Unveiling Supply Chain Nervousness: A Strategic Framework for Disruption Management under Fuzzy Environment
Abstract
:1. Introduction
- Modeling SCNM elements by identifying, assessing, and mitigating SCN factors;
- Determining the SCNM goal and objectives for current and future SC resilience and sustainability;
- Analyzing the pillars of SCNM in terms of strategies, planning, measuring, and continuity;
- Propose a fuzzy-MCDA approach to prioritize SCNM factors;
- Assessing SCN in UAE using the proposed technique and framework, then evaluating and validating the results with the experts and concerned parties.
2. Related Work and Background
2.1. Supply Chain Nervousness
2.2. Fuzzy-ELECTRE Method
3. Materials and Methods
3.1. The Study Tools
3.2. Sample and Population
3.3. Analysis and Evaluation of Nervousness Factors Using Fuzzy-ELECTRE Technique
4. Case Study
4.1. Supply Chain Management in UAE
4.2. SCNM Framework
4.3. SCNM Elements
4.3.1. Identifying SCN Factors
- Demand variations. Client demand variability refers to the unpredictability of consumer demand, which can be driven by factors such as complexity and international differences. The main factors affecting demand variability are volatility, uncertainty, complexity, and ambiguity, and managing it can be a challenge for supply chain executives due to the cost and impact of the bullwhip effect [29].
- Flows interruptions. Supply chains are complex systems that need cooperation from all participants to function effectively. The main supply chain flows are product, negotiation, risk, information, and promotion. Disruptions to the supply chain can have serious consequences and can be caused by various events such as pandemics, conflicts, political instability, financial crises, social unrest, and natural disasters [29,31].
- SGEC factors. The supply chain can be influenced by various factors including social issues, government policies, economic conditions, and cultural attitudes. These factors can impact the growth, safety, and welfare of communities and human rights.
- Unplanned events. The supply chain can be disrupted by unexpected events such as sudden increases in demand, weaknesses in partners, economic shocks, cyberattacks, and natural disasters. The COVID-19 crisis highlights the potential impact of these events on supply and demand [6].
- Business uncertainties. Business uncertainty refers to the difficulty in predicting future outcomes due to changes in the economy, competition, and society. This can result from expanding into new markets, economic uncertainty, or competitors’ actions.
- Operations disruptions. The supply chain and its internal systems and processes are prone to operational issues that can cause disruptions in the manufacture, sale, or distribution of goods.
- SC-ICT vulnerabilities. Information and communication technologies (ICT) play a vital role in the supply chain process as they facilitate connections and improve management of time, cost, and quality. However, ICT also brings security risks, as hackers often target supply chain vendors to gain access to a corporation through a backdoor attack, causing potential harm to operations, finances, and reputation. This has caused concern among supply chain managers and partners, requiring immediate action to address these vulnerabilities.
4.3.2. Assessing SCN
4.3.3. Mitigate SCN
4.4. SCNM Main Goals
4.5. Pillars of SCNM
4.5.1. SCN Strategy
4.5.2. SCN Planning
4.5.3. SCN Measuring
4.5.4. SCN Continuity
5. Results and Discussion
6. Conclusions, Implications, Limitations, and Future Work
6.1. Summary of Findings
6.2. Interpretation and Implications
6.3. Limitations
6.4. Recommendations for Future Research
6.5. Management Insight
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ayan, B.; Güner, E.; Son-Turan, S. Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach. Logistics 2022, 6, 85. [Google Scholar] [CrossRef]
- Mensah, P.; Merkuryev, Y. Developing a resilient supply chain. Procedia Soc. Behav. Sci. 2014, 110, 309–319. [Google Scholar] [CrossRef] [Green Version]
- Antonio, F.; Atayde, J.; Yamzon, M.; Sy, C. An optimization model for the design of supply chains considering disruptions from pandemic uncertainty and infection trends. Clean. Eng. Technol. 2022, 11, 100577. [Google Scholar] [CrossRef]
- Magableh, G.M.; Mistarihi, M. Supply Chain Nervousness Strategies. Available online: https://ssrn.com/abstract=4415220 (accessed on 7 June 2023).
- Ali, S.M.; Rahman, M.H.; Tumpa, T.J.; Rifat, A.A.M.; Paul, S.K. Examining price and service competition among retailers in a supply chain under potential demand disruption. J. Retail. Consum. Serv. 2018, 40, 40–47. [Google Scholar] [CrossRef]
- Raj, A.; Mukherjee, A.A.; de Sousa Jabbour, A.B.L.; Srivastava, S.K. Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned. J. Bus. Res. 2022, 142, 1125–1139. [Google Scholar] [CrossRef]
- Dohale, V.; Ambilkar, P.; Gunasekaran, A.; Verma, P. Supply chain risk mitigation strategies during COVID-19: Exploratory cases of “make-to-order” handloom saree apparel industries. Int. J. Phys. Distrib. Logist. Manag. 2022, 52, 109–129. [Google Scholar] [CrossRef]
- Hernandez, M.C.; Alvarez, A.N.R.; Anguiano, F.I.S. Project management and supply chain 4.0 improvement: The case of infant formulas in the face of the challenge of COVID-19. Procedia Comput. Sci. 2023, 217, 278–285. [Google Scholar] [CrossRef] [PubMed]
- Kaipia, R.; Korhonen, H.; Hartiala, H. Planning nervousness in a demand supply network: An empirical study. Int. J. Logist. Manag. 2006, 17, 95–113. [Google Scholar] [CrossRef] [Green Version]
- Atadeniz, S.N.; Sridharan, S.V. Effectiveness of nervousness reduction policies when capacity is constrained. Int. J. Prod. Res. 2020, 58, 4121–4137. [Google Scholar] [CrossRef]
- Pooya, A.; Fakhlaei, N.; Alizadeh-Zoeram, A. Designing a dynamic model to evaluate lot-sizing policies in different scenarios of demand and lead times in order to reduce the nervousness of the MRP system. J. Ind. Prod. Eng. 2021, 38, 122–136. [Google Scholar] [CrossRef]
- Magableh, G.M.; Mistarihi, M.Z. Causes and effects of supply chain nervousness: Mena case study. Acta Logist. 2022, 9, 223–235. [Google Scholar] [CrossRef]
- Gobind, J. Transport anxiety and work performance. SA J. Hum. Resour. Manag. 2018, 16, 1–7. [Google Scholar] [CrossRef]
- Liu, B.; Ju, T.; Chan, H.K. The diverse impact of heterogeneous customer characteristics on supply chain finance: Empirical evidence from Chinese factoring. Int. J. Prod. Econ. 2022, 243, 108321. [Google Scholar] [CrossRef]
- Wieland, A.; Durach, C.F. Two perspectives on supply chain resilience. J. Bus. Logist. 2021, 42, 315–322. [Google Scholar] [CrossRef]
- Park, A.; Li, H. The effect of blockchain technology on supply chain sustainability performances. Sustainability 2021, 13, 1726. [Google Scholar] [CrossRef]
- Mentzer, J.T.; DeWitt, W.; Keebler, J.S.; Min, S.; Nix, N.W.; Smith, C.D.; Zacharia, Z.G. Defining supply chain management. J. Bus. Logist. 2001, 22, 1–25. [Google Scholar] [CrossRef]
- Lu, X.; Swaminathan, J.M. Supply chain management. In International Encyclopedia of the Social Behavioral Sciences, 2nd ed.; Elsevier: Amsterdam, The Netherlands, 2015; pp. 709–713. [Google Scholar]
- Cousins, P.D.; Lawson, B.; Petersen, K.J.; Fugate, B. Investigating green supply chain management practices and performance: The moderating roles of supply chain ecocentricity and traceability. Int. J. Oper. Prod. Manag. 2019, 39, 767–786. [Google Scholar] [CrossRef]
- Ho, W.; Zheng, T.; Yildiz, H.; Talluri, S. Supply chain risk management: A literature review. Int. J. Prod. Res. 2015, 53, 5031–5069. [Google Scholar] [CrossRef]
- Pai, R.R. Supply Chain Risk Mitigation. International Series in Operations Research & Management Science. 2022. Available online: https://www.semanticscholar.org/paper/Supply-Chain-Risk-Mitigation-Pai/dcd303a905c6a0ddad477db4ac9a91d9debfcf9b (accessed on 5 May 2022).
- Handfield, R.; Sun, H.; Rothenberg, L. Assessing supply chain risk for apparel production in low cost countries using newsfeed analysis. Supply Chain Manag. Int. J. 2020, 25, 803–821. [Google Scholar] [CrossRef]
- Helmold, M.; Yılmaz, A.K.; Dathe, T.; Flouris, T.G. Supply Chain Risk Management. Manag. Prof. 2022, 20, 113879. [Google Scholar]
- Thomas, C.; Chermack, T. Using Scenario Planning to Supplement Supply Chain Risk Assessments. In Revisiting Supply Chain Risk; Springer: Cham, Switzerland, 2019; pp. 37–51. [Google Scholar]
- Shang, Z.; Yang, X.; Barnes, D.; Wu, C. Supplier selection in sustainable supply chains: Using the integrated BWM, fuzzy Shannon entropy, and fuzzy MULTIMOORA methods. Expert Syst. Appl. 2022, 195, 116567. [Google Scholar] [CrossRef]
- Mamun, M. Supply Chain Risk Management in a Digital Era: Evidence from SMEs of Clothing Retailers in Australia. J. Risk Financ. Manag. 2023, 16, 242. [Google Scholar] [CrossRef]
- Moosavi, J.; Fathollahi-Fard, A.M.; Dulebenets, M.A. Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies. Int. J. Disaster Risk Reduct. 2022, 75, 102983. [Google Scholar] [CrossRef] [PubMed]
- Kamalahmadi, M.; Parast, M.M. An assessment of supply chain disruption mitigation strategies. Int. J. Prod. Econ. 2017, 184, 210–230. [Google Scholar] [CrossRef] [Green Version]
- Azadegan, A.; Modi, S.; Lucianetti, L. Surprising supply chain disruptions: Mitigation effects of operational slack and supply redundancy. Int. J. Prod. Econ. 2021, 240, 108218. [Google Scholar] [CrossRef]
- McMaster, M.; Nettleton, C.; Tom, C.; Xu, B.; Cao, C.; Qiao, P. Risk management: Rethinking fashion supply chain management for multinational corporations in light of the COVID-19 outbreak. J. Risk Financ. Manag. 2020, 13, 173. [Google Scholar] [CrossRef]
- El Baz, J.; Ruel, S. Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. Int. J. Prod. Econ. 2021, 233, 107972. [Google Scholar] [CrossRef]
- Gurtu, A.; Johny, J. Supply chain risk management: Literature review. Risks 2021, 9, 16. [Google Scholar] [CrossRef]
- Khan, M.D.; Schaefer, D.; Milisavljevic-Syed, J. Supply Chain Management 4.0: Looking Backward, Looking Forward. Procedia CIRP 2022, 107, 9–14. [Google Scholar] [CrossRef]
- Lambert, D.M.; Cooper, M.C.; Pagh, J.D. Supply chain management: Implementation issues and research opportunities. Int. J. Logist. Manag. 1998, 9, 1–20. [Google Scholar] [CrossRef]
- Yang, J.; Xie, H.; Yu, G.; Liu, M. Antecedents and consequences of supply chain risk management capabilities: An investigation in the post-coronavirus crisis. Int. J. Prod. Res. 2021, 59, 1573–1585. [Google Scholar] [CrossRef]
- Kurdi, B.; Alzoubi, H.; Alshurideh, M.; Alquqa, E.; Hamadneh, S. Impact of supply chain 4.0 and supply chain risk on organizational performance: Empirical evidence from the UAE food manufacturing industry. Uncertain Supply Chain Manag. 2023, 11, 111–118. [Google Scholar] [CrossRef]
- Alshurideh, M.; Alquqa, E.; Alzoubi, H.; Kurdi, B.; Hamadneh, S. The effect of information security on e-supply chain in the UAE logistics and distribution industry. Uncertain Supply Chain Manag. 2023, 11, 145–152. [Google Scholar] [CrossRef]
- Al-Ayed, S.; Al-Tit, A. The effect of supply chain risk management on supply chain resilience: The intervening part of Internet-of-Things. Uncertain Supply Chain Manag. 2023, 11, 179–186. [Google Scholar] [CrossRef]
- Sari, K. A novel multi-criteria decision framework for evaluating green supply chain management practices. Comput. Ind. Eng. 2017, 105, 338–347. [Google Scholar] [CrossRef]
- Guan, Z.; Mou, Y.; Sun, M. Hybrid robust and stochastic optimization for a capital-constrained fresh product supply chain integrating risk-aversion behavior and financial strategies. Comput. Ind. Eng. 2022, 169, 108224. [Google Scholar] [CrossRef]
- Badhotiya, G.K.; Soni, G.; Jain, V.; Joshi, R.; Mittal, S. Assessing supply chain resilience to the outbreak of COVID-19 in Indian manufacturing firms. Oper. Manag. Res. 2022, 15, 1161–1180. [Google Scholar] [CrossRef]
- Tang, C.; Tomlin, B. The power of flexibility for mitigating supply chain risks. Int. J. Prod. Econ. 2008, 116, 12–27. [Google Scholar] [CrossRef] [Green Version]
- Shen, B.; Li, Q. Market disruptions in supply chains: A review of operational models. Int. Trans. Oper. Res. 2017, 24, 697–711. [Google Scholar] [CrossRef] [Green Version]
- Zavala-Alcívar, A.; Verdecho, M.J.; Alfaro-Saiz, J.J. A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 2020, 12, 6300. [Google Scholar] [CrossRef]
- Haraguchi, M.; Neise, T.; She, W.; Taniguchi, M. Conversion strategy builds supply chain resilience during the COVID-19 pandemic: A typology and research directions. Prog. Disaster Sci. 2023, 17, 100276. [Google Scholar] [CrossRef]
- Rinaldi, M.; Bottani, E. How did COVID-19 affect logistics and supply chain processes? Immediate, short and medium-term evidence from some industrial fields of Italy. Int. J. Prod. Econ. 2023, 262, 108915. [Google Scholar] [CrossRef]
- Gurbuz, M.C.; Yurt, O.; Ozdemir, S.; Sena, V.; Yu, W. Global supply chains risks and COVID-19: Supply chain structure as a mitigating strategy for Small and Medium-Sized Enterprises. J. Bus. Res. 2023, 155, 113407. [Google Scholar] [CrossRef]
- Das, D. The impact of Sustainable Supply Chain Management practices on firm performance: Lessons from Indian organizations. J. Clean. Prod. 2018, 203, 179–196. [Google Scholar] [CrossRef]
- Zhou, H.; Li, L. The impact of supply chain practices and quality management on firm performance: Evidence from China’s small and medium manufacturing enterprises. Int. J. Prod. Econ. 2020, 230, 107816. [Google Scholar] [CrossRef]
- Patare, S.; Venkataraman, S.V. Strategies in supply chain competition: A game theoretic approach. Comput. Ind. Eng. 2023, 180, 109242. [Google Scholar] [CrossRef]
- Majumdar, A.; Sinha, S.K.; Govindan, K. Prioritising risk mitigation strategies for environmentally sustainable clothing supply chains: Insights from selected organisational theories. Sustain. Prod. Consum. 2021, 28, 543–555. [Google Scholar] [CrossRef]
- Zhou, J.; Chen, S.L.P.; Shi, W.W.; Kanrak, M. Cruise supply chain risk mitigation strategies: An empirical study in Shanghai, China. Mar. Policy 2023, 153, 105600. [Google Scholar] [CrossRef]
- Suryadi, A.; Rau, H. Considering Region Risks and Mitigation Strategies in the Supplier Selection Process for Improving Supply Chain Resilience. Comput. Ind. Eng. 2023, 181, 109288. [Google Scholar] [CrossRef]
- Alghababsheh, M.; Butt, A.S.; Ali, S.M. The role of buyers’ justice in achieving socially sustainable global supply chains: A perspective of apparel suppliers and their workers. J. Purch. Supply Manag. 2023, 29, 100820. [Google Scholar] [CrossRef]
- Min, S.; Zacharia, Z.G.; Smith, C.D. Defining supply chain management: In the past, present, and future. J. Bus. Logist. 2019, 40, 44–55. [Google Scholar] [CrossRef] [Green Version]
- Xu, S.; Zhang, X.; Feng, L.; Yang, W. Disruption risks in supply chain management: A literature review based on bibliometric analysis. Int. J. Prod. Res. 2020, 58, 3508–3526. [Google Scholar] [CrossRef]
- Ummi, N.; Ferdinant, P.F.; Irman, A.; Gunawan, A. Integration house of risk and analytical network process for supply chain risk mitigation of cassava opak chips industry. In MATEC Web of Conferences; EDP Sciences: Castanet-Tolosan, France, 2018; Volume 218, p. 04022. [Google Scholar]
- Sufiyan, M.; Haleem, A.; Khan, S.; Khan, M.I. Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustain. Prod. Consum. 2019, 20, 40–57. [Google Scholar] [CrossRef]
- Lohmer, J.; Bugert, N.; Lasch, R. Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. Int. J. Prod. Econ. 2020, 228, 107882. [Google Scholar] [CrossRef]
- Wang, M.; Zhang, K. Improving Agricultural Green Supply Chain Management by a Novel Integrated Fuzzy-Delphi and Grey-WINGS Model. Agriculture 2022, 12, 1512. [Google Scholar] [CrossRef]
- Bernard, R. Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Rev. D’Inform. Rech. Oper. RIRO 1968, 2, 57–75. [Google Scholar]
- Uddin, S.; Ali, S.M.; Kabir, G.; Suhi, S.A.; Enayet, R.; Haque, T. An AHP-ELECTRE framework to evaluate barriers to green supply chain management in the leather industry. Int. J. Sustain. Dev. World Ecol. 2019, 26, 732–751. [Google Scholar] [CrossRef]
- Kumar, N.; Tyagi, M.; Sachdeva, A. Estimation of best possible solutions for environmental trade-offs in cold supply chain using BWM based ELECTRE-I approach. Int. J. Six Sigma Compet. Advant. 2022, 14, 152–169. [Google Scholar] [CrossRef]
- Kabadayı, N. Dealership performance evaluation in supply chain with dematel and electre methods. Kabadayı, N.; Dağ, S.Dealership Performance Evaluation in Supply Chain with DEMATEL and ELECTRE Methods. Pamukkale Univ. J. Eng. Sci. 2020, 26, 241–253. [Google Scholar] [CrossRef] [Green Version]
- Farughi, H.; Mostafayi, S. A hybrid approach based on ANP, ELECTRE and SIMANP metaheuristic method for outsourcing manufacturing procedures according to supply chain risks-Case study: A medical equipment manufacturer company in Iran. Decis. Sci. Lett. 2017, 6, 77–94. [Google Scholar] [CrossRef]
- Zandi, F.; Tavana, M.; Martin, D. A fuzzy group Electre method for electronic supply chain management framework selection. Int. J. Logist. Res. Appl. 2011, 14, 35–60. [Google Scholar] [CrossRef] [Green Version]
- Bizhan, N.; Asia, M.H. Ranking the Factors Affecting the Green Supply Chain with Economics-Based Approach Using ELECTRE (Case Study: Waste Management In Shiraz). Int. J. Resist. Econ. 2016, 4, 28–43. [Google Scholar]
- Edalat Sarvestani, M.R.; Shahraki, M.R. Comparing affective factors Ranks in the supply chain Management by using Fuzzy ELECTRE (I) method and ackknife resampling method with an Interval Analysis. Emerg. Manag. 2016, 4, 107–117. [Google Scholar]
- Shojaie, A.A.; Babaie, S.; Sayah, E.; Mohammaditabar, D. Analysis and prioritization of green health suppliers using Fuzzy ELECTRE method with a case study. Glob. J. Flex. Syst. Manag. 2018, 19, 39–52. [Google Scholar] [CrossRef]
- Qu, G.; Zhang, Z.; Qu, W.; Xu, Z. Green supplier selection based on green practices evaluated using fuzzy approaches of TOPSIS and ELECTRE with a case study in a Chinese Internet company. Int. J. Environ. Res. Public Health 2020, 17, 3268. [Google Scholar] [CrossRef]
- Korucuk, S.; Tirkolaee, E.B.; Aytekin, A.; Karabasevic, D.; Karamaşa, Ç. Agile supply chain management based on critical success factors and most ideal risk reduction strategy in the era of industry 4.0: Application to plastic industry. Oper. Manag. Res. 2023, 1–22. [Google Scholar] [CrossRef]
- Stević, Ž.; Ulutaş, A.; Korucuk, S.; Memiş, S.; Demir, E.; Topal, A.; Karamaşa, Ç. Supply Chain Management (SCM) Breakdowns and SCM Strategy Selection during the COVID-19 Pandemic Using the Novel Rough MCDM Model. Complexity 2023, 2023, 3478719. [Google Scholar] [CrossRef]
- Tsai, J.F.; Shen, S.P.; Lin, M.H. Applying a Hybrid MCDM Model to Evaluate Green Supply Chain Management Practices. Sustainability 2023, 15, 2148. [Google Scholar] [CrossRef]
- Wei, Y. A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management. Int. Trans. Electr. Energy Syst. 2022, 2022, 4766597. [Google Scholar] [CrossRef]
- Shyur, H.J.; Shih, H.S. A hybrid MCDM model for strategic vendor selection. Math. Comput. Model. 2006, 44, 749–761. [Google Scholar] [CrossRef]
- Tliche, Y.; Taghipour, A.; Mahfod-Leroux, J.; Vosooghidizaji, M. Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain. Int. J. Inf. Syst. Supply Chain. Manag. 2023, 16, 1–37. [Google Scholar] [CrossRef]
- Kao, J.C.; Wang, C.N.; Nguyen, V.T.; Husain, S.T. A Fuzzy MCDM Model of Supplier Selection in Supply Chain Management. Intell. Autom. Soft Comput. 2022, 31, 1451–1466. [Google Scholar] [CrossRef]
- Bairagi, B. A novel MCDM model for warehouse location selection in supply chain management. Decis. Mak. Appl. Manag. Eng. 2022, 5, 194–207. [Google Scholar] [CrossRef]
- Roy, S.; Paul, A.; Paul, A.; Kashyap, S.; Jana, A. Ranking barriers of supply chain management by MCDM method during disaster management: A case study of India. Int. J. Syst. Dyn. Appl. IJSDA 2021, 10, 1–16. [Google Scholar] [CrossRef]
- Riaz, M.; Akmal, K.; Almalki, Y.; Ahmad, D. Cubic intuitionistic fuzzy topology with application to uncertain supply chain management. Math. Probl. Eng. 2022, 2022, 9631579. [Google Scholar] [CrossRef]
- Singh, N.P. Managing environmental uncertainty for improved firm financial performance: The moderating role of supply chain risk management practices on managerial decision making. Int. J. Logist. Res. Appl. 2020, 23, 270–290. [Google Scholar] [CrossRef]
- Ganguly, A.; Kumar, C. Evaluating Supply Chain Resiliency Strategies in the Indian pharmaceutical sector: A fuzzy analytic hierarchy process (F-AHP) approach. Int. J. Anal. Hierarchy Process 2019, 11, 153–180. [Google Scholar] [CrossRef]
- Chand, M.; Bhatia, N.; Singh, R.K. ANP-MOORA-based approach for the analysis of selected issues of green supply chain management. Benchmarking Int. J. 2018, 25, 642–659. [Google Scholar] [CrossRef]
- Luqman, N.A.; Ahmad, S.Z.; Hussain, M. Effects of the degree of supply chain resilience capability in supply chain performance in the UAE energy industry. Supply Chain. Manag. Int. J. 2023; ahead-of-print. [Google Scholar]
- Misbauddin, S.M.; Alam, M.J.; Karmaker, C.L.; Nabi, M.N.U.; Hasan, M.M. Exploring the antecedents of supply chain viability in a pandemic context: An empirical study on the commercial flower supply chain of an emerging economy. Sustainability 2023, 15, 2146. [Google Scholar] [CrossRef]
- Wu, A.; Sun, Y.; Zhang, H.; Sun, L.; Wang, X.; Li, B. Research on Resilience Evaluation of Coal Industrial Chain and Supply Chain Based on Interval Type-2F-PT-TOPSIS. Processes 2023, 11, 566. [Google Scholar] [CrossRef]
- Liu, W.; He, Y.; Dong, J.; Cao, Y. Disruptive technologies for advancing supply chain resilience. Front. Eng. Manag. 2023, 10, 360–366. [Google Scholar] [CrossRef]
- Sharma, M.; Antony, R.; Tsagarakis, K. Green, resilient, agile, and sustainable fresh food supply chain enablers: Evidence from India. Ann. Oper. Res. 2023, 1–27. [Google Scholar] [CrossRef]
- Hsu, C.H.; Li, M.G.; Zhang, T.Y.; Chang, A.Y.; Shangguan, S.Z.; Liu, W.L. Deploying big data enablers to strengthen supply chain resilience to mitigate sustainable risks based on integrated HOQ-MCDM framework. Mathematics 2022, 10, 1233. [Google Scholar] [CrossRef]
- Sathyan, R.; Parthiban, P.; Dhanalakshmi, R.; Sachin, M.S. An integrated Fuzzy MCDM approach for modelling and prioritising the enablers of responsiveness in automotive supply chain using Fuzzy DEMATEL, Fuzzy AHP and Fuzzy TOPSIS. Soft Comput. 2023, 27, 257–277. [Google Scholar] [CrossRef]
- Hsu, C.H.; He, X.; Zhang, T.Y.; Chang, A.Y.; Liu, W.L.; Lin, Z.Q. Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers. Mathematics 2022, 10, 1635. [Google Scholar] [CrossRef]
- Khan, J.; Ishizaka, A.; Mangla, S.K. Assessing risk of supply chain disruption due to COVID-19 with fuzzy VIKORSort. Ann. Oper. Res. 2022, 1–26. [Google Scholar] [CrossRef] [PubMed]
- Mabrouk, N. Green supplier selection using fuzzy Delphi method for developing sustainable supply chain. Decis. Sci. Lett. 2021, 10, 63–70. [Google Scholar] [CrossRef]
- Sumarliah, E.; Li, T.; Wang, B.; Indriya, I. An examination of halal fashion supply chain management risks based on the fuzzy best-worst approach. Inf. Resour. Manag. J. IRMJ 2021, 34, 69–92. [Google Scholar] [CrossRef]
- Aytac, E.; IŞIK, A.T.; Kundakci, N. Fuzzy ELECTRE I method for evaluating catering firm alternatives. Ege Acad. Rev. 2011, 11, 125–134. [Google Scholar]
- Hatami-Marbini, A.; Tavana, M. An extension of the Electre I method for group decision-making under a fuzzy environment. Omega 2011, 39, 373–386. [Google Scholar] [CrossRef]
- Shayah, M.H.; Qifeng, Y. Development of free zones in United Arab Emirates. Int. Rev. Res. Emerg. Mark. Glob. Econ. IRREM 2015, 1, 286–294. [Google Scholar]
- Kırılmaz, O.; Erol, S. A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks. J. Purch. Supply Manag. 2017, 23, 54–65. [Google Scholar] [CrossRef]
- Mistarihi, M.Z.; Magableh, G.M. Prioritization of Supply Chain Capabilities Using the FAHP Technique. Sustainability 2023, 15, 6308. [Google Scholar] [CrossRef]
- Mistarihi, M.Z.; Magableh, G.M. Supply Chain Nervousness Optimization Using the FuzzyELECTRE Technique. In Proceedings of the International Conference on Mechanical, Industrial and Production Engineering (ICMIPE-23), Suez, Egypt, 24–25 May 2023. [Google Scholar]
Reference, Authors, and Year | Adapted Strategies |
---|---|
[45] Haraguchi et al. (2023) | Develop conversion strategies by considering six conversion types: the location of production, the type of production line, storage location, usage, distribution channel, and the skills of the workforce. |
[46] Rinaldi and Bottani (2023) | Prompt two types of strategies: Preventive strategies and sourcing strategies. The preventive strategies include the use of PPE, the usage of protective barriers, space layout redesign, and smart working. While sourcing strategies include multiple sourcing, global sourcing, and local sourcing. Also, they highlight the need of improving cooperation, information sharing, and process automation. |
[47] Gurbuz et al. (2023) | They emerged different strategies, such as collaboration strategies to enhance coordination and information sharing; flexibility strategies to adapt the changes due to disruptions; responsiveness strategies to highlight the agile response to customer needs; multi-shoring and multi-sourcing strategies; expanding the customer base and markets by adapting the customer base diversification strategy; navigating the complexity of regulations by developing the political advantage and trade strategy; and, finally, the digitalization strategies. |
[48] Das (2018) | They suggest different practices including the integration of SC practices, operational practices, environmental management practices, and employee and community-inclusive practices. |
[49] Zhou and Li (2020) | Highlight the need for the integration of quality management practices with the information sharing within the SC and supplier-specific investment. |
[50] Patare and Venkataraman (2023) | Discuss the strategies of quality production, marketing efforts, and product pricing. |
[51] Majumdar et al. (2021) | Develop strategies with a combination of supply chain agility, flexibility, sustainability, coordination, and collaboration to increase revenues. |
[52] Zhou et al. (2023) | Adapt controlling strategies, flexibility strategies, relationship-based and policy-based strategies, and marketing strategies. |
[53] Suryadi and Rau (2023) | Supplier selection strategies with a focus on inventories level, level of inventories, shipping routes, and method of shipping. |
[54] Alghababsheh et al. (2023) | They focus on different strategies concerning sustainability such as green purchasing, eco-design, environmental management, collaboration, and investment recovery. |
Reference, Authors, and Year | Field of Research | MCDA Approach |
---|---|---|
[71] Korucuk et al. (2023) | Supply Chain Management | Bipolar Neutrosophic Stepwise Weight Assessment Ratio Analysis (BN-SWARA) and (BN-TOPSIS) methods |
[72] Stević et al. (2023) | Supply Chain Management, COVID-19 | Rough MCDM Model (rough set theory, SWARA, and MARCOS) |
[73] Tsai et al. (2023) | Green Supply Chain Management | Hybrid MCDM Model (DEMATEL-based ANP (DANP)) |
[74] Wei (2022) | Supply Chain Management, Risk Assessment | Machine Learning-based Linear Regression Algorithm (ML-LRA) |
[75] Shyur (2006) | Vendor Selection | AHP, ANP, and TOPSIS |
[76] Tliche et al. (2023) | Supply Chain Management, Bullwhip Effect | Bi-objective Optimization |
[77] Kao et al. (2022) | Supplier Selection | Fuzzy Analytic Hierarchical Process (FAHP) and Weighted Aggregated Sum Product Assessment (WASPAS) |
[78] Bairagi (2022) | Warehouse Location Selection | Fuzzy Multi-criteria Analysis (FMCA) |
[79] Roy et al. (2021) | Ranking Barriers of Supply Chain Management, Disaster Management | ELECTRE Method |
[80] Riaz et al. (2022) | Supply Chain Management, Uncertainty | Cubic Intuitionistic Fuzzy Topology |
[81] Singh (2020) | Supply Chain Risk Management, Environment Uncertainty | Uncertainty Analysis, Hypothesis Testing |
[82] Ganguly and Kumar (2019) | Supply Chain Resiliency | Fuzzy Analytic Hierarchy Process (F-AHP) |
[83] Chand et al. (2018) | Green Supply Chain Management | ANP-MOORA |
[84] Luqman et al. (2023) | Supply Chain Resilience | Partial Least Squares Structural Equation Modeling (PLS-SEM) |
[85] Misbauddin et al. (2023) | Supply Chain Viability | (PLS-SEM) |
[86] Wu et al. (2023) | Supply Chain Resilience | Interval Type-2F-PT-TOPSIS |
[87] Liu et al. (2023) | Supply Chain Resilience | Disruptive Technologies |
[88] Sharma et al. (2023) | Green, Resilient, Agile, and Sustainable Fresh Food Supply Chain | Integrated Fuzzy Interpretive Structural Modeling—Decision-making Trial and Evaluation Laboratory (FISM-DEMATEL) Techniques |
[89] Hsu et al. (2022) | Supply Chain Resilience | HOQ-MCDM |
[90] Sathyan et al. (2022) | Responsiveness in Automotive Supply Chain | Fuzzy MCDM (Fuzzy DEMATEL, Fuzzy AHP, and Fuzzy TOPSIS) |
[91] Hsu et al. (2022) | Supply Chain Agility | Integrated QFD-MCDM |
[92] Khan et al. (2022) | Supply Chain Risk Management | Fuzzy VIKORSort |
[68] Sarvestani (2016) | Supply Chain Management | Fuzzy-ELECTRE (I) |
[93] Mabrouk (2021) | Sustainable Supply Chain Management | Fuzzy Delphi |
[94] Sumarliah et al. (2021) | Risk Supply Chain Management | Fuzzy Best-worst |
The Linguistic Terms for the Importance Weights of the Criteria | The Linguistic Terms for the Performance Rating | ||
---|---|---|---|
Linguistic Terms | Equivalent Fuzzy Number | Linguistic Terms | Equivalent Fuzzy Number |
Very Low (VL) | (0,0.1,0.3) | Very Poor (VP) | (0,1,2) |
Low (L) | (0.1,0.3,0.5) | Poor (P) | (2,3,4) |
Medium (M) | (0.3,0.5,0.7) | Fair (F) | (4,5,6) |
High (H) | (0.5,0.7,0.9) | Good (G) | (6,7,8) |
Very High (VH) | (0.7,0.9,1) | Very Good (VG) | (8,9,10) |
Expert/Criteria | Re | Co | Sc | Vi | Ic |
---|---|---|---|---|---|
E1 | H | M | H | VL | VL |
E2 | M | H | VH | M | M |
E3 | VH | H | H | L | VL |
E3 | H | M | VH | L | L |
E4 | M | H | H | M | M |
E5 | H | M | VH | M | VL |
E6 | M | M | M | H | L |
E7 | H | M | H | VL | VL |
Fuzzy weights | (0.3,0.64,0.83) | (0.3,0.59,0.79) | (0.3,0.76,0.91) | (0,0.41,0.61) | (0,0.27,0.47) |
Expert | E1 | E2 | E3 | E4 | E5 | E6 | E7 | Aggregate Fuzzy Values |
---|---|---|---|---|---|---|---|---|
FI-Re | H | M | H | L | L | M | M | |
(6,7,8) | (4,5,6) | (6,7,8) | (2,3,4) | (2,3,4) | (4,5,6) | (4,5,6) | (4,5,6) | |
SF-Re | VH | H | M | M | H | VH | H | |
(8,9,10) | (6,7,8) | (4,5,6) | (4,5,6) | (6,7,8) | (8,9,10) | (6,7,8) | (6,7,8) |
Alternative/Criteria | Re | Co | Sc | Vi | Ic |
---|---|---|---|---|---|
DV | (8,9,10) | (8,9,10) | (6,7,8) | (6,7,8) | (8,9,10) |
FI | (4,5,6) | (2,3,4) | (4,5,6) | (8,9,10) | (8,9,10) |
SF | (6,7,8) | (4,5,6) | (4,5,6) | (6,7,8) | (8,9,10) |
UE | (4,5,6) | (4,5,6) | (2,3,4) | (6,7,8) | (6,7,8) |
BU | (2,3,4) | (0,1,2) | (2,3,4) | (4,5,6) | (0,1,2) |
Re | Co | Sc | Vi | Ic | |
---|---|---|---|---|---|
DV | (0.69,0.65,0.63) | (0.78,0.74,0.7) | (0.69,0.65,0.62) | (0.44,0.44,0.44) | (0.53,0.53,0.52) |
FI | (0.34,0.36,0.38) | (0.2,0.25,0.28) | (0.46,0.46,0.46) | (0.58,0.57,0.55) | (0.53,0.53,0.52) |
SF | (0.51,0.51,0.5) | (0.39,0.41,0.42) | (0.46,0.46,0.46) | (0.44,0.44,0.44) | (0.53,0.53,0.52) |
UE | (0.34,0.36,0.38) | (0.39,0.41,0.42) | (0.23,0.28,0.31) | (0.44,0.44,0.44) | (0.4,0.41,0.42) |
BU | (0.17,0.22,0.25) | (0.2,0.25,0.28) | (0.23,0.28,0.31) | (0.29,0.31,0.33) | (0,0.06,0.1) |
Re | Co | Sc | Vi | Ic | |
---|---|---|---|---|---|
DV | (0.21,0.42,0.52) | (0.24,0.44,0.55) | (0.21,0.49,0.56) | (0,0.18,0.27) | (0,0.14,0.25) |
FI | (0.1,0.23,0.31) | (0.06,0.15,0.22) | (0.14,0.35,0.42) | (0,0.23,0.34) | (0,0.14,0.25) |
SF | (0.15,0.33,0.42) | (0.12,0.24,0.33) | (0.14,0.35,0.42) | (0,0.18,0.27) | (0,0.14,0.25) |
UE | (0.1,0.23,0.31) | (0.12,0.24,0.33) | (0.07,0.21,0.28) | (0,0.18,0.27) | (0,0.11,0.2) |
BU | (0.05,0.14,0.21) | (0.06,0.15,0.22) | (0.07,0.21,0.28) | (0,0.13,0.2) | (0,0.02,0.05) |
DV | FI | SF | UE | BU | |
---|---|---|---|---|---|
DV | - | (0,0.68,1.08) | (0,0.68,1.08) | (0,0.41,0.61) | (0.3,0,0) |
FI | (0.9,2.26,3) | - | (0.9,2.26,3) | (0.6,1.23,1.62) | (0.3,0.59,0.79) |
SF | (0.9,1.99,2.53) | (0.3,1.44,1.99) | - | (0.3,1,1.4) | (0.3,0,0) |
UE | (0.9,2.67,3.61) | (0.6,2.08,2.82) | (0.9,2.67,3.61) | - | (0.3,0.76,0.91) |
BU | (0.9,2.67,3.61) | (0.9,2.67,3.61) | (0.9,2.67,3.61) | (0.3,0,0) | - |
(0.53,1.44,1.94) |
DV | FI | SF | UE | BU | |
---|---|---|---|---|---|
DV | - | 0.84 | 1.00 | 1.00 | 1 |
FI | 1.00 | - | 1.00 | 1.00 | 1 |
SF | 1.00 | 1.00 | - | 1.00 | 1 |
UE | 0.43 | 0.20 | 0.54 | - | 1 |
BU | 0.65 | 0.31 | 1.00 | 0.79823 | - |
0.900 |
DV | FI | SF | UE | BU | |
---|---|---|---|---|---|
DV | 1 | 1 | 1 | 1 | 1 |
FI | 0 | 1 | 0 | 1 | 1 |
SF | 0 | 1 | 1 | 1 | 1 |
UE | 0 | 0 | 0 | 1 | 1 |
BU | 0 | 0 | 0 | 1 | 1 |
DV | FI | SF | UE | BU | |
---|---|---|---|---|---|
DV | 0 | 0 | 1 | 1 | 1 |
FI | 1 | 0 | 1 | 1 | 1 |
SF | 1 | 1 | 0 | 1 | 1 |
UE | 0 | 0 | 0 | 0 | 1 |
BU | 0 | 0 | 1 | 0 | 0 |
DV | FI | SF | UE | BU | |
---|---|---|---|---|---|
DV | 0 | 0 | 1 | 1 | 1 |
FI | 0 | 0 | 0 | 1 | 1 |
SF | 0 | 1 | 0 | 1 | 1 |
UE | 0 | 0 | 0 | 0 | 1 |
BU | 0 | 0 | 0 | 0 | 0 |
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Share and Cite
Mistarihi, M.Z.; Magableh, G.M. Unveiling Supply Chain Nervousness: A Strategic Framework for Disruption Management under Fuzzy Environment. Sustainability 2023, 15, 11179. https://doi.org/10.3390/su151411179
Mistarihi MZ, Magableh GM. Unveiling Supply Chain Nervousness: A Strategic Framework for Disruption Management under Fuzzy Environment. Sustainability. 2023; 15(14):11179. https://doi.org/10.3390/su151411179
Chicago/Turabian StyleMistarihi, Mahmoud Z., and Ghazi M. Magableh. 2023. "Unveiling Supply Chain Nervousness: A Strategic Framework for Disruption Management under Fuzzy Environment" Sustainability 15, no. 14: 11179. https://doi.org/10.3390/su151411179
APA StyleMistarihi, M. Z., & Magableh, G. M. (2023). Unveiling Supply Chain Nervousness: A Strategic Framework for Disruption Management under Fuzzy Environment. Sustainability, 15(14), 11179. https://doi.org/10.3390/su151411179