Arif Suryadi, Hsin Rau
This study explores how a supply chain manager selects sourcing mitigation strategies to enhance the supply chain resilience of an automotive company, considering the manager's risk tolerances, goals, and various disruption probabilities. Sixteen hybrid mitigation strategies are presented in this study, combining multi-sourcing with backup suppliers, regionalized suppliers, suppliers’ excess capacity, reserved inventory at supplier sites, and alternate shipment strategies. The two-stage multi-objective stochastic programming approach is utilized to determine the optimal pre-disruption and post-disruption decisions while accommodating all strategies considered. A fuzzy c-means clustering algorithm is used to provide regional parameter settings. Then, the problem is solved using the augmented ε-constraint with the linear programming technique for multidimensional analysis of preference. The results show that different hybrid mitigation strategies work best depending on the manager's selection. The total rank score method is introduced to select a hybrid mitigation strategy, considering the deterioration of total cost, service level, order stability to main suppliers, and GHG emission performance. The findings show that no universal mitigation strategy fits all situations; instead, the strategy must be tailored to individual managers’ preferences and understanding of risks. The study also offers practical guidance by showing how strategies should be customized to match each manager's risk tolerance and knowledge of disruption probability. Finally, it highlights that using too many strategies at once does not always lead to better outcomes; instead, choosing the right mix of hybrid mitigation strategy that fits the manager's needs leads to better resilience. © 2025 Elsevier Ltd
Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan City, 32023, Taiwan; Bachelor Program in Industrial Engineering, Universitas Kristen Maranatha, Bandung, 40164, Indonesia; Department of Agroindustrial Technology, Faculty of Agricultural Technology, Brawijaya University, Malang, 64145, Indonesia
