Challenges in Applying Artificial Intelligence for Supply Chain Risk Management

Gerda Zigiene, Egidijus Rybakovas and Rimgaile Vaitkiene

International Journal of Economics & Business Administration (IJEBA), 2020, vol. VIII, issue 4, 299-318

Abstract: Purpose: To define the scope and nature of challenges in applying artificial intelligence (AI) for supply chain risk management (SCRM). Design/Methodology/Approach: Initial theoretical conceptualisation and respective approach were set by following the risk management maturity framework. The scope of explored challenges was defined by two data categories (supply chain risk events’ and risk events’ indicators) that are essential for AI tools to predict risk events’ probability based on a set of risk prediction indicators. The nature of challenges is associated with the ways and forms of data collection, management, and application. The qualitative primary data research strategy was employed to explore selected case company practices associated with conceptually defined categories of scope and nature of challenges in applying AI for SCRM. Findings: The article concludes with a conceptual typology of challenges in applying AI for SCRM defined by their scope and nature along with the selected illustrative practices. Practical Implications: Empirical case study data based illustrative practices serve as research indicators or practical checklist entries for empirical evaluation of the level in progress towards the application of AI in SCRM. They also could be used as guidelines setting a direction for needed improvements in the way of applying AI for SCRM. Originality/Value: This research contributes to the SCRM literature by defining the typology of challenges according to their scope and nature in applying AI for SCRM in the context of risk management maturity.

Keywords: Artificial intelligence; supply chain risk; risk management maturity. (search for similar items in EconPapers)
JEL-codes: D81 G32 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ers:ijebaa:v:viii:y:2020:i:4:p:299-318

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