An Integrated Risk Management Framework for Global Supply Chains

In this study, a risk management framework is developed to support risk management decisions in global supply chains. The proposed framework covers all phases of risk management, namely, risk identification, risk miti-gation and control. In the risk identification phase of the framework, the supply chain is decomposed into either material-level or product-level sub-networks according to the decision maker’s preference. Afterwards, the most crit-ical sub-network is modelled to evaluate different risk mitigation strategies. In particular, a combination of redun-dancy and flexibility strategies is considered for risk mitigation. These strategies are evaluated by simulation models in terms of their effectiveness and efficiency. While inventory holding cost is used as efficiency measure, effective-ness of the strategies is measured by premium freight ratio. The proposed framework provides a comprehensive and reliable decision support since it covers all phases of risk management and relies on quantitative data, and statistical analysis in risk modelling. Moreover, it is flexible as it can be easily adapted to any change in supply chain environ-ment and strategy. In order to show the applicability of the framework, a practical demonstration is presented for a European automotive company. The results indicate that the proposed framework improves the supply chain perfor-mance in terms of efficiency and effectiveness.

Keywords:

Supply chain risk management, premium freight, TOPSIS simulation,

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Aqlan, F., & Lam, S. S. (2015a). A fuzzy-based integrated framework for supply chain risk assessment. International Journal of Production Economics, 161, 54-63.
Aqlan, F., & Lam, S. S. (2015b). Supply chain risk modelling and mitigation. International Journal of Production Research, 53(18), 5640-5656.
Bueno-Solano, A., & Cedillo-Campos, M. G. (2014). Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts. Transportation Research Part E: Logistics and Transportation Review, 61, 1-12.
Cardoso, S. R., Paula Barbosa-Póvoa, A., Relvas, S., & Novais, A. Q. (2015). Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty. Omega, 56, 53-73.
Carvalho, H., Barroso, A. P., Machado, V. H., Azevedo, S., & Cruz-Machado, V. (2012). Supply chain redesign for resilience using simulation. Computers & Industrial Engineering, 62(1), 329-341.
Chaudhuri, A., Mohanty, B. K., & Singh, K. N. (2013). Supply chain risk assessment during new product development: a group decision making approach using numeric and linguistic data. International Journal of Production Research, 51(10), 2790-2804.
Chen, P.-S., & Wu, M.-T. (2013). A modified failure mode and effects analysis method for supplier selection problems in the supply chain risk environment: A case study. Computers & Industrial Engineering, 66(4), 634-642.
Chopra, S., & Sodhi, M. S. (2004). Managing Risk To Avoid Supply-Chain Breakdown. MIT Sloan management review, 46(1), 53-61.
Christopher, M., & Peck, H. (2004). Building the Resilient Supply Chain. International Journal of Logistics Management, 15(2), 1 - 14.
Colicchia, C., & Strozzi, F. (2012). Supply chain risk management: a new methodology for a systematic literature review. Supply Chain Management: An International Journal, 17(4), 403-418.
Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). A systems approach for modelling supply chain risks. Supply Chain Management: An International Journal, 18(5), 523-538.
Giannakis, M., & Louis, M. (2011). A multi-agent based framework for supply chain risk management. Journal of Purchasing and Supply Management, 17(1), 23-31.
Govindan, K., & Jepsen, M. B. (2015). Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers. Journal of the Operational Research Society, 67(2), 339-376.
Guertler, B., & Spinler, S. (2015). When does operational risk cause supply chain enterprises to tip? A simulation of intra-organizational dynamics. Omega, 57, 54-69.
Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk – Definition, measure and modeling. Omega, 52, 119-132.
Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: a literature review. International Journal of Production Research, 53(16), 5031-5069.
Hwang, C. L., & Yoon, K. P. (1981). Multiple attribute decision making: Methods and applications: Springer-Verlag, New York.
Kara, M. E., Fırat, S. Ü. O. & Ghadge, A. (2020). A data mining-based framework for supply chain risk management. Computers & Industrial Engineering, 139, 105570.
Klibi, W., & Martel, A. (2012). Scenario-based Supply Chain Network risk modeling. European Journal of Operational Research, 223(3), 644-658.
Montgomery, D. C. (2008). Design and analysis of experiments: John Wiley & Sons.
Oliveira, J. B., Jin, M., Lima, R. S., Kobza, J. E., & Montevechi, J. A. B. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice and Theory, 92, 17-44.
Pournader, M., Kach, A., & Talluri, S. (2020). A review of the existing and emerging topics in the supply chain risk management literature. Decision Sciences, 51(4), 867-919.
Rajesh, R., & Ravi, V. (2015). Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey–DEMATEL approach. Computers & Industrial Engineering, 87, 126-139.
Rangel, D. A., de Oliveira, T. K., & Leite, M. S. A. (2014). Supply chain risk classification: discussion and proposal. International Journal of Production Research, 1-20.
Samvedi, A., Jain, V., & Chan, F. T. S. (2013). Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS. International Journal of Production Research, 51(8), 2433-2442.
Schmitt, A. J., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1), 22-32.
Sheffi, Y. (2005). A Supply Chain View of the Resilient Enterprise. MIT Sloan management review, Fall 2005, 41-48.
Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., . . . Zhang, D. (2015). Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain. Interfaces, 45(5), 375-390.
Singhal, P., Agarwal, G., & Mittal, M. L. (2011). Supply chain risk management: review, classification and future research directions. International Journal of Business Science and Applied Management, 6(3), 15-42.
Sodhi, M. S., Son, B.-G., & Tang, C. S. (2012). Researchers' Perspectives on Supply Chain Risk Management. Production and Operations Management, 21(1), 1-13.
Tang, O., & Nurmaya Musa, S. (2011). Identifying risk issues and research advancements in supply chain risk management. International Journal of Production Economics, 133(1), 25-34.
Tummala, R., & Schoenherr, T. (2011). Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP). Supply Chain Management: An International Journal, 16(6), 474 - 483.
Wagner, S. M., Mizgier, K. J., & Arnez, P. (2014). Disruptions in tightly coupled supply chain networks: the case of the US offshore oil industry. Production Planning & Control, 25(6), 494-508.
Wang, X., Chan, H. K., Yee, R. W. Y., & Diaz-Rainey, I. (2012). A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. International Journal of Production Economics, 135(2), 595-606.