Evaluation of Critical Success Factors for Antifragile Supply Chains Using Delphi and Fuzzy QFD Methods

• Autorzy: Maden Ayça , Özceylan Eren , Muhacir Dilara , Mrugalska Beata

Identyfikatory

DOI

10.24425/mper.2024.151484

• Języki publikacji: EN

Abstrakty

EN

The antifragile supply chain (ASC) provides a significant advantage compared to traditional systems, particularly when considering the impacts of unexpected crises such as COVID-19. Such challenging events highlight the value of the ASCs that enable businesses to navigate crises turn these challenges into opportunities, and continuously strengthen their structures. While resilience strategies gain attention, practical studies on ASCs are limited. This study applies a case study to examine critical success factors for ASCs, demonstrating the practical application of the Delphi and Fuzzy QFD method within Boyar Kimya, a textile manufac turer. By understanding the relationships between customer needs and critical success factors, the analysis contributes to determining and effectively implementing ASC strategies. The study concludes by unveiling key success factors, including SC risk management, information sharing, proactive management, efficient knowledge processes, collaboration, and innovation strategies. This research provides a valuable roadmap for enhancing SC efficiency and sus taining a competitive advantage.

Słowa kluczowe

EN

antifragile supply chain; Delphi; fuzzy QFD; critical success factors; supply chain risks

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2024
• Tom: Vol. 15, No. 3
• Strony: 1--12
• Opis fizyczny: Bibliogr. 49 poz., rys., tab.

Twórcy

autor

Maden Ayça

• Beykent University, Industrial Engineering Department, Türkiye

• https://orcid.org/0000-0002-8239-3084

autor

Özceylan Eren

• Gaziantep Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Gaziantep 27310, Türkiye
• Department of Technical Sciences, Western Caspian University, Baku, Azerbaijan

• https://orcid.org//0000-0002-5213-6335+

autor

Muhacir Dilara

• BOYAR Kimya Company, R&D Department, Türkiye

• https://orcid.org/0000-0003-4923-4652

autor

Mrugalska Beata

• Poznan University of Technology, Faculty of Engineering Management

• https://orcid.org/0000-0001-9827-9449

Bibliografia

• Agarwal, N., Seth, N., & Agarwal, A. (2022). Selecting Capabilities to Mitigate Supply Chain Resilience Barriers for an Industry 4.0 Manufacturing Company: An AHP-Fuzzy Topsis Approach. Journal of Advanced Manufacturing Systems, 21 (1), 55–83. DOI: 10.1142/S0219686721500426
• Ajalli, M., Saberifard, N., & Zinati, B. (2021). Evaluation and ranking the resilient suppliers with the combination of decision making techniques. Management and Production Engineering Review, 12 (3), 129–140. DOI: 10.24425/mper.2021.137685
• Ali, A., Mahfouz, A., & Arisha, A. (2017). Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Management, 22 (1), 16-39. DOI: 10.1108/SCM-06-2016-0197
• Ali, I., & Gölgeci, I. (2019). Where is supply chain resilience research heading? A systematic and cooccurrence analysis. International Journal of Physical Distribution and Logistics Management, 49 (8), 793–815. DOI: 10.1108/IJPDLM-02-2019-0038
• Aven, T. (2015). The concept of antifragility and its implications for the practice of risk analysis. Risk Analysis, 35 (3), 476–483. DOI: 10.1111/risa.12279
• Benaben, F., Montreuil, B., Gou, J., Li, J., Lauras, M., Koura, I., & Mu, W. (2019). A tentative framework for risk and opportunity detection in a collaborative environment based on data interpretation. Proceedings of the Annual Hawaii International Conference on System Sciences, 2019 -January, 3056–3065. DOI: 10.24251/hicss.2019.369
• Bevilacqua, M., Ciarapica, F.E., & Giacchetta, G. (2006). A fuzzy-QFD approach to supplier selection. Journal of Purchasing and Supply Management, 12 (1), 14–27. DOI: 10.1016/J.PURSUP.2006.02.001
• Boz, E., Çizmecioğlu, S., & Çalık, A. (2022). A Novel MDCM Approach for Sustainable Supplier Selection in Healthcare System in the Era of Logistics 4.0. Sustainability (Switzerland), 14 (21). DOI: 10.3390/su142113839
• Brandon-Jones, E., Squire, B., Autry, C.W., & Petersen, K.J. (2014). A Contingent Resource-Based Perspective of Supply Chain Resilience and Robustness. Journal of Supply Chain Management, 50 (3), 55–73. DOI: 10.1111/jscm.12050
• Bravo, O., & Hernández, D. (2021). Measuring organizational resilience: Tracing disruptive events facing unconventional oil and gas enterprise performance in the Americas. Energy Research and Social Science, 80 (July). DOI: 10.1016/j.erss.2021.102187
• Brown, P.G. (1991). QFD: Echoing the Voice of the Customer. AT&T Technical Journal, 70 (2), 18–32. DOI: 10.1002/j.1538-7305.1991.tb00342.x
• Carvalho, H., Azevedo, S.G., & Cruz-Machado, V. (2012). Agile and resilient approaches to supply chain management: Influence on performance and competitiveness. Logistics Research, 4 (1–2), 49–62. DOI: 10.1007/s12159-012-0064-2
• Çevik Onar, S., Büyüközkan, G., Öztayşi, B., & Kahraman, C. (2016). A new hesitant fuzzy QFD approach: An application to computer workstation selection. Applied Soft Computing, 46, 1–16. DOI: 10.1016/J.ASOC.2016.04.023
• Delbufalo, E. (2022). Disentangling the multifaceted effects of supply base complexity on supply chain agility and resilience. International Journal of Physical Distribution and Logistics Management, 52 (8), 700–721. DOI: 10.1108/IJPDLM-07-2021-0302
• Dohale, V., Gunasekaran, A., Akarte, M., & Verma, P. (2021). An integrated Delphi-MCDM-Bayesian Network framework for production system selection. International Journal of Production Economics, 242 (January), 108296. DOI: 10.1016/j.ijpe.2021.108296
• Emovon, I., Norman, R.A., & Murphy, A.J. (2018). Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing, 29 (3), 519–531. DOI: 10.1007/s10845-015-1133-6
• Fayezi, S., Zutshi, A., & O’Loughlin, A. (2017). Understanding and Development of Supply Chain Agility and Flexibility: A Structured Literature Review. International Journal of Management Reviews, 19 (4), 379–407. DOI: 10.1111/ijmr.12096
• Gligor, D., Gligor, N., Holcomb, M., & Bozkurt, S. (2019). Distinguishing between the concepts of supply chain agility and resilience: A multidisciplinary literature review. International Journal of Logistics Management, 30 (2), 467–487. DOI: 10.1108/IJLM10-2017-0259
• Gölgeci, I., Arslan, A., Dikova, D., & Gligor, D.M. (2020). Resilient agility in volatile economies: institutional and organizational antecedents. Journal of Organizational Change Management, 33 (1), 100–113. DOI: 10.1108/JOCM-02-2019-0033
• Gölgeci, I., & Kuivalainen, O. (2020). Does social capital matter for supply chain resilience? The role of absorptive capacity and marketing-supply chain management alignment. Industrial Marketing Management, 84, 63–74. DOI: 10.1016/J.INDMARMAN.2019.05.006
• Gong, Y. & Janssen, M. (2012). From policy implementation to business process management: Principles for creating flexibility and agility. Government Information Quarterly, 29 (SUPPL. 1), S61–S71. DOI: 10.1016/j.giq.2011.08.004
• Größler, A. (2020). A managerial operationalization of antifragility and its consequences in supply chains. Systems Research and Behavioral Science, 37 (6), 896–905. DOI: 10.1002/sres.2759
• Haktanır, E., & Kahraman, C. (2019). A novel intervalvalued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development. Computers & Industrial Engineering, 132, 361–372. DOI: 10.1016/J.CIE.2019.04.022
• Halkos, G., Skouloudis, A., Malesios, C., & Evangelinos, K. (2018). Bouncing Back from Extreme Weather Events: Some Preliminary Findings on Resilience Barriers Facing Small and Medium-Sized Enterprises. Business Strategy and the Environment, 27 (4), 547–559. DOI: 10.1002/bse.2019
• Hanss, M. (2005). Applied Fuzzy Arithmetic. In Applied Fuzzy Arithmetic. DOI: 10.1007/b138914
• Hsu, C.H., Li, M.G., Zhang, T.Y., Chang, A.Y., Shangguan, S.Z., & Liu, W.L. (2022). Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework. Mathematics, 10 (8), 1–35. DOI: 10.3390/math10081233
• Hsu, C.H., Yu, R.Y., Chang, A.Y., Chung, W.H., & Liu, W.L. (2021). Resilience-enhancing solution to mitigate risk for sustainable supply chain-an empirical study of elevator manufacturing. Processes, 9 (4). DOI: 10.3390/pr9040596
• Jones, K.H. (2015). Antifragile Systems: An Enabler for System Engineering of Elegant Systems. 30. https://ntrs.nasa.gov/search.jsp?R=20160007433 2018-03-06T19:26:20+00:00Z%0AAntifragile
• Lee, H.L. (2004). The Triple-A Supply Chain.
• Liu, H.T. (2009). The extension of fuzzy QFD: From product planning to part deployment. Expert Systems with Applications, 36 (8), 11131–11144. DOI: 10.1016/j.eswa.2009.02.070
• Liu, H.T., & Wang, C.H. (2010). An advanced quality function deployment model using fuzzy analytic network process. Applied Mathematical Modelling, 34 (11), 3333–3351. DOI: 10.1016/J.APM.2010.02.024
• Liu, J., Gu, B., & Chen, J. (2023). Enablers for maritime supply chain resilience during pandemic: An integrated MCDM approach. Transportation Research Part A: Policy and Practice, 175, 103777. DOI: 10.1016/J.TRA.2023.103777
• Mohammed, A., Harris, I., Soroka, A., & Nujoom, R. (2019). A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design. Computers & Industrial Engineering, 127, 297–312. DOI: 10.1016/J.CIE.2018.09.052
• Nazari-Shirkouhi, S., Tavakoli, M., Govindan, K., & Mousakhani, S. (2023). A hybrid approach using Znumber DEA model and Artificial Neural Network for Resilient supplier Selection. Expert Systems with Applications, 222, 119746. DOI: 10.1016/J.ESWA.2023.119746
• Nikookar, E., Varsei, M., & Wieland, A. (2021). Gaining from disorder: Making the case for antifragility in purchasing and supply chain management. Journal of Purchasing and Supply Management, 27 (3), 100699. DOI: 10.1016/j.pursup.2021.100699
• Nikookar, H., Takafile, M., Ajalli, J., Sahebi, D., & Kantola, J. (2014). A Qualitative Approach for Assessing Resiliency in Supply Chains. Management and Production Engineering Review, 5 (4), 36–45. DOI: 10.2478/mper-2014-0034
• Okorie, O., Subramoniam, R., Charnley, F., Patsavellas, J., Widdifield, D., & Salonitis, K. (2020). Manufacturing in the Time of COVID-19: An Assessment of Barriers and Enablers. IEEE Engineering Management Review, 48 (3), 167–175. DOI: 10.1109/ EMR.2020.3012112
• Patel, B.S. & Sambasivan, M. (2022). A systematic review of the literature on supply chain agility. Management Research Review, 45 (2), 236–260. DOI: 10.1108/MRR-09-2020-0574
• Priyadarshini, J., Singh, R.K., Mishra, R., & Bag, S. (2022). Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach. Operations Management Research, 567–588. DOI: 10.1007/s12063-022-00259-7
• Rajesh, R. (2018). Measuring the barriers to resilience in manufacturing supply chains using Grey Clustering and VIKOR approaches. Measurement, 126, 259–273. DOI: 10.1016/J.MEASUREMENT.2018.05.043
• Rehman, O. ur, & Ali, Y. (2022). Enhancing healthcare supply chain resilience: decision-making in a fuzzy environment. International Journal of Logistics Management, 33 (2), 520–546. DOI: 10.1108/IJLM01-2021-0004
• Sharma, A., Adhikary, A., & Borah, S.B. (2020). Covid19’s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data. Journal of Business Research, 117, 443–449. DOI: 10.1016/J.JBUSRES.2020.05.035
• Sun, J., Wang, H., & Cui, Z. (2023). Alleviating the Bauxite Maritime Supply Chain Risks through Resilient Strategies: QFD-MCDM with Intuitionistic Fuzzy Decision Approach. Sustainability (Switzerland), 15 (10). DOI: 10.3390/su15108244
• Taleb, N.N. (2012). Antifragile: Things that gain from disorder. Random House Audio.
• Taleb, N.N. & Douady, R. (2013). Mathematical definition, mapping, and detection of (anti)fragility. Quantitative Finance, 13 (11), 1677–1689. DOI: 10.1080/14697688.2013.800219
• Temponi, C., Yen, J., & Tiao, W.A. (1999). House of quality: A fuzzy logic-based requirements analysis. European Journal of Operational Research, 117 (2), 340–354. DOI: 10.1016/S0377-2217(98)00275-6
• The National Association of Manufacturers. (2020). NAM CORONAVIRUS OUTBREAK SPECIAL SURVEY. https://www.nam.org/wp-content/uploads/2020/03/NAM-SPECIAL-CORONA-SURVEY.pdf
• Verhulsta, E. (2014). Applying Systems and Safety Engineering Principles for Antifragility. Procedia Computer Science, 32, 842–849. DOI: 10.1016/J.PROCS.2014.05.500
• White, L.H. (2013). Antifragile banking and monetary systems. Cato Journal, 33 (3), 471–484.