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Integrating Generative Artificial Intelligence into Supply Chain Management Education Using the SCOR Model

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Advanced Information Systems Engineering Workshops (CAiSE 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 521))

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Abstract

Bridging rule-based Supply Chain Management (SCM) systems with Generative Artificial Intelligence (GenAI) presents a novel approach towards overcoming persistent SCM challenges. This study introduces a novel approach that integrates GenAI with the Supply Chain Operations Reference (SCOR) Model, a widely accepted quasi-ontology in SCM, through Retrieval-Augmented Generation (RAG). Utilizing Google’s Vertex AI Search as an implementation case in an educational context, we demonstrate the practical application of resulting generative SCM (GenSCM), which seeks to combine the advantages of both symbolic and sub-symbolic AI. Our study contributes to the literature by outlining an approachable pathway for integrating GenAI in SCM, and it provides insights on a domain-specific integration of symbolic and sub-symbolic AI. While the findings illustrate the potential of GenSCM in education, future research is needed on superior SCM problem-solving and operational execution in real-life SCM settings.

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Notes

  1. 1.

    Please see [1]. SCOR, in the current ‘Digital Standard’ version, is a comprehensive and relational, organising SCM processes (orchestrate, plan, order, source, transform, fulfill, return) and mapping them onto a hierarchical taxonomy (strategic to operational), including performance metrics (resilience, economic, sustainability) and human resources (skills, experiences, training), making it a foundation for strategic and operational excellence in SCM.

  2. 2.

    While this does not involve real-time supply chain data analysis and execution, it serves as a suitable environment to test the integration of GenAI with the SCOR model and to explore its capability in understanding the interconnected elements of SCM as empirical evidence of on GenAI in SCM is limited [17].

  3. 3.

    The most well-known information management related issue in SCM is the bullwhip effect, in which small variations in demand amplify through the supply chain, leading to substantial overstock and/or shortages due to a lack of information-sharing and synchronized decisions.

  4. 4.

    To unify data without tooling such as Vertex AI, a common approach is using vector databases, which convert data into vectors for efficient analysis of complex datasets across different data types.

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Authors and Affiliations

  1. University of Applied Sciences and Arts Northwester Switzerland FHNW, 5210, Windisch, Switzerland

    Joachim C. F. Ehrenthal & Phillip Gachnang

  2. Google Cloud Platform, Mountain View, CA, 94043, USA

    Louisa Loran, Hellmer Rahms & Fabian Schenker

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  1. Joachim C. F. Ehrenthal
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Correspondence to Joachim C. F. Ehrenthal .

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Editors and Affiliations

  1. Federal University of Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  2. Utrecht University, Utrecht, The Netherlands

    Claudio Di Ciccio

  3. University of the Aegean, Mytilene, Greece

    Christos Kalloniatis

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Ehrenthal, J.C.F., Gachnang, P., Loran, L., Rahms, H., Schenker, F. (2024). Integrating Generative Artificial Intelligence into Supply Chain Management Education Using the SCOR Model. In: Almeida, J.P.A., Di Ciccio, C., Kalloniatis, C. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2024. Lecture Notes in Business Information Processing, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-031-61003-5_6

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