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International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(4): 343-359

Published online December 25, 2024

https://doi.org/10.5391/IJFIS.2024.24.4.343

© The Korean Institute of Intelligent Systems

Supply Chain Risk Analysis through the Computational Method

Torky Althaqafi

College of Business, University of Jeddah, Jeddah, Saudi Arabia

Correspondence to :
Torky Althaqafi (tmalthaqafi@uj.edu.sa)

Received: September 21, 2024; Accepted: December 17, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supply chain management (SCM) requires risk analysis for the sustainable development of organizations such as retail, healthcare, information technology, and media. SCM has set ambitious goals and requirements for organizations to increase their share of productivity. However, considering the various criteria and factors involved in the process, selecting and deciding on the optimal SCM source can be challenging for organizations. In addressing this challenge, selection priority and risk analysis factors in SCM and alternatives are important. This challenge was resolved using the hesitant fuzzy-analytic hierarchy process (HF-AHP) and hesitant fuzzy-technique for order preference by similarity to ideal solution (HF-TOPSIS). The proposed approach considers numerous criteria, assigning weights using the HF-AHP method. Natural disasters are assigned the highest weight and geopolitical uncertainty the lowest weight. Within these groups, among subfactors, hurricane has the highest weight and economic conditions the lowest weight. HF-TOPSIS ranks the SCM alternatives, whereby systematic SCM has the highest priority and mitigation strategy the lowest priority. The proposed strategy can maintain the dynamics of choosing the ideal SCM, providing significant knowledge to policymakers and SCM partners.

Keywords: Supply chain, Risk analysis, Hesitant fuzzy, AHP, TOPSIS

The author is grateful to the University of Jeddah for technical and financial support.

There are no potential conflicts of interest to declare relevant to this article.

This work was funded by the University of Jeddah, Jeddah, Saudi Arabia (Grant No. UJ-23-DR-246).

Torky Althaqafi is an associate professor at the University of Jeddah. His research includes topics like sustainable supply chain practices. His work examines how sustainability can be integrated into supply chains, exploring methods to enhance environmental performance and efficiency in production processes.

Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(4): 343-359

Published online December 25, 2024 https://doi.org/10.5391/IJFIS.2024.24.4.343

Copyright © The Korean Institute of Intelligent Systems.

Supply Chain Risk Analysis through the Computational Method

Torky Althaqafi

College of Business, University of Jeddah, Jeddah, Saudi Arabia

Correspondence to:Torky Althaqafi (tmalthaqafi@uj.edu.sa)

Received: September 21, 2024; Accepted: December 17, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Supply chain management (SCM) requires risk analysis for the sustainable development of organizations such as retail, healthcare, information technology, and media. SCM has set ambitious goals and requirements for organizations to increase their share of productivity. However, considering the various criteria and factors involved in the process, selecting and deciding on the optimal SCM source can be challenging for organizations. In addressing this challenge, selection priority and risk analysis factors in SCM and alternatives are important. This challenge was resolved using the hesitant fuzzy-analytic hierarchy process (HF-AHP) and hesitant fuzzy-technique for order preference by similarity to ideal solution (HF-TOPSIS). The proposed approach considers numerous criteria, assigning weights using the HF-AHP method. Natural disasters are assigned the highest weight and geopolitical uncertainty the lowest weight. Within these groups, among subfactors, hurricane has the highest weight and economic conditions the lowest weight. HF-TOPSIS ranks the SCM alternatives, whereby systematic SCM has the highest priority and mitigation strategy the lowest priority. The proposed strategy can maintain the dynamics of choosing the ideal SCM, providing significant knowledge to policymakers and SCM partners.

Keywords: Supply chain, Risk analysis, Hesitant fuzzy, AHP, TOPSIS

Fig 1.

Figure 1.

Hierarchy diagram of the SCM.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 343-359https://doi.org/10.5391/IJFIS.2024.24.4.343

Fig 2.

Figure 2.

Process diagram of HF-AHP and HF-TOPSIS.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 343-359https://doi.org/10.5391/IJFIS.2024.24.4.343

Fig 3.

Figure 3.

Schematic illustration of the level of satisfaction.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 343-359https://doi.org/10.5391/IJFIS.2024.24.4.343

Fig 4.

Figure 4.

Schematic illustration of the sensitivity examination.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 343-359https://doi.org/10.5391/IJFIS.2024.24.4.343

Fig 5.

Figure 5.

Schematic illustration of different outcomes.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 343-359https://doi.org/10.5391/IJFIS.2024.24.4.343

Table 1 . SCMlosses due to various associated risks around the world.

YearDisruptions & delaysInefficienciesTheft & fraudCompliance & regulationTechnological failuresEnvironmental costsTotal estimated losses
2014$200B$150B$30B$25B$10B$5B$420B
2015$220B$155B$32B$28B$12B$6B$453B
2016$250B$160B$35B$30B$15B$7B$497B
2017$280B$165B$37B$32B$18B$8B$540B
2018$300B$170B$40B$35B$20B$9B$574B
2019$320B$175B$42B$38B$22B$10B$607B
2020$1.5T (COVID-19 impact)$180B$45B$40B$25B$12B$1.802T
2021$400B$185B$48B$43B$28B$13B$717B
2022$420B$190B$50B$45B$30B$14B$749B
2023$450B$195B$53B$48B$32B$15B$793B

Table 2 . Hesitant fuzzy pairwise comparison matrix (HPCM) at level 1.

A1A2A3A4Defuzzified local weights
A11.0000, 1.0000, 1.0000, 1.00001.0000, 1.0000, 3.0000, 5.00000.3000, 1.0000, 1.1000, 3.00001.000, 1.200, 3.000, 5.0000.050, 0.160, 0.280, 1.014
A20.200, 0.300, 1.000, 1.0001.000, 1.000, 1.000, 1.0000.200, 0.330, 1.000, 1.0000.330, 1.000, 1.000, 3.0000.035, 0.166, 0.225, 0.625
A30.330, 1.000, 1.000, 3.0001.000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0000.330, 1.000, 1.000, 3.0000.050, 0.200, 0.348, 1.263
A40.200, 0.330, 1.000, 1.0000.330, 1.000, 1.000, 3.0000.200, 0.300, 1.000, 1.0001.000, 1.000, 1.000, 1.0000.050, 0.133, 0.280, 0.940

Table 3 . At level 1, combined hesitant fuzzy possibilistic C-means (FPCM) criteria.

A1A11A12A13Defuzzified local weights

A111.0000, 1.0000, 1.0000, 1.00000.3300, 1.0000, 1.0000, 3.00001.0000, 1.0000, 1.0000, 1.00000.200, 0.330, 1.000, 1.0000.033, 0.120, 0.212, 0.781
A120.330, 1.000, 1.000, 3.0001.000, 1.000, 1.000, 1.0001.000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0000.064, 0.240, 0.426, 1.214
A130.200, 0.330, 1.000, 1.0001.000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0001.000, 1.000, 3.000, 5.0000.035, 0.097, 0.198, 0.514
A141.000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0000.200, 0.330, 1.000, 1.0001.000, 1.000, 1.000, 1.0000.032, 0.079, 0.122, 0.392

A1A21A22A23Defuzzified local weights

A211.0000, 1.0000, 1.0000, 1.00000.3300, 1.0000, 1.0000, 3.00001.0000, 1.0000, 3.0000, 5.00000.0540, 0.1330, 0.2810, 0.9480
A220.3300, 1.0000, 1.0000, 3.00001.0000, 1.0000, 1.0000, 1.00000.3300, 1.0000, 1.0000, 3.00000.0330, 0.0860, 0.1810, 0.4980
A230.2000, 0.3300, 1.0000, 1.00000.3300, 1.0000, 1.0000, 3.00001.0000, 1.0000, 1.0000, 1.00000.0480, 0.1570, 0.2710, 1.0250

A3A31A32A33A34Defuzzified local weights
A311.0000, 1.0000, 1.0000, 1.00000.2000, 0.3300, 1.0000, 1.00000.200, 0.330, 1.000, 1.0001.000, 1.000, 3.000, 5.0000.052, 0.159, 0.290, 1.030
A321.000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0001.000, 1.000, 3.000, 5.0000.200, 0.330, 1.000, 1.0000.020, 0.073, 0.113, 0.500
A330.200, 0.330, 1.000, 1.0001.000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0001.000, 1.000, 3.000, 5.0000.064, 0.240, 0.426, 1.214
A341.000, 1.000, 3.000, 5.0000.200, 0.330, 1.000, 1.0001.000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0000.149, 0.276, 0.723, 1.509

A4A41A42A43Defuzzified local weights

A411.0000, 1.0000, 1.0000, 1.00000.2000, 0.3300, 1.0000, 1.00000.330, 1.000, 1.000, 3.0000.033, 0.129, 0.212, 0.782
A42000, 1.000, 3.000, 5.0001.000, 1.000, 1.000, 1.0001.000, 1.000, 3.000, 5.0000.064, 0.240, 0.426, 1.214
A430.200, 0.330, 1.000, 1.0000.200, 0.330, 1.000, 1.0001.000, 1.000, 1.000, 1.0000.053, 0.159, 0.298, 1.026

Table 4 . Overall weights.

First level attributesLocal weightsSecond level attributesLocal weightsGlobal weightsRanks
A10.050, 0.160, 0.280, 1.014A110.033, 0.120, 0.212, 0.7810.080, 0.040, 0.164, 1.35310
A120.064, 0.240, 0.426, 1.2140.004, 0.022, 0.105, 0.7102
A130.035, 0.097, 0.198, 0.5140.004, 0.022, 0.105, 0.7117
A140.032, 0.079, 0.122, 0.3920.006, 0.040, 0.157, 1.4621

A20.035, 0.166, 0.225, 0.625A210.054, 0.133, 0.281, 0.9480.006, 0.040, 0.157, 1.46213
A220.033, 0.086, 0.181, 0.4980.006, 0.030, 0.164, 1.35314
A230.048, 0.157, 0.271, 1.0250.004, 0.022, 0.105, 0.71112

A30.050, 0.200, 0.348, 1.263A310.052, 0.159, 0.290, 1.0300.006, 0.040, 0.157, 1.4629
A320.020, 0.073, 0.113, 0.5000.004, 0.033, 0.123, 1.1146
A330.030, 0.078, 0.121, 0.3910.008, 0.062, 0.248, 1.73211
A340.149, 0.276, 0.723, 1.5090.006, 0.030, 0.164, 1.3533

A40.048, 0.157, 0.271, 1.030A410.033, 0.129, 0.212, 0.7820.004, 0.033, 0.123, 1.1148
A420.064, 0.240, 0.426, 1.2140.008, 0.062, 0.248, 1.7325
A430.053, 0.159, 0.298, 1.0260.006, 0.041, 0.173, 1.4624

Table 5 . Closeness coefficients of numerous alternatives.

Alternativesd + idiGap degreeSatisfaction degree
Systematic SCM [C1]0.050.030.3790.632
Risk identification model [C2]0.040.040.4990.527
Predicted outcome model [C3]0.040.040.5370.464
Develop response strategy [C4]0.040.030.4330.571
Regularize decision making [C5]0.040.050.550.465
Mitigation strategy [C6]0.030.050.6250.405

Table 6 . Sensitivity examination.

C1C2C3C4C5C6
Original weights0.6320.5270.4640.5710.4650.405
A110.6320.5270.4640.5710.4650.406
A120.6330.5270.4660.5890.4790.397
A130.6330.5270.4640.5710.4660.406
A140.6370.5270.470.5710.4650.415
A210.6320.5250.4640.5770.4660.415
A220.6320.5270.4640.5710.4650.424
A230.6450.5360.4630.5720.4650.405
A310.6320.5270.4640.5720.4650.406
A320.6320.5270.4790.5890.4790.39
A330.6320.5270.4640.5710.4650.424
A340.6320.5270.4640.5720.4650.406
A410.6320.5250.4640.5770.4660.415
A420.6330.5270.4640.5710.4660.406
A430.6460.5360.4780.5860.4790.415

Table 7 . Comparative analysis.

ApproachesC1C2C3C4C5C6
HF-AHP-TOPSIS0.63200.52700.46400.57100.46500.4050
AHP-TOPSIS0.63700.52700.45000.57100.46500.3890
Fuzzy AHP-TOPSIS0.61200.51400.45100.57200.46500.3980

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