Fuzzy AHP Approach for Supply Chain Strategy Selection : A Post - Pandemic Scenario
DOI:
https://doi.org/10.17010/pijom/2023/v16i3/169913Keywords:
Analytic Hierarchical Process
, Fuzzy Analytic Hierarchy Process, Multi-Criteria Decision Making, Supply Chain Disruption, Supply Chain Risk, Logistics Cost, Multi-Sourcing, Buffering.JELClassification Codes
, C60, L62, L91, M11, M16Paper Submission Date
, July 10, 2022, Paper sent back for Revision, February 5, 2023, Paper Acceptance Date, February 15, Paper Published Online, March 15, 2023Abstract
Supply chains have been severely disrupted globally due to the COVID-19 pandemic. The paper examined the strategic responses of automobile firms for meeting supply chain challenges they face post-pandemic. Data were collected using a specifically designed structured questionnaire from supply chain experts working with leading automobile manufacturing firms in India. The fuzzy analytic hierarchy process (FAHP), as a part of a multi-criteria decision-making model using R programming, was applied to identify and rank the choice of supply strategies using various criteria, such as lead time, logistics cost (holding cost, carrying cost, warehousing cost, handling cost), and the need of products. Two-wheeler and four-wheeler manufacturing firms were selected for the study. Logistics cost was found to be a dominant criterion, followed by a demand for products and lead time, which helped select an appropriate supply chain strategy. Buffering was observed to be the best strategic choice, and automation and robotics applications were the least preferred ones both for two-wheelers and four-wheeler manufacturing companies. The findings would be helpful to both practitioners and researchers in evaluating diverse strategic choices, especially under the risk and disruptions faced by business firms in the supply chain.Downloads
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Accenture. (2020, April 7). COVID-19: Mobilizing the automotive industry now. https://www.accenture.com/cn-en/insights/automotive/coronavirus-automotive-rapid-response
Anbumozhi, V., Kimura, F., & Thangavelu, S. M. (2020). Global supply chain resilience: Vulnerability and shifting risk management strategies. In V. Anbumozhi, F. Kimura, & S. Thangavelu, (eds.). Supply chain resilience. Springer. https://doi.org/10.1007/978-981-15-2870-5_1
Araz, O. M., Choi, T.-M., Olson, D. L., & Salman, F. S. (2020). Data analytics for operational risk management. Decision Sciences, 51(6), 1316–1319. https://doi.org/10.1111/deci.12443
Barrios, K. (2020). Top 10 global supply chain risks. https://supplychaingamechanger.com/top-10-global-supply-chain-risks/
Belhadi, A., Kamble, S., Jabbour, C. J., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change, 163, 120447. https://doi.org/10.1016/j.techfore.2020.120447
Cabral, I., Grilo, A., & Cruz-Machado, V. (2012). A decision-making model for lean, agile, resilient and green supply chain management. International Journal of Production Research, 50(17), 4830–4845. https://doi.org/10.1080/00207543.2012.657970
Carvalho, H., Duarte, S., & Cruz Machado, V. (2011). Lean, agile, resilient and green: Divergencies and synergies. International Journal of Lean Six Sigma, 2(2), 151–179. https://doi.org/10.1108/20401461111135037
Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operation Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
Childerhouse, P., Hermiz, R., Mason-Jones, R., Popp, A., & Towill, D. R. (2003). Information flow in automotive supply chains – present industrial practice. Industrial Management & Data Systems, 103(3), 137–149. https://doi.org/10.1108/02635570310465625
Chopra, S., & Sodhi, M. S. (2014). Reducing the risk of supply chain disruptions. MIT Sloan Management Review. https://sloanreview.mit.edu/article/reducing-the-risk-of-supply-chain-disruptions/
Ganeshan, H., & Suresh, P. (2017). An empirical analysis on supply chain problems, strategy, and performance with reference to SMEs. Prabandhan: Indian Journal of Management, 10(11), 19–30. https://doi.org/10.17010/pijom/2017/v10i11/119400
Ganguly, K., & Kumar, G. (2019). Supply chain risk assessment: A fuzzy AHP approach. Operations and Supply Chain Management, 12(1), 1–13. http://doi.org/10.31387/oscm0360217
Ghasemzadeh, F., Pishdar, M., & AntucheviÄienÄ—, J. (2017). Prioritization of petroleum supply chains' disruption management strategies using combined framework of BSC approach, fuzzy AHP and fuzzy Choquet integral operator. Journal of Business Economics and Management, 18(5), 897–919. https://doi.org/10.3846/16111699.2017.1380075
Gurtu, A., & Johny, J. (2021). Supply chain risk management: Literature review. Risks, 9(1), 16. https://doi.org/10.3390/risks9010016
Guru, S., Bhatt, N., & Agrawal, N. (2021). Prioritization of dimensions of online trust using analytical hierarchy approach. Indian Journal of Marketing, 51(5–7), 81–92. https://doi.org/10.17010/ijom/2021/v51/i5-7/163886
Harapko, S. (2023, January 6). How COVID-19 impacted supply chains and what comes next. E&Y. https://www.ey.com/en_gl/supply-chain/how-covid-19-impacted-supply-chains-and-what-comes-next
Joshi, A., Sunny, N., & Vashisht, S. (2017). Recent trends in HRM: A qualitative analysis using AHP. Prabandhan: Indian Journal of Management, 10(10), 41–52. https://doi.org/10.17010/pijom/2017/v10i10/118814
Kilincci, O., & Onal, S. A. (2011). Fuzzy AHP approach for supplier selection in a washing machine company. Expert Systems with Applications, 38(8), 9656–9664. https://doi.org/10.1016/j.eswa.2011.01.159
Kumar, B., & Sharma, A. (2021). Managing the supply chain during disruptions: Developing a framework for decision-making. Industrial Marketing Management, 97, 159–172. https://doi.org/10.1016/j.indmarman.2021.07.007
Kumar, S., & Managi, S. (2020). Does stringency of lockdown affect air quality? Evidence from Indian cities. Economics of Disasters and Climate Change, 4, 481–502. https://doi.org/10.1007/s41885-020-00072-1
Lee, J., & Wright, J. (2020). COVID-19 and shattered supply chains. IBM Institute for Business Value. https://www.ibm.com/thought-leadership/institute-business-value/report/covid-19-supply-chains
Lücker, F., Seifert, R. W., & Biçer, I. (2019). Roles of inventory and reserve capacity in mitigating supply chain disruption risk. International Journal of Production Research, 57(4), 1238–1249. https://doi.org/10.1080/00207543.2018.1504173
Luna, M., Llorente, I., & Cobo, A. (2022). Determination of feeding strategies in aquaculture farms using a multiple-criteria approach and genetic algorithms. Annals of Operations Research, 314, 551–576. https://doi.org/10.1007/s10479-019-03227-w
Mahajan, A., & Chand, P. (2022). COVID-19 and Disparities in India: An analysis. Prabandhan: Indian Journal of Management, 15(12), 54–61. https://doi.org/10.17010/pijom/2022/v15i12/172601
Majeed, R. A., & Sriram, K. V. (2019). Determining the best advertising medium for a footwear company: A case study. Indian Journal of Marketing, 49(5), 21–32. https://doi.org/10.17010/ijom/2019/v49/i5/144022
Mishra, R., Singh, R. K., & Subramanian, N. (2022). Impact of disruptions in agri-food supply chain due to COVID-19 pandemic: Contextualised resilience framework to achieve operational excellence. The International Journal of Logistics Management, 33(3), 926–954. https://doi.org/10.1108/IJLM-01-2021-0043
Moritz, B. (2020). Supply chain disruptions and COVID-19. Supply Chain Management Review, 24(3), 14–17. https://www.scmr.com/article/supply_chain_disruptions_and_covid_19
Mulubrhan, F., Mokhtar, A. A., & Muhammad, M. (2014). Comparative analysis between fuzzy and traditional analytical hierarchy process. MATEC Web of Conferences, 13, 01006. https://doi.org/10.1051/matecconf/20141301006
Pouyakian, M., Khatabakhsh, A., Yazdi, M., & Zarei, E. (2022). Optimizing the allocation of risk control measures using fuzzy MCDM approach: Review and application. In, M. Yazdi (ed.), Linguistic methods under fuzzy information in system safety and reliability analysis. Studies in fuzziness and soft computing (Vol. 414). Springer. https://doi.org/10.1007/978-3-030-93352-4_4
Radivojević, V., & Gajović, V. (2014). Supply chain risk modeling by AHP and fuzzy AHP methods. Journal of Risk Research, 17(3), 337–352. https://doi.org/10.1080/13669877.2013.808689
Raj, A., Mukherjee, A. A., de Sousa Jabbour, A. B., & Srivastava, S. K. (2022). Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned. Journal of Business Research, 142, 1125–1139. https://doi.org/10.1016/j.jbusres.2022.01.037
Rashid, A., & Rokade, V. (2021). Multi-criterion decision making approach to assess retail service quality: A market perspective from Iraq. Prabandhan: Indian Journal of Management, 14(3), 49–63. https://doi.org/10.17010/pijom/2021/v14i3/158156
Rennie, E. (2020, May 8). Five lessons from a crisis. SCM Now, 30(3), 35–38.
Saaty, R. W. (1987). The analytic hierarchy process — What it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8
Schmidt, W., & Raman, A. (2012) When supply-chain disruptions matter (Working Paper No. 13-006). Harvard Business School. http://www.hbs.edu/faculty/Publication%20Files/13-00
Shah, J. (2009). Supply chain management: Text and cases. Pearson Education.
Sharma, R., Shishodia, A., Kamble, S., Gunasekaran, A., & Belhadi, A. (2020). Agriculture supply chain risks and COVID-19: Mitigation strategies and implications for the practitioners. International Journal of Logistics Research and Applications, 1–27. https://doi.org/10.1080/13675567.2020.1830049
Sharma, S., Chadha, S., & Dhar, S. (2018). Measuring the impact of socially responsible supply chains on firm performance. Prabandhan: Indian Journal of Management, 11(10), 22–38. https://doi.org/10.17010/pijom/2018/v11i10/132509
Shaw, K., Shankar, R., Yadav, S. S., & Thakur, L. S. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications, 39(9), 8182–8192. https://doi.org/10.1016/j.eswa.2012.01.149
Shih, W. C. (2020). Global supply chains in a post-pandemic world. Harvard Business Review. https://hbr.org/2020/09/global-supply-chains-in-a-post-pandemic-world
Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., Gusikhin, O., Sanders, M., & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375–390. http://www.jstor.org/stable/43699517
Singh, A., & Nanda, P. (2022). Comparing factors influencing loyal consumption behavior towards fast-food restaurants. Indian Journal of Marketing, 52(6), 41–58. https://doi.org/10.17010/ijom/2022/v52/i6/169835
Sinha, D., & Dey, D. (2018). Supply chain strategies to sustain economic and customer uncertainties. In S. Dhir & Shushil (eds.), Flexible strategies in VUCA markets (pp. 139–153). Springer. https://doi.org/10.1007/978-981-10-8926-8
Sinha, D., Bagodi, V., & Dey, D. (2020). The supply chain disruption framework post COVID-19: A system dynamics model. Foreign Trade Review, 55(4), 511–534. https://doi.org/10.1177/0015732520947904
Sinha, G. K., & Dhingra, D. (2022). Managing supply chain risk and uncertainty in the post-pandemic era: A strategic perspective. In Y. Ramakrishna (ed.), Handbook of research on supply chain resiliency, efficiency, and visibility in the post-pandemic era (pp. 467–487). IGI Global. https://doi.org/10.4018/978-1-7998-9506-0.ch023
Taylor III, B. W. (2017). Introduction to management science (13th ed.). Pearson Education.
Visiongain. (2022). Steel market report 2021-2031. https://www.visiongain.com/report/steel-market-2021/#download_sampe_div
Yıldırım, N., Siyahi, B. T., Özbek, O., Ahioğlu, İ., & Kahya, A. S. (2022). A combined multi-criteria decision-making framework for process-based digitalisation opportunity and priority assessment (DOPA). International Journal of Business Analytics (IJBAN), 9(5), 1–22. https://doi.org/10.4018/IJBAN.298018