Distribution Network Optimization A Case Study Of Sabeco

Abstract

Closed-loop supply chains have become a global trend today, primarily due to their ability to reduce environmental emissions and material costs. However, the challenge in closed-loop supply chains lies in determining the optimal quantity and placement of Distribution Centers (DCs) and return centers. Numerous studies have been conducted to address the question of whether DCs and return centers should be located together or separately. In this thesis, we have analyzed and addressed the challenges of the closed-loop supply chain for SBC. SBC's supply chain evolved from the 1990s, initially employing a point-to-point delivery model directly to customers. Over more than 30 years of continuous development, the point-to-point transport model transitioned into a central Distribution Center (DC) model. However, due to the increasing demand over the years, the opening of central DCs and Return Centers became uncontrollable, resulting in escalating operational costs while the efficiency of low-performing DCs diminished. Additionally, inadequate allocation of manufactured goods from the factories led to increased distribution costs. To tackle these issues, we presented a Mixed Integer Linear Programming (MILP) model that can assist SBC in optimizing its current supply chain by closing inefficient DCs or opening new ones. The model's output includes the reallocation of production flows from the factories, the utilization efficiency of the remaining DCs, and the return center after closure. Sensitivity analysis was also conducted to identify optimal operational parameters for supply chain efficiency. Finally, the improvement proposals based on the model were provided to help SBC enhance operational efficiency.