Skip to main content
Top

2023 | OriginalPaper | Chapter

Developing Supply Chain Risk Management Strategies by Using Counterfactual Explanation

Authors : Amir Hossein Ordibazar, Omar Hussain, Ripon K. Chakrabortty, Morteza Saberi, Elnaz Irannezhad

Published in: Service-Oriented Computing – ICSOC 2022 Workshops

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Supply Chain Risk Management (SCRM) is necessary for economic development and the well-being of society. Therefore, many researchers and practitioners focus on developing new methods to identify, assess, mitigate and monitor supply chain risks. This paper developed the Risk Management by Counterfactual Explanation (RMCE) framework to manage risks in Supply Chain Networks (SCNs). The RMCE framework focuses on monitoring SCN, and in case of any risks eventuating, it explains them to the user and recommends mitigation strategies to avoid them proactively. RMCE uses optimisation models to design the SCN and Counterfactual Explanation (CE) to generate mitigation recommendations. The developed approach is applied to an actual case study related to a global SCN to test and validate the proposed framework. The final results show that the RMCE framework can correctly predict risks and give understandable explanations and solutions to mitigate the impact of the monitored risks on the case study.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Harinarayan, R.R.A., Shalinie, S.M.: XFDDC: eXplainable Fault Detection Diagnosis and Correction framework for chemical process systems. Process Saf. Environ. Prot. 165, 463–474 (2022)CrossRef Harinarayan, R.R.A., Shalinie, S.M.: XFDDC: eXplainable Fault Detection Diagnosis and Correction framework for chemical process systems. Process Saf. Environ. Prot. 165, 463–474 (2022)CrossRef
2.
go back to reference Kanamori, K., Takagi, T., Kobayashi, K., Arimura, H.: DACE: distribution-aware counterfactual explanation by mixed-integer linear optimization. In: IJCAI, pp. 2855–2862 (2020) Kanamori, K., Takagi, T., Kobayashi, K., Arimura, H.: DACE: distribution-aware counterfactual explanation by mixed-integer linear optimization. In: IJCAI, pp. 2855–2862 (2020)
3.
go back to reference Ordibazar, A.H., Hussain, O., Saberi, M.: A recommender system and risk mitigation strategy for supply chain management using the counterfactual explanation algorithm. In: International Conference on Service-Oriented Computing, pp. 103–116. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14135-5_8 Ordibazar, A.H., Hussain, O., Saberi, M.: A recommender system and risk mitigation strategy for supply chain management using the counterfactual explanation algorithm. In: International Conference on Service-Oriented Computing, pp. 103–116. Springer, Cham (2022). https://​doi.​org/​10.​1007/​978-3-031-14135-5_​8
4.
go back to reference Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., Flach, P.: FACE: feasible and actionable counterfactual explanations. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 344–350 (2020) Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., Flach, P.: FACE: feasible and actionable counterfactual explanations. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 344–350 (2020)
5.
go back to reference Bajaj, M., et al.: Robust counterfactual explanations on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 5644–5655 (2021) Bajaj, M., et al.: Robust counterfactual explanations on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 5644–5655 (2021)
6.
go back to reference Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617 (2020) Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617 (2020)
7.
go back to reference Tran, K.H., Ghazimatin, A., Saha Roy, R.: Counterfactual explanations for neural recommenders. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1627–1631 (2021) Tran, K.H., Ghazimatin, A., Saha Roy, R.: Counterfactual explanations for neural recommenders. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1627–1631 (2021)
8.
go back to reference Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A., Ivanov, D.: A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. Int. J. Inf. Manag. 49, 86–97 (2019)CrossRef Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A., Ivanov, D.: A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. Int. J. Inf. Manag. 49, 86–97 (2019)CrossRef
9.
go back to reference Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B.E., Kazancoglu, Y., Narwane, V.: Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. Int. J. Logistics Manag. (2021) Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B.E., Kazancoglu, Y., Narwane, V.: Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. Int. J. Logistics Manag. (2021)
10.
go back to reference Budiman, S.D., Rau, H.: A stochastic model for developing speculation-postponement strategies and modularization concepts in the global supply chain with demand uncertainty. Comput. Ind. Eng. 158, 107392 (2021)CrossRef Budiman, S.D., Rau, H.: A stochastic model for developing speculation-postponement strategies and modularization concepts in the global supply chain with demand uncertainty. Comput. Ind. Eng. 158, 107392 (2021)CrossRef
11.
go back to reference Schätter, F., Hansen, O., Wiens, M., Schultmann, F.: A decision support methodology for a disaster-caused business continuity management. Decis. Support Syst. 118, 10–20 (2019)CrossRef Schätter, F., Hansen, O., Wiens, M., Schultmann, F.: A decision support methodology for a disaster-caused business continuity management. Decis. Support Syst. 118, 10–20 (2019)CrossRef
12.
go back to reference Gupta, S., Modgil, S., Meissonier, R., Dwivedi, Y.K.: Artificial intelligence and information system resilience to cope with supply chain disruption. In: IEEE Transactions on Engineering Management (2021) Gupta, S., Modgil, S., Meissonier, R., Dwivedi, Y.K.: Artificial intelligence and information system resilience to cope with supply chain disruption. In: IEEE Transactions on Engineering Management (2021)
Metadata
Title
Developing Supply Chain Risk Management Strategies by Using Counterfactual Explanation
Authors
Amir Hossein Ordibazar
Omar Hussain
Ripon K. Chakrabortty
Morteza Saberi
Elnaz Irannezhad
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-26507-5_5

Premium Partner