<![CDATA[Supply chain organizations are hitting a wall with AI (Artificial Intelligence) automation initiatives because their data is a fragmented mess. Inconsistent terminology and siloed systems create a structural bottleneck that kills AI effectiveness before it can even scale. This study delivers the essential semantic infrastructure with a specialized ontology framework specifically designed to bridge these existing gaps. It transforms AI from a disconnected tool into a scalable, explainable, and high-performing asset that actually works across the entire supply chain network. Utilizing a rigorous Design Science Research (DSR) approach, this study moves beyond theory to build a high-functioning solution for real-world operational failures. By analysing documented semantic gaps in current supply chains, a domain-specific ontology was engineered to formalize every relationship, attribute, and constraint within the operation. This framework was then stress-tested through scenario analysis across procurement, forecasting, inventory management, and logistics to ensure it demonstrates potential utility and logical coherence. The results are clear, this ontology successfully bridges the gap between fragmented systems, ensuring data remains consistent and actionable across the entire network. By embedding logical rules and explicit relationships directly into the data structure, the framework facilitates interoperability across heterogeneous platforms. This doesn't just make systems talk to each other; it empowers AI to perform complex reasoning and enhances traceability for every decision made, ensuring total transparency. This research redefines the “ontology” as a strategic mandate for AI success rather than a mere modelling exercise. It proves that semantic architecture is the primary driver of AI scalability. For industry leaders, this provides a structured pathway for harmonizing data definitions and building a foundation that can support large-scale, high-stakes AI integration.]]>
