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

Author

Listed:
  • 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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