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Setting the Grounds for the Transition from Business Analytics to Artificial Intelligence in Solving Supply Chain Risk

Author

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  • Gerda Žigienė

    (School of Economics and Business, Kaunas University of Technology, Gedimino Str. 50, LT-44249 Kaunas, Lithuania)

  • Egidijus Rybakovas

    (School of Economics and Business, Kaunas University of Technology, Gedimino Str. 50, LT-44249 Kaunas, Lithuania)

  • Rimgailė Vaitkienė

    (School of Economics and Business, Kaunas University of Technology, Gedimino Str. 50, LT-44249 Kaunas, Lithuania)

  • Vaidas Gaidelys

    (School of Economics and Business, Kaunas University of Technology, Gedimino Str. 50, LT-44249 Kaunas, Lithuania)

Abstract

As supply chains (SCs) become more complex globally, businesses are looking for efficient business analytics (BA), business intelligence (BI), and artificial intelligence (AI) tools for managing supply-chain risk. The tools and methodologies proposed by the supply-chain risk management (SCRM) literature are mostly based on experts’ judgments, their knowledge, and past data. The expert evaluation-based approach could be partly or fully replaced by AI solutions, increasing objectivity, impartiality, and impersonality, reducing sources of human mistakes, biases, and inefficiencies in SCRM. However, the transition from BA to AI in SCRM is not a self-contained process; though attractive as a vision, it is not straightforward as a management or implementation process. The purpose of this research is to explore and define the conceptual grounds for transitioning from BA to AI in SCRM. The conceptual SCRM structure, its AI suitability, and implementation terms are defined theoretically based on a literature review. A single, in-depth business case study is employed to explore the theoretically defined terms of AI-based SCRM implementation. The proposed conceptual AI-suitable SCRM structure is defined by five principal building blocks: risk events, risk-event indicators, data-processing rules and algorithms, analytical techniques, and risk event probability forecasts. The study concludes that the business environment meets AI-based SCRM-implementation terms of data existence and access. Since data on risk events and negative outcomes are limited for machine learning, experts’ experience and knowledge might be utilised to build initial rules and data-processing algorithms for AI.

Suggested Citation

  • Gerda Žigienė & Egidijus Rybakovas & Rimgailė Vaitkienė & Vaidas Gaidelys, 2022. "Setting the Grounds for the Transition from Business Analytics to Artificial Intelligence in Solving Supply Chain Risk," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11827-:d:919751
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    References listed on IDEAS

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    1. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    2. Long Gao, 2015. "Long-Term Contracting: The Role of Private Information in Dynamic Supply Risk Management," Production and Operations Management, Production and Operations Management Society, vol. 24(10), pages 1570-1579, October.
    3. GROTSCH, Volker M. & BLOME, Constantin & SCHLEPER, Martin C., 2013. "Antecedents of proactive supply chain risk management - a contingency theory perspective," LIDAM Reprints CORE 2490, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Rajendra Akerkar, 2019. "Artificial Intelligence for Business," SpringerBriefs in Business, Springer, number 978-3-319-97436-1, January.
    5. Rajendra Akerkar, 2019. "Introduction to Artificial Intelligence," SpringerBriefs in Business, in: Artificial Intelligence for Business, pages 1-18, Springer.
    6. Rameshwar Dubey & Angappa Gunasekaran & David J. Bryde & Yogesh K. Dwivedi & Thanos Papadopoulos, 2020. "Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3381-3398, June.
    7. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    8. Lee, Byung Kwon & Zhou, Rong & de Souza, Robert & Park, Jaehun, 2016. "Data-driven risk measurement of firm-to-firm relationships in a supply chain," International Journal of Production Economics, Elsevier, vol. 180(C), pages 148-157.
    9. Marzieh Mehrjoo & Zbigniew J. Pasek, 2016. "Risk assessment for the supply chain of fast fashion apparel industry: a system dynamics framework," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 28-48, January.
    10. Aqlan, Faisal & Lam, Sarah S., 2015. "A fuzzy-based integrated framework for supply chain risk assessment," International Journal of Production Economics, Elsevier, vol. 161(C), pages 54-63.
    11. Wong, Christina W.Y. & Lirn, Taih-Cherng & Yang, Ching-Chiao & Shang, Kuo-Chung, 2020. "Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization," International Journal of Production Economics, Elsevier, vol. 226(C).
    12. Tang, Ou & Nurmaya Musa, S., 2011. "Identifying risk issues and research advancements in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 133(1), pages 25-34, September.
    13. Dong, Qingxing & Cooper, Orrin, 2016. "An orders-of-magnitude AHP supply chain risk assessment framework," International Journal of Production Economics, Elsevier, vol. 182(C), pages 144-156.
    14. Célestin Elock Son, 2018. "Supply Chain Risk Management: A Review of Thirteen Years of Research," Post-Print hal-03029453, HAL.
    15. Gerda Žigienė & Egidijus Rybakovas & Robertas Alzbutas, 2019. "Artificial Intelligence Based Commercial Risk Management Framework for SMEs," Sustainability, MDPI, vol. 11(16), pages 1-23, August.
    16. Chung-Yee Lee & Xi Li & Yapeng Xie, 2013. "Procurement risk management using capacitated option contracts with fixed ordering costs," IISE Transactions, Taylor & Francis Journals, vol. 45(8), pages 845-864.
    17. Wu, Yunna & Jia, Weibing & Li, Lingwenying & Song, Zixin & Xu, Chuanbo & Liu, Fangtong, 2019. "Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation," Energy, Elsevier, vol. 182(C), pages 397-411.
    18. Lohmer, Jacob & Bugert, Niels & Lasch, Rainer, 2020. "Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study," International Journal of Production Economics, Elsevier, vol. 228(C).
    19. Rajendra Akerkar, 2019. "Employing AI in Business," SpringerBriefs in Business, in: Artificial Intelligence for Business, pages 63-74, Springer.
    20. Dmitry Ivanov & Alexandre Dolgui, 2020. "Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak," International Journal of Production Research, Taylor & Francis Journals, vol. 58(10), pages 2904-2915, May.
    21. Seyedmohsen Hosseini & Dmitry Ivanov & Alexandre Dolgui, 2020. "Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3284-3303, June.
    22. Mishra, Deepa & Sharma, R.R.K. & Kumar, Sameer & Dubey, Rameshwar, 2016. "Bridging and buffering: Strategies for mitigating supply risk and improving supply chain performance," International Journal of Production Economics, Elsevier, vol. 180(C), pages 183-197.
    23. Gu, Minhao & Yang, Lu & Huo, Baofeng, 2021. "The impact of information technology usage on supply chain resilience and performance: An ambidexterous view," International Journal of Production Economics, Elsevier, vol. 232(C).
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