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

2024; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-031-61003-5_6

1865-1356

Joachim C.F. Ehrenthal, Phillip Gachnang, Louisa Loran, H. Rahms, Fabian Schenker,

Business Process Modeling and Analysis

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.