Mathematical Modelling and Optimisation of Supply Chain Networks Under Uncertain Demand Scenarios

Abstract

Demand uncertainty is a significant challenge in modern supply chain management, affecting operational efficiency, costs, and customer service levels. This study develops a mathematical model based on stochastic programming and robust optimization to optimize the supply chain network in the face of such uncertainty. The model considers decision variables, such as product allocation, facility location, and various operational constraints, including capacity and logistics costs. Multi-scenario simulations are applied to evaluate supply chain performance under various uncertain conditions. The results show that the applied mathematical model can significantly improve supply chain efficiency. The stochastic programming approach successfully reduces operational costs by 15%, stock-out rates by 25%, and storage costs by 10%. Meanwhile, robust optimization can reduce the risk of supply chain disruptions by 20% while maintaining optimal customer service levels. The scenario-based approach increases customer service levels by up to 95%, demonstrating the superiority of this strategy in responding to dynamic market changes. These findings confirm that mathematical optimization methods can improve supply chain resilience and efficiency, even under uncertain conditions. Although this model has challenges in its implementation, such as the need for accurate data and the complexity of calculations, integration with digital technologies such as big data analytics can be a solution in the future. This research contributes to supply chain management and offers new directions for more adaptive and effective decision-making strategies.