A decision support system for global supply chain risk management by considering premium freights

As a result of globalization, supply chains geographically spread on larger areas. This fact makes supply chains more vulnerable against supply chain risks. An adverse event affecting a supply chain partner may influence the entire supply chain, and even pose a threat on the competitive advantage of the supply chain. In this context, a decision support system (DSS) is developed for supply chain risk management (SCRM) in global supply chains. The proposed DSS covers all phases of SCRM, namely, risk identification, risk assessment, risk mitigation, risk monitoring and control phases. In the risk identification phase, the significant supply chain risks affecting material flow between supply chain agents are identified. In risk assessment phase, global supply chain is decomposed into material-level or product-level critical sub-networks according to the decision maker's preference by using TOPSIS. In the risk mitigation phase, the best parameter values for risk mitigation strategies are determined by using a simulation-based optimization framework. In particular, a decomposition-based multi-objective differential evolution algorithm is developed for the optimization phase of the framework. In the risk monitoring and control phase, supply chain performance is monitored continuously, and the DSS is re-implemented if it is needed. The proposed DSS is implemented to an automotive supply chain spread on Europe. The DSS is employed to inbound and outbound risk management cases. The results obtained for both cases are presented in comparison with the results obtained from non-dominated sorting genetic algorithm-II (NSGA-II) and the current operating condition of the supply chain. The results reveal that the proposed DSS suggest better risk management solutions for both cases.