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Developing Supply Chain Risk Management Strategies by Using Counterfactual Explanation

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Service-Oriented Computing – ICSOC 2022 Workshops (ICSOC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13821))

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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.

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References

  1. 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)

    Article  Google Scholar 

  2. Kanamori, K., Takagi, T., Kobayashi, K., Arimura, H.: DACE: distribution-aware counterfactual explanation by mixed-integer linear optimization. In: IJCAI, pp. 2855–2862 (2020)

    Google Scholar 

  3. 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. 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)

    Google Scholar 

  5. Bajaj, M., et al.: Robust counterfactual explanations on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 5644–5655 (2021)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. Tiwari, S.: DataCo smart supply chain for big data analysis. www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis

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Author information

Authors and Affiliations

  1. School of Business, UNSW Canberra, Canberra, ACT, Australia

    Amir Hossein Ordibazar & Omar Hussain

  2. School of Engineering and Information Technology, UNSW Canberra, Canberra, ACT, Australia

    Ripon K. Chakrabortty

  3. School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia

    Morteza Saberi

  4. School of Civil and Environmental Engineering, UNSW Sydney, Kensington, NSW, Australia

    Elnaz Irannezhad

Authors
  1. Amir Hossein Ordibazar
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  2. Omar Hussain
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  3. Ripon K. Chakrabortty
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  4. Morteza Saberi
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  5. Elnaz Irannezhad
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Corresponding author

Correspondence to Amir Hossein Ordibazar .

Editor information

Editors and Affiliations

  1. University of Malaga, Málaga, Spain

    Javier Troya

  2. Politecnico di Milano, Milano, Italy

    Raffaela Mirandola

  3. University of Castilla-La Mancha, Albacete, Spain

    Elena Navarro

  4. University of the Republic, Montevideo, Uruguay

    Andrea Delgado

  5. University of Seville, Sevilla, Spain

    Sergio Segura

  6. University of Cádiz, Cádiz, Spain

    Guadalupe Ortiz

  7. Faculty of Informatics, Universita della Svizzera Italiana, Lugano, Switzerland

    Cesare Pautasso

  8. Karlsuhe Institute of Technology, Karlsruhe, Germany

    Christian Zirpins

  9. University of Seville, Seville, Spain

    Pablo Fernández

  10. ISA, Universidad de Sevilla, Sevilla, Spain

    Antonio Ruiz-Cortés

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Ordibazar, A.H., Hussain, O., Chakrabortty, R.K., Saberi, M., Irannezhad, E. (2023). Developing Supply Chain Risk Management Strategies by Using Counterfactual Explanation. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-26507-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26506-8

  • Online ISBN: 978-3-031-26507-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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