Optimize Multi-Sourcing Process In Supply Chain: A Case Study Of A Food Manufacturer

Abstract

Effective supplier selection and optimization of multi-sourcing strategies are critical for enhancing supply chain performance and resilience in stochastic business environments. This thesis focuses on addressing the challenges associated with choosing suppliers and optimizing multi-sourcing through the application of stochastic programming. The goal of this thesis is to provide a framework that integrates stochastic programming techniques into the supplier selection process and multi-sourcing decisions. By considering uncertainties and variability in factors such as supplier characteristics, pricing, exchange rate, and disruption, the proposed approach aims to improve decisionmaking under uncertain conditions. Stochastic programming models are developed to account for the probabilistic nature of factors affecting supplier performance and market dynamics. Through the application of the proposed framework, supply chain managers and decision-makers can make informed decisions regarding supplier selection and multisourcing, taking into account risk factors and uncertainties. The research findings contribute to the body of knowledge in supply chain management and offer practical insights for practitioners seeking to enhance supply chain resilience and performance.