximize the profits of a two-echelon supply chain in the presence of non-deterministic or uncertain demand. Due to the complexity of the model, a micro-genetic algorithm is developed to determine the lot size strategy to address the variable pattern of the non-deterministic demand within the profit function and reduce the stockout risk. The proposed model was validated through computer simulation, which is important to dynamically evaluate the performance of the model’s parameters. The dynamic evaluation showed that the proposed model is more efficient to reduce stockout events than models with consider deterministic demand patterns.