TITLE:
Supply Chain AI Education Case Library: A Multi-Dimensional Framework for Teaching Advanced Analytics in Logistics and Operations Management
AUTHORS:
Kui Meng, Jingyi Zhou, Yi Yang
KEYWORDS:
Artificial Intelligence Education, Supply Chain Management, Conceptual Framework, Educational Case Library, Data-Driven Learning
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.7,
July
17,
2026
ABSTRACT: This paper proposes a conceptual multi-dimensional framework and an associated educational case library for teaching artificial intelligence in supply chain management. The framework integrates four dimensions—data authenticity, technical sophistication, business relevance, and pedagogical effectiveness—to systematically transform real-world datasets into structured learning experiences. We present three proposed educational cases: 1) Joint demand forecasting and safety stock optimization using retail inventory transaction data; 2) Multi-objective vehicle routing and fuel efficiency optimization based on logistics operations data; 3) Logistics operational risk and route performance analysis. As a conceptual framework proposal, this paper describes the design methodology, case structures, and technical specifications of the proposed educational resource. The framework is intended to serve as a scalable template for AI education in operations management, with empirical validation identified as a critical direction for future work. All cases are designed to be computationally accessible on standard laptop hardware, lowering barriers to adoption.