Maximizing EVA in a Cocoa Supply Chain Network Design with a Hybrid Optimization Approach: A Case Study in Vietnam

Abstract

Given the intricacy of the cocoa supply chain and the abundance of relevant data, effectively managing factor variations proves to be a challenge, making traditional mathematical optimization methods insufficient for practical applications. This study formulates a three-phase optimization approach combining Mixed-Integer Linear Programming (MILP) and Simulation-Based Optimization (SBO), where MILP determines strategic decisions (i.e., supplier selection and establishment of distribution centers), and the simulation derives tactical plans (i.e., inventory levels) under demand uncertainty. The proposed approach combines the strengths of both methods and helps compensate for their weaknesses. Moreover, this study demonstrates the necessity of maximizing Economic Value Added (EVA) by prioritizing the capital charge from an investment when designing the network instead of the operating profit maximization, which considers the short-term gains. Employing a case study on the food industry in Southern Vietnam, the effectiveness of this approach and its ability to derive valuable managerial insights can be demonstrated. The proposed three-phase approach can enhance a significant methodological deficiency by introducing a hybrid approach that effectively encapsulates the structural and dynamic intricacies inherent in supply chain systems, while simultaneously optimizing for EVA, the financial performance metric that has been overlooked in many existing supply chain modeling approaches.