Multi-objective supply chain sourcing strategy design under risk using PSO and simulation

Sourcing strategy design in a supply chain is vital to gain competitive advantage. In recent years, supply chain risks are growing significantly and supplier failure is identified as one of the top supply chain risks. Researchers attempt to mitigate the negative impacts of supplier failure by applying strategies such as local versus global sourcing, single versus dual/multiple-sourcing, performance-based supply contracts, and optimizing the order allocation among suppliers. Global sourcing is a widely recognized strategy among firms, and it involves a trade-off between reliable, high-cost local suppliers and unreliable, low-cost offshore suppliers. The global sourcing is associated with the risks of exchange rate volatility, trade restrictions, longer lead time, and problems with supplier reliability. Sourcing strategy design considering price, exchange rate risks, and supplier delivery reliability is an important research topic and needs attention. In this work, a hybrid optimization and simulation approach is proposed to design the supply chain sourcing strategy. In the optimization approach, a multi-objective binary particle swarm algorithm is developed for minimizing the total cost and maximizing the supplier delivery reliability. Selected scenarios from the optimization results are modeled using Witness simulation software to evaluate the robustness of sourcing strategies under price, exchange rate and demand risks. The proposed approach is exemplified using a real-life case study of a plastic product manufacture in India.