A Technology Enabled Framework for Mitigating Risk during Supply chain disruptions in a pandemic scenario

Document Type : SI; Managing SCL in COVID-19

Authors

1 IPE Hyderabad, Telagana, India

2 Jaipuria Institute of Management, Noida, India

3 Jaipuria Institute of Management, Jaipur, India

Abstract

At present supply chains are dynamic and interactive in nature which integrates suppliers, manufacturers, distributors, and consumers. An important objective of supply chain management is to ensure that each supply chain partner is in the coordination with others so that supply chain potential and enhanced surplus can be realized in sales. In general, this coordination breaks due to distrust, misinformation, poor logistics and transportation infrastructure; however, in specific cases like Covid-19, it arises due to uncertainties caused by various types of risks such as delays and disruptions. During pandemic Covid-19 global supply chains have been distorted badly due to multiple lockdowns and country specific decisions to contain the spread of coronavirus. For dealing with such pandemic situation in future, we have learned and proposed some of the strategies from literature and practice that a supply chain manager can think of to minimize supply chain disruptions during a pandemic. These supply chain strategies include Resilience, Outsourcing/Offshoring, Agility, and Digitalization. For helping in decision making to the practitioners, we have applied Best Worst Method (BWM) to evaluate these strategies during pandemic times and found that Digitalization strategy (0.574) has been most differentiating among the proposed four strategies in a pandemic scenario; whereas, Outsourcing/Offshoring strategy is most hampered/ineffective during such times.

Keywords


Abadi, F., Sahebi, I., Arab, A., Alavi, A., and Karachi, H. (2018). Application of best-worst method in evaluation of medical tourism development strategy. Decision Science Letters, Vol. 7(1), pp. 77-86.
Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., and Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production, Vol. 153, pp.242-252.
Ahmadi, H.B., Kusi-Sarpong, S. and Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best-Worst Method, Resources, Conservation and Recycling, Vol. 126, pp. 99-106.
Akter S, Wamba SF, Gunasekaran A, Dubey R, Childe SJ (2016) How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, Vol. 182, pp.113–131.
Askarifar, K., Motaffef, Z., and Aazaami, S. (2018). An investment development framework in Iran's seashores using TOPSIS and best-worst multi-criteria decision making methods. Decision Science, Letters, Vol. 7(1), pp. 55-64.
Baghalian, A., Rezapour, S., and Farahani, R. Z. (2013). Robust supply chain network design with service level against disruption and demand uncertainties: A real-life case. European Journal of Operational Research, Vol. 227(1), pp. 199-215..
Beske, P., Land, A., and Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. International Journal of Production Economics, Vol. 152, pp. 131-143.
Bhattacharya, S. (2020), Dealing with global supply chain breaks, Business Times (Singapore), 11-11. Research Collection Lee Kong Chian School of Business, Available at: https://ink.library.smu.edu.sg/lkcsb_research/6562.
Biswas S, Sen J (2016) A Proposed Architecture for Big Data Driven Supply Chain Analytics. International Journal of Supply Chain Management, pp.1–24.
Bradenburg, M., Govindan, K., Sarkis, J., and Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, Vol. 233(2), pp. 299-312.
Cao, E., Wan, C., and Lai, M. (2013). Coordination of a supply chain with one manufacturer and multiple competing retailers under simultaneous demand and cost disruptions, International Journal of Production Economics, Vol. 141(1), pp. 425- 433.
Chauhan, A., Kaur, H., Yadav, S., and Jakhar, S. K. (2020). A hybrid model for investigating and selecting a sustainable supply chain for agri-produce in India. Annals of Operations Research, Vol. 290(1), pp. 621-642.
Chopra, S. and M.S. Sodhi (2014) “Reducing the Risk of Supply Chain Disruptions” MIT Sloan Management Review Spring, pp. 73-80.
Christopher, M. (2016) Logistics and Supply Chain Management (5th Edition) Pearson.
Christopher, M. and Holweg, M. (2011) “Supply Chain 2.0: Managing Supply Chains in the Era of Turbulence” International Journal of Physical Distribution and Logistics Management, Vol. 41(1), pp 63-82.
Christopher, M. and Peck, H (2004) “Building the Resilient Supply Chain”, International Journal of Logistics Management, Vol. 15(2), pp. 1-13.
Dubey, R., Gunasekaran, A., and Childe, S. J. (2015). The design of a responsive sustainable supply chain network under uncertainty. International Journal of Advanced Manufacturing Technology, 80(1–4), pp. 427–445.
Faisal, M.N., Banwet, D.K. and Shankar, R. (2006), ―Mapping supply chains on risk and customer sensitivity dimensions‖, Industrial Management & Data Systems, Vol. 106(5-6), pp. 878-95.
Fan H, Cheng TCE, Li G, Lee PKC (2016) The Effectiveness of Supply Chain Risk Information Processing Capability: An Information Processing Perspective. IEEE Transactions on Engineering Management, Vol. 63(4), pp. 414–425.
Fan H, Li G, Sun H, Cheng T (2017). An information processing perspective on supply chain risk management: Antecedents, mechanism, and consequences. International Journal of Production Economics, Vol. 185, pp. 63–75.
Fine, C.H. Clock Speed: Wining industry control in the age of temporary advantage. Reading, M.A: Perseus Books, 1998.
Ghaffari, S., Arab, A., Nafari, J., and Manteghi, M. (2017). Investigation and evaluation of key success factors in technological innovation development based on BWM. Decision Science Letters, Vol. 6(3), pp. 295-306.
Ghosh D (2015) Big Data in Logistics and Supply Chain Management - A rethinking step. 2015 International Symposium on Advanced Computing and Communication (lSACC ).
Goh RSM, Wang Z, Yin X, Fu X, Ponnambalam L, Lu S, Li X (2013) Risk Visibility Supply chain visualization with risk management and real-time monitoring. In: IEEE International Conference on Automation Science and Engineering (CASE), 2013: 17 - 20 Aug. 2013, Madison, WI, USA. IEEE, Piscataway, NJ, pp. 207–212.
Güller M, Koc E, Hegmanns T, Henke M, Noche B (2015) A Simulation-based Decision Support Framework for the Real-time Supply Chain Risk Management. International Journal of Advanced Logistics, Vol. 4(1), pp.17–26.
Gurumurthy, A., Soni, G., Prakash, S. and Badhotiya, G. K. (2013), Review on Supply Chain Management Research: An Indian Perspective‖, IIM Kozhikode Society & Management Review, Vol. 2(1), pp. 1-19.
Haren, P., and Simchi-Levi, D. (2020). ‘How Coronavirus could impact the global supply chain by mid-March’. Harvard Business Review. Retrieved May 13, 2020, from https://hbr.org/2020/ 02/how-coronavirus-could-impact-the-global-supply-chain-bymid-march.
Hillman M, Keltz H (2007) Managing Risk in the Supply Chain - A Quantitative Study. http://www.scrlc.com/articles/AMR Managing Risk.pdf. Accessed 27 December 2016.
Ho W, Zheng T, Yildiz H, Talluri S (2015) Supply chain risk management: a literature review. International Journal of Production Research, Vol. 53(16), pp. 5031–5069.
Hopkin, P. (2014). Fundamentals of risk management: Understanding, evaluating and implementing effective risk management. London, UK: Kogan Page.
Ittmann HW (2015). The impact of big data and business analytics on supply chain management. Journal of Transport and Supply Chain Management, Vol. 9(1), pp. 1-9.
Ivanov D (2019) Disruption tails and revival policies: a simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Comput Ind Eng, Vol. 127:558–570. https ://doi.org/10.1016/j.cie.2018.10.043.
Ivanov D (2020).  Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp Res Part E. https ://doi.org/10.1016/j.tre.2020.10192 2.
Ivanov D, Dolgui A (2020) Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research. https ://doi.org/10.1080/00207 543.2020.17507 27.
Ivanov, D. (2019). Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Computers & Industrial Engineering, Vol. 127, pp. 558–570.
Ivanov, D. (2020). Viable supply chain model: integrating agility, resilience and sustainability perspectives. Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03640-6.
Ji, G., C. Zhu. (2008). Study on supply chain disruption risk management strategies and model, in Proceedings of the IEEE 2008 International Conference on Service Systems and service management, June 30-July 2, Melbourne, Australia.
Johnson, M.E., (2001). Learning from toys: lessons in managing supply chain risk from the toy industry‖, California Management Review, Vol. 43(3), pp. 106–124.
Juttner, U., H. Peck and Christopher, M. (2003) “Supply Chain Risk Management: Outlining an agenda for future research” International Journal of Logistics: Research Applications Vol. 15(2), pp. 197-210.
Kaplan, R. S., and Mikes, A. (2012). Managing risks: A new framework. Harvard Business Review, 90(6). Kar, A.K., (2010). Risk in supply chain management. http:// business-fundas.com/2010/riskin-supply-chain-management.
Kırılmaz O, Erol S (2017). A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks. Journal of Purchasing and Supply Management, Vol. 23(1), pp. 54–65.
Kleindorfer, P.R., G.H. Saad. (2005) Managing disruption risks in supply chains. Production and Operations Management, Vol. 14(1), pp. 53-68.
Manuj, I., J. Mentzer.( 2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, Vol. 38(3), pp. 192-223.
Micheli, G.J., Mogre, R. and Perego, A., (2014). How to choose mitigation measures for supply chain risks. International Journal of Production Research, Vol. 52(1), pp. 117-129.
Mou, Q., Xu, Z., and Liao, H. (2016). An intuitionistic fuzzy multiplicative best-worst method for multicriteria group decision making. Information Sciences, Vol. 374, pp. 224-239.
Niesen T, Houy C, Fettke P, Loos P (2016) Towards an Integrative Big Data Analysis Framework for Data-Driven Risk Management in Industry 4.0. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 5065–5074.
Norrman A, Jansson U (2004) Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident. International Journal of Physical Distribution & Logistics Management, Vol. 34(5), pp. 434–456.
Peck, H. (2005). Drivers of supply chain vulnerability: an integrated framework. International Journal of Physical Distribution & Logistics Management, Vol. 35(4), pp. 210-232.
Pickett, C.B. (2003). Strategies for maximizing supply chain resilience: Learning from the past to prepare for the future. MSc Thesis. Master of Engineering in Logistics, Engineering Systems Division, Massachusetts Institute of Technology. Boston.
Pickett, C.B. (2006). Prepare for supply chain disruptions before they hit. Logistics Today, Vol. 47(6), pp. 22-25.
Ren, J. (2018). Technology selection for ballast water treatment by multi-stakeholders: a multi-attribute decision analysis approach based on the combined weights and extension theory, Chemosphere, Vol. 191, pp. 747-760.
Ren, J., Liang, H., and Chan, F. T. (2017). Urban sewage sludge, sustainability, and transition for Eco-City: Multi-criteria sustainability assessment of technologies based on best-worst method. Technological Forecasting and Social Change, Vol. 116, pp. 29-39.
Rezaei, J., (2015). Best-worst multi-criteria decision-making method, Omega, Vol. 53, pp. 49-57.
Rezaei, J., (2016). Best-worst multi-criteria decision-making method: some properties and a linear model, Omega, Vol. 64, pp. 126-130.
Rezaei, J., Fahim, P.B., Tavasszy, L., (2014). Supplier selection in the airline retail industry using a funnel methodology: conjunctive screening method and fuzzy AHP. Expert System Application, Vol. 41(18), pp. 8165-8179.
Rice, B. and Caniato, F., Supply chain response to terrorism: creating resilient and secure supply chains. Supply Chain Response to Terrorism Project Interim Report, MIT Centre for Transportation and Logistics, MIT, MA, 2003.
Ritchie, B., C. Brindley. 2007. Supply chain risk management and performance. A guiding framework for future development, International Journal of Operations & Production Management, Vol. 27(3), pp. 303-322.
Sahebi, I.G., Arab, A. and Moghadam, M.R.S. (2017). Analyzing the barriers to humanitarian supply chain management: a case study of the Tehran Red Crescent societies, International Journal of Disaster Risk Reduction, Vol. 24, pp.232-241.
Salimi, N. and Rezaei, J. (2016). Measuring efficiency of university-industry PhD projects using Best-Worst Method, Scientometrics, Vol. 109(3), pp.1911-1938.
Salimi, N. and Rezaei, J. (2018). Evaluating firms’ R&D performance using Best-Worst Method, Evaluation and Program Planning, Vol. 66, pp.147-155.
Salimi, N., and Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and program planning, Vol. 66, pp. 147-155.
Salo, A.A. and Hämäläinen, R.P. (1997), “On the measurement of preferences in the analytic hierarchy process”, Journal of Multi-Criteria Decision Analysis, Vol. 6(6), pp. 309-319.
Scholten K, Sharkey-Scott P, Fynes B (2014) Mitigation processes—antecedents for building supply chain resilience. Supply Chain Management, Vol. 19(2), pp. 211–228.
Schröder M, Indorf M, Kersten W (2014) Industry 4.0 and its Impact on Supply Chain Risk Management. In: Kabashkin IV, Yatskiv IV (eds) Proceedings of the 14th International Conference "Reliability and Statistics in Transportation and Communication" (RelStat'14), pp. 114–125.
Serrai, W., Abdelli, A., Mokdad, L., and Hammal, Y. (2017). Towards an efficient and a more accurate web service selection using MCDM methods. Journal of Computational Science, Vol. 22, pp. 253-267.
Sheffi Y (2005) Supply chain strategy—building a resilient supply chain. Harvard Business Review, Vol. 1(8), pp.1–4.
Sheffi, Y. and J.B. Rice (2005) “A Supply Chain View of the Resilient Enterprise” MIT Sloan Management Review Vol. 47(1), pp. 41-48.
Sheu, J. B., and Kundu, T. (2018). Forecasting time-varying logistics distribution flows in the One Belt-One Road strategic context. Transportation Research Part E: Logistics and Transportation Review, Vol. 117, pp. 5-22.
Skipper, JB, Hanna, JB (2009) Minimizing supply chain disruption risk through enhanced flexibility. International Journal of Physical Distribution & Logistics Management, Vol. 39(5), pp. 404–427.
Stecke, K.E., S. Kumar. (2006). Sources of supply chain disruptions. Factors that breed vulnerability, and mitigating strategies. School of Management, University of Texas, Dallas. Retrieved January 13, 2009, http://som.utdallas.edu/ centers/ c4isn/documents/ SOM200674...Strategies.pdf.
Svensson, G.A., 2000. Conceptual framework for the analysis of vulnerability in supply chains. International Journal of Physical Distribution and Logistics Management, Vol. 30(9), pp.731–749.
Tang, C. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, Vol. 103(2), pp. 451-488.
Tang, C. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, Vol. 9(1), pp. 33-45.
Terrados, J., Almonacid, G., and PeRez-Higueras, P. (2009). Proposal for a combined methodology for renewable energy planning. Application to a Spanish region. Renewable and Sustainable Energy, Reviews, Vol. 13(8), pp. 2022-2030.
Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, Vol. 52(5), pp. 639-657.
Tummala, R. and Schoenherr, T. (2011), Assessing and managing risks using the supply chain risk management process (SCRMP), Supply Chain Management: An International Journal, Vol. 16(6), pp. 474 – 483.
Vahidi, F., Torabi, S.A. and Ramezankhani, M.J. (2018). Sustainable supplier selection and order allocation under operational and disruption risks”, Journal of Cleaner Production, Vol. 174, pp.1351-1365.
Van de Kaa, G., Fens, T. and Rezaei, J. (2018). Residential grid storage technology battles: a multi-criteria analysis using BWM, Technology Analysis & Strategic Management, Vol. 31(1), pp.1431-13.
Van de Kaa, G., Kamp, L. and Rezaei, J. (2017). Selection of biomass thermochemical conversion technology in the Netherlands: a Best-Worst Method approach, Journal of Cleaner Production, Vol. 166, pp.14332-39.
Wan Ahmad, N.K.W., Rezaei, J., Sadaghiani, S. and Tavasszy, L.A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best-Worst Method, Journal of Cleaner Production, Vol. 153, pp.143242-252.
Wang, Y., Gilland, W., Tomlin, B., (2010), Mitigating supply risk: dual sourcing or process improvement, Manufacturing & Service Operations Management, Vol. 12(3), pp. 489–510.
Yin S, Kaynak O (2015) Big Data for Modern Industry: Challenges and Trends [Point of View]. Proceedings of the IEEE Vol. 103(2), pp.143–146.