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Enabling Explainable Artificial Intelligence capabilities in Supply Chain Decision Support Making

Author

Listed:
  • F. Olan
  • K. Spanaki

    (Audencia Business School)

  • W. Ahmed
  • G. Zhao

Abstract

Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to conduct a thorough analysis of the existing literature on decision support systems (DSSs) and their incorporation of XAI functionalities within the domain of SC. Our analysis has revealed the influence of XAI on the decision-making process in the field of SC. This study utilizes the SHapley Additive exPlanations (SHAP) technique to analysis the online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted to augment the lucidity of ML models by furnishing rationales for the prognostications they produce.The present study aims to establish measurable standards for identifying the constituents of XAI and DSSs that augment decision-making in the context of SC. This study assessed prior research with regards to their ability to make predictions, utilization of online dataset, number of variables examined, development of learning capability, and validation within the context of decision-making, emphasizes the research domains that necessitate additional exploration concerning intelligent decision-making under conditions of uncertainty.

Suggested Citation

  • F. Olan & K. Spanaki & W. Ahmed & G. Zhao, 2025. "Enabling Explainable Artificial Intelligence capabilities in Supply Chain Decision Support Making," Post-Print hal-05018234, HAL.
    • Handle: RePEc:hal:journl:hal-05018234
    DOI: 10.1080/09537287.2024.2313514
    Note: View the original document on HAL open archive server: https://hal.science/hal-05018234v2
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    References listed on IDEAS

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    1. repec:dar:wpaper:137446 is not listed on IDEAS
    2. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    3. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    4. Olan, Femi & Ogiemwonyi Arakpogun, Emmanuel & Suklan, Jana & Nakpodia, Franklin & Damij, Nadja & Jayawickrama, Uchitha, 2022. "Artificial intelligence and knowledge sharing: Contributing factors to organizational performance," Journal of Business Research, Elsevier, vol. 145(C), pages 605-615.
    5. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    6. Femi Olan & Shaofeng Liu & Jana Suklan & Uchitha Jayawickrama & Emmanuel Ogiemwonyi Arakpogun, 2022. "The role of Artificial Intelligence networks in sustainable supply chain finance for food and drink industry," International Journal of Production Research, Taylor & Francis Journals, vol. 60(14), pages 4418-4433, July.
    7. Rameshwar Dubey & Angappa Gunasekaran & Stephen J. Childe & Samuel Fosso Wamba & David Roubaud & Cyril Foropon, 2021. "Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience," International Journal of Production Research, Taylor & Francis Journals, vol. 59(1), pages 110-128, January.
    8. Luis C. Dias & Humberto Rocha, 2023. "A stochastic method for exploiting outranking relations in multicriteria choice problems," Annals of Operations Research, Springer, vol. 321(1), pages 165-189, February.
    9. Cankaya, Burak & Topuz, Kazim & Delen, Dursun & Glassman, Aaron, 2023. "Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents," Omega, Elsevier, vol. 120(C).
    10. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    11. Sebastian Bach & Alexander Binder & Grégoire Montavon & Frederick Klauschen & Klaus-Robert Müller & Wojciech Samek, 2015. "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-46, July.
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