Fuzzy-FMEA Theory Approach for Prioritizing Supply Chain Nervousness Factors
Source abstract
Abstract
Global supply chain shocks and interruptions underscore the importance of supply chain nervousness (SCN). A clear understanding of nervousness in the event of a crisis allows an organization to make a good plan to deal with interruptions and future shocks. This study aims to develop a model for assessing nervousness parameters in the supply chains (SCs) by incorporating the fuzzy-FMEA theory and gray correlation approach to rank the SCN factors. The integrated model used in this study can improve the accuracy of outcomes when uncertainty exists in the SCN data. The results show that the most important nervousness factors are SC planning, visibility, stability, decision support systems, and SC flexibility. The developed SCN approach allows understanding and prioritizing SCN factors with more realistic and effective assessment. Findings offer beneficial insights for SCN prevention, and suggestions are made to identify nervousness mitigations. The outcomes of this research can be used by researchers and SC specialists to develop decision support systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Sustainable Supply Chain Management · Quality Function Deployment in Product Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Industrial Engineering Department, Faculty of Hijjawi for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
Department of Mechanical and Industrial Engineering, Liwa College, Abu Dhabi 41009, United Arab Emirates
References (69)
Show source-provided references
1. Revisiting Rescheduling: MRP Nervousness and the Bullwhip Effect10.1080/00207543.2016.1261196 ↗
2. Mousavi, B.A., Azzouz, R., Heavey, C., and Ehm, H. (2019, January 8–11). MRP analysis of the nervousness within semiconductors supply chain planning: Insight from a case study. Proceedings of the Winter Simulation Conference (WSC), National Harbor, MD, USA.10.1109/WSC40007.2019.9004936 ↗
3. Planning nervousness in a demand supply network: An empirical study10.1108/09574090610663455 ↗
4. A comparative study of schedule nervousness among high-tech manufacturers across the straits10.1080/00207540903246623 ↗
5. Reduction of Supply Chain Nervousness with Vendor Managed Inventory Approach
6. Pengembangan Model Consignment Stock pada Sistem Rantai Pasok Dua Eselon dengan Permintaan Berfluktuasi dan Perubahan Order Awal
7. A simple approach for assessing the cost of system nervousness10.1016/j.ijpe.2012.09.022 ↗
8. Causes and Effects of Supply Chain Nervousness: Mena case study10.22306/al.v9i2.299 ↗
9. Magableh, G.M., and Mistarihi, M.Z. (2023). Global Supply Chain Nervousness (GSCN). Sustainability, 15.10.3390/su151612115 ↗
10. Lot-Sizing Algorithm for Reducing Nervousness in MRP Systems10.1287/mnsc.30.2.240 ↗
11. Correlating MRP system nervousness with forecast errors10.1080/002075498192904 ↗
12. Concepts, Theories, and Techniques Heuristic Lot-Sizing Approaches for Dealing with MRP System Nervousness10.1111/j.1540-5915.1983.tb00177.x ↗
13. Evaluating the impact of operating environments on MRP system nervousness10.1080/00207548908942611 ↗
14. MRP System Nervousness: Causes and Cures10.1016/0167-188X(85)90021-7 ↗
15. Comparison of Strategies to Dampen Nervousness in MRP Systems10.1287/mnsc.32.4.413 ↗
16. Evaluating dampening effects of alternative lot-sizing rules to reduce MRP system nervousness10.1080/00207540210134489 ↗
17. An investigation of alternative dampening procedures to cope with MRP system nervousness10.1080/00207549608904895 ↗
18. Evaluating the effectiveness of using lot-sizing rules to cope with MRP system nervousness10.1080/095372899233307 ↗
19. A diagnostic analysis of the impact of forecast errors on production planning via MRP system nervousness10.1080/09537289308919453 ↗
20. Research Framework for Investigating the Effectiveness of Dampening Procedures to Cope with MRP System Nervousness10.1108/01443579210013145 ↗
21. Braun, M.W., and Schwartz, J.D. (2012). A control theoretic evaluation of schedule nervousness suppression techniques for master production scheduling. Decision Policies for Production Networks, Springer.10.1007/978-0-85729-644-3_7 ↗
22. An Explicit Online Resource-Task Network Scheduling Formulation to Avoid Scheduling Nervousness10.1016/B978-0-12-818634-3.50011-4 ↗
23. Hasachoo, N., and Masuchun, R. (2016, January 4–7). Reducing Schedule Nervousness in Production and Operations Under Non-Stationary Stochastic Demand: The Case of an Airline Catering Company. Proceedings of the IEEM2016: 2016 International Conference on Industrial Engineering and Engineering Management, Bali, Indonesia.10.1109/IEEM.2016.7798016 ↗
24. Schedule nervousness in a manufacturing system: A case study10.1080/09537280410001726320 ↗
25. Alternative approaches for reducing schedule instability in multistage manufacturing under demand uncertainty10.1016/0272-6963(95)00023-L ↗
26. Nervousness in inventory control: Analytical results10.1007/BF01719468 ↗
27. Effectiveness of nervousness reduction policies when capacity is constrained10.1080/00207543.2019.1643513 ↗
28. Simulation of less master production schedule nervousness model Simulation of less master production schedule nervousness model Simulation of Less Master Production Schedule Nervousness Model
29. Interaction effects on MRP nervousness10.1080/00207549008942691 ↗
30. Less Nervous MRP Systems: A Dynamic Economic Lot-Sizing Approach10.1287/mnsc.25.8.754 ↗
31. Stochastic lot sizing problem with nervousness considerations10.1016/j.cor.2018.01.021 ↗
32. Mistarihi, M., and Magableh, G. (2023). Unveiling Supply Chain Nervousness: A Strategic Framework for Disruption Management under Fuzzy Environment. Sustainability, 15.10.3390/su151411179 ↗
33. Mistarihi, M., and Magableh, G. (2023, January 30). Supply Chain Nervousness Optimization using Fuzzy-ELECTRE Technique. Proceedings of the International Conference On Mechanical, Industrial and Production Engineering—(ICMIPE-23), Davanagere, India.
34. Assessment of risks associated with third-party logistics in restaurant supply chain10.1108/BIJ-06-2020-0343 ↗
35. Supply chain risk management and operational performance: The enabling role of supply chain integration10.1016/j.ijpe.2020.107667 ↗
36. A novel plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management10.1016/j.jclepro.2019.119586 ↗
37. Supply Chain Capabilities, Risks, and Resilience10.1016/j.ijpe.2016.09.008 ↗
38. Supply chain optimization under risk and uncertainty: A case study for high-end server manufacturing10.1016/j.cie.2015.12.025 ↗
39. Uncertainty and supply chain risk: The moderating role of supply chain flexibility in risk mitigation10.1016/j.ijpe.2017.07.024 ↗
40. Optimal Strategy of Supply Chain considering Interruption Insurance10.1155/2021/9922515 ↗
41. Linqiao, Y., Xuedong, L., Fumin, D., and Zhaoxia, G. (2015, January 6–9). Risk Assessment of Supply Chain Based on Combination Weighting Method and Grey Theory. Proceedings of the 2015 IEEE IEEM, Singapore.
42. Resilience assessment of the lithium supply chain in China under impact of new energy vehicles and supply interruption10.1016/j.jclepro.2019.119624 ↗
43. Zhang, Q., Fan, W., Lu, J., Wu, S., and Wang, X. (2021). Research on dynamic analysis and mitigation strategies of supply chains under different disruption risks. Sustainability, 13.10.3390/su13052462 ↗
44. Analysis of the impact of covid-19 on the coupling of the material flow and capital flow in a closed-loop supply chain
45. Supply interruption supply chain network model with uncertain demand: An application of chance-constrained programming with fuzzy parameters10.1155/2021/6686992 ↗
46. Monitoring of the production processing in a metallurgical company using FMEA method10.1515/amm-2017-0309 ↗
47. Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment10.1016/j.ssci.2017.10.018 ↗
48. An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks. Transportation Research Part E: Logistics and Transportation Review LJMU Research Online An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks10.1016/j.tre.2019.03.011 ↗
49. Benchmarking the risk assessment in green supply chain using fuzzy approach to FMEA: Insights from an Indian case study10.1108/BIJ-04-2017-0074 ↗
50. Product deletion and supply chain repercussions: Risk management using FMEA10.1108/BIJ-01-2020-0007 ↗
51. An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk10.1016/j.jclepro.2018.04.167 ↗
52. Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR10.1108/IJQRM-02-2019-0070 ↗
53. Failure modes and effects analysis (FMEA) for the security of the supply chain system of the gas station in China10.1016/j.ecoenv.2018.08.028 ↗
54. Comparative Analysis of Risk Management Strategies for Additive Manufacturing Supply Chains10.3844/ajassp.2019.273.282 ↗
55. Using FMEA for Supply Chain Risk Management
56. Using the Saaty Method and the FMEA Method for Evaluation of Constraints in Logistics Chain10.1016/j.proeng.2017.04.433 ↗
57. Measuring the supply chain risk and vulnerability in frequency space10.1016/j.ijpe.2006.12.017 ↗
58. An Integrated Method of Supply Chains Vulnerability Assessment
59. Hybrid fuzzy-probabilistic approach to supply chain resilience assessment Hybrid fuzzy-probabilistic ap-proach to supply chain resilience assessment Hybrid fuzzy-probabilistic approach to supply chain resilience assessment
60. Prioritizing drivers to creating traceability in the food supply chain10.1016/j.procir.2021.01.176 ↗
61. Sustainable supply chain management using approximate fuzzy DEMATEL method10.1016/j.resconrec.2016.11.017 ↗
62. Evaluating food supply chain performance using hybrid fuzzy MCDM technique10.1016/j.spc.2019.03.004 ↗
63. Supply Chains and the COVID-19 Pandemic: A Comprehensive Framework10.1111/emre.12449 ↗
64. Applications of MCDM approach (ANP-TOPSIS) to evaluate supply chain solutions in the context of COVID-1910.1016/j.heliyon.2022.e09062 ↗
65. Prevention schemes for future fresh agricultural products (FAPs) supply chain: Mathematical model and experience of guaranteeing the supply of FAPs during the COVID-19 pandemic10.1002/jsfa.11308 ↗
66. Lahmar, A., Galasso, F., Chabchoub, H., and Lamothe, J. (2016, January 1–4). Conceptual Framework of Supply Chain Vulnerability. Proceedings of the 6th International Conference on Information Systems, Logistics and Supply Chain, Bordeaux, France.
67. A risk evaluation method to prioritize failure modes based on failure data and a combination of fuzzy sets theory and grey theory10.1016/j.engappai.2019.03.023 ↗
68. Improvement on Judgement Matrix Based on Triangle Fuzzy Number
69. An improved FMEA using fuzzy evidential reasoning approach and grey theory