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In this paper, we propose a stochastic programming approach to manage supply chain disruptions of an enterprise with an emphasis on demand and disruptions uncertainty. The supply chain considered here is multi-product, multi-agent in nature. However, the model considers purchasing cost, inventory cost and emergency ordering cost. Decisions such as ordering quantities in pre-disruptions and post-disruptions situation are taken into consideration. On the other hand, quality and delivery performance requirements are also included in the proposed analytical framework. We use Monte Carlo sampling approach for the purpose of sampling for a given probability distribution of stochastic parameters. In addition, we consider a disruptions planning case study and apply Benders decomposition (BD)/L-shaped algorithm to solve the model. The model is coded on GAMS 24.1.3 and run by CPLEX (12.5.1.0) and DECIS solver. We minimize total cost that includes first stage cost and second stage disruptions scenario cost. Several test instances with different disruptions scenarios are considered. We then compare the total costs under several disruptions scenarios. We hope that the model could be used as an effective tool to analyze and decide on supply chain disruptions planning and management of an enterprise thus it could contribute in continuity of manufacturing/business operations and therefore help in building a resilient supply chain.

A Stochastic Programming Approach to Supply Chain Disruptions Planning and Management