Toward a Semantic SCOR-VSM Framework for Supply Chain Performance Evaluation

Abstract

Supply chain performance assessment requires models that are capable of linking strategic, tactical, and operational data in a consistent and traceable manner. SCOR provides a standardized process and performance framework, while Value Stream Mapping (VSM) captures operational flows, waste, and machine-level data. However, SCOR remains weakly connected to shop-floor observations, and VSM often supports local improvement without aggregating data into global supply chain indicators. To address this gap, this paper proposes a conceptual architecture integrating SCOR, VSM, and ontology through a semantic, data-driven pivot layer. Rather than extending SCOR to a universal Level 4, which would be subject to industry limitations, our approach normalizes VSM data at the machine level and semantically maps it to SCOR Level 3 processes. The ontology formalizes relationships among operators, activities, resources, flows, contexts, constraints, and metrics. The proposed prototype supports traceability from shop-floor data to global performance evaluation and prepares future implementation in digital supply chain environments, including process mining, industrial IoT, and digital twin-based decision support.