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
Torky Althaqafi
College of Business, University of Jeddah, Jeddah, Saudi Arabia
Correspondence to :
Torky Althaqafi (tmalthaqafi@uj.edu.sa)
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
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).
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.
Torky Althaqafi
College of Business, University of Jeddah, Jeddah, Saudi Arabia
Correspondence to:Torky Althaqafi (tmalthaqafi@uj.edu.sa)
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
Hierarchy diagram of the SCM.
Process diagram of HF-AHP and HF-TOPSIS.
Schematic illustration of the level of satisfaction.
Schematic illustration of the sensitivity examination.
Schematic illustration of different outcomes.
Table 1 . SCMlosses due to various associated risks around the world.
Year | Disruptions & delays | Inefficiencies | Theft & fraud | Compliance & regulation | Technological failures | Environmental costs | Total 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.
A1 | A2 | A3 | A4 | Defuzzified local weights | |
---|---|---|---|---|---|
A1 | 1.0000, 1.0000, 1.0000, 1.0000 | 1.0000, 1.0000, 3.0000, 5.0000 | 0.3000, 1.0000, 1.1000, 3.0000 | 1.000, 1.200, 3.000, 5.000 | 0.050, 0.160, 0.280, 1.014 |
A2 | 0.200, 0.300, 1.000, 1.000 | 1.000, 1.000, 1.000, 1.000 | 0.200, 0.330, 1.000, 1.000 | 0.330, 1.000, 1.000, 3.000 | 0.035, 0.166, 0.225, 0.625 |
A3 | 0.330, 1.000, 1.000, 3.000 | 1.000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 0.330, 1.000, 1.000, 3.000 | 0.050, 0.200, 0.348, 1.263 |
A4 | 0.200, 0.330, 1.000, 1.000 | 0.330, 1.000, 1.000, 3.000 | 0.200, 0.300, 1.000, 1.000 | 1.000, 1.000, 1.000, 1.000 | 0.050, 0.133, 0.280, 0.940 |
Table 3 . At level 1, combined hesitant fuzzy possibilistic C-means (FPCM) criteria.
A11 | A12 | A13 | Defuzzified local weights | |||
A11 | 1.0000, 1.0000, 1.0000, 1.0000 | 0.3300, 1.0000, 1.0000, 3.0000 | 1.0000, 1.0000, 1.0000, 1.0000 | 0.200, 0.330, 1.000, 1.000 | 0.033, 0.120, 0.212, 0.781 | |
A12 | 0.330, 1.000, 1.000, 3.000 | 1.000, 1.000, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 0.064, 0.240, 0.426, 1.214 | |
A13 | 0.200, 0.330, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 0.035, 0.097, 0.198, 0.514 | |
A14 | 1.000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 0.200, 0.330, 1.000, 1.000 | 1.000, 1.000, 1.000, 1.000 | 0.032, 0.079, 0.122, 0.392 | |
A21 | A22 | A23 | Defuzzified local weights | |||
A21 | 1.0000, 1.0000, 1.0000, 1.0000 | 0.3300, 1.0000, 1.0000, 3.0000 | 1.0000, 1.0000, 3.0000, 5.0000 | 0.0540, 0.1330, 0.2810, 0.9480 | ||
A22 | 0.3300, 1.0000, 1.0000, 3.0000 | 1.0000, 1.0000, 1.0000, 1.0000 | 0.3300, 1.0000, 1.0000, 3.0000 | 0.0330, 0.0860, 0.1810, 0.4980 | ||
A23 | 0.2000, 0.3300, 1.0000, 1.0000 | 0.3300, 1.0000, 1.0000, 3.0000 | 1.0000, 1.0000, 1.0000, 1.0000 | 0.0480, 0.1570, 0.2710, 1.0250 | ||
A31 | A32 | A33 | A34 | Defuzzified local weights | ||
A31 | 1.0000, 1.0000, 1.0000, 1.0000 | 0.2000, 0.3300, 1.0000, 1.0000 | 0.200, 0.330, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 0.052, 0.159, 0.290, 1.030 | |
A32 | 1.000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 0.200, 0.330, 1.000, 1.000 | 0.020, 0.073, 0.113, 0.500 | |
A33 | 0.200, 0.330, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 0.064, 0.240, 0.426, 1.214 | |
A34 | 1.000, 1.000, 3.000, 5.000 | 0.200, 0.330, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 0.149, 0.276, 0.723, 1.509 | |
A41 | A42 | A43 | Defuzzified local weights | |||
A41 | 1.0000, 1.0000, 1.0000, 1.0000 | 0.2000, 0.3300, 1.0000, 1.0000 | 0.330, 1.000, 1.000, 3.000 | 0.033, 0.129, 0.212, 0.782 | ||
A42 | 000, 1.000, 3.000, 5.000 | 1.000, 1.000, 1.000, 1.000 | 1.000, 1.000, 3.000, 5.000 | 0.064, 0.240, 0.426, 1.214 | ||
A43 | 0.200, 0.330, 1.000, 1.000 | 0.200, 0.330, 1.000, 1.000 | 1.000, 1.000, 1.000, 1.000 | 0.053, 0.159, 0.298, 1.026 |
Table 4 . Overall weights.
First level attributes | Local weights | Second level attributes | Local weights | Global weights | Ranks |
---|---|---|---|---|---|
A1 | 0.050, 0.160, 0.280, 1.014 | A11 | 0.033, 0.120, 0.212, 0.781 | 0.080, 0.040, 0.164, 1.353 | 10 |
A12 | 0.064, 0.240, 0.426, 1.214 | 0.004, 0.022, 0.105, 0.710 | 2 | ||
A13 | 0.035, 0.097, 0.198, 0.514 | 0.004, 0.022, 0.105, 0.711 | 7 | ||
A14 | 0.032, 0.079, 0.122, 0.392 | 0.006, 0.040, 0.157, 1.462 | 1 | ||
A2 | 0.035, 0.166, 0.225, 0.625 | A21 | 0.054, 0.133, 0.281, 0.948 | 0.006, 0.040, 0.157, 1.462 | 13 |
A22 | 0.033, 0.086, 0.181, 0.498 | 0.006, 0.030, 0.164, 1.353 | 14 | ||
A23 | 0.048, 0.157, 0.271, 1.025 | 0.004, 0.022, 0.105, 0.711 | 12 | ||
A3 | 0.050, 0.200, 0.348, 1.263 | A31 | 0.052, 0.159, 0.290, 1.030 | 0.006, 0.040, 0.157, 1.462 | 9 |
A32 | 0.020, 0.073, 0.113, 0.500 | 0.004, 0.033, 0.123, 1.114 | 6 | ||
A33 | 0.030, 0.078, 0.121, 0.391 | 0.008, 0.062, 0.248, 1.732 | 11 | ||
A34 | 0.149, 0.276, 0.723, 1.509 | 0.006, 0.030, 0.164, 1.353 | 3 | ||
A4 | 0.048, 0.157, 0.271, 1.030 | A41 | 0.033, 0.129, 0.212, 0.782 | 0.004, 0.033, 0.123, 1.114 | 8 |
A42 | 0.064, 0.240, 0.426, 1.214 | 0.008, 0.062, 0.248, 1.732 | 5 | ||
A43 | 0.053, 0.159, 0.298, 1.026 | 0.006, 0.041, 0.173, 1.462 | 4 |
Table 5 . Closeness coefficients of numerous alternatives.
Alternatives | Gap degree | Satisfaction degree | ||
---|---|---|---|---|
Systematic SCM [C1] | 0.05 | 0.03 | 0.379 | 0.632 |
Risk identification model [C2] | 0.04 | 0.04 | 0.499 | 0.527 |
Predicted outcome model [C3] | 0.04 | 0.04 | 0.537 | 0.464 |
Develop response strategy [C4] | 0.04 | 0.03 | 0.433 | 0.571 |
Regularize decision making [C5] | 0.04 | 0.05 | 0.55 | 0.465 |
Mitigation strategy [C6] | 0.03 | 0.05 | 0.625 | 0.405 |
Table 6 . Sensitivity examination.
C1 | C2 | C3 | C4 | C5 | C6 | |
---|---|---|---|---|---|---|
Original weights | 0.632 | 0.527 | 0.464 | 0.571 | 0.465 | 0.405 |
A11 | 0.632 | 0.527 | 0.464 | 0.571 | 0.465 | 0.406 |
A12 | 0.633 | 0.527 | 0.466 | 0.589 | 0.479 | 0.397 |
A13 | 0.633 | 0.527 | 0.464 | 0.571 | 0.466 | 0.406 |
A14 | 0.637 | 0.527 | 0.47 | 0.571 | 0.465 | 0.415 |
A21 | 0.632 | 0.525 | 0.464 | 0.577 | 0.466 | 0.415 |
A22 | 0.632 | 0.527 | 0.464 | 0.571 | 0.465 | 0.424 |
A23 | 0.645 | 0.536 | 0.463 | 0.572 | 0.465 | 0.405 |
A31 | 0.632 | 0.527 | 0.464 | 0.572 | 0.465 | 0.406 |
A32 | 0.632 | 0.527 | 0.479 | 0.589 | 0.479 | 0.39 |
A33 | 0.632 | 0.527 | 0.464 | 0.571 | 0.465 | 0.424 |
A34 | 0.632 | 0.527 | 0.464 | 0.572 | 0.465 | 0.406 |
A41 | 0.632 | 0.525 | 0.464 | 0.577 | 0.466 | 0.415 |
A42 | 0.633 | 0.527 | 0.464 | 0.571 | 0.466 | 0.406 |
A43 | 0.646 | 0.536 | 0.478 | 0.586 | 0.479 | 0.415 |
Table 7 . Comparative analysis.
Approaches | C1 | C2 | C3 | C4 | C5 | C6 |
---|---|---|---|---|---|---|
HF-AHP-TOPSIS | 0.6320 | 0.5270 | 0.4640 | 0.5710 | 0.4650 | 0.4050 |
AHP-TOPSIS | 0.6370 | 0.5270 | 0.4500 | 0.5710 | 0.4650 | 0.3890 |
Fuzzy AHP-TOPSIS | 0.6120 | 0.5140 | 0.4510 | 0.5720 | 0.4650 | 0.3980 |
Trupti Bhosale and Hemant Umap
International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(1): 19-29 https://doi.org/10.5391/IJFIS.2024.24.1.19Amany Mohamed Elhosiny, Haitham El-Ghareeb, Bahaa T. Shabana, and Ahmed AbouElfetouh
International Journal of Fuzzy Logic and Intelligent Systems 2021; 21(1): 12-28 https://doi.org/10.5391/IJFIS.2021.21.1.12Hierarchy diagram of the SCM.
|@|~(^,^)~|@|Process diagram of HF-AHP and HF-TOPSIS.
|@|~(^,^)~|@|Schematic illustration of the level of satisfaction.
|@|~(^,^)~|@|Schematic illustration of the sensitivity examination.
|@|~(^,^)~|@|Schematic illustration of different outcomes.