SENS: Semantic Synthetic Integrated Model for Sustainable Supply Chain Analysis and Benchmarking
DOI:
https://doi.org/10.18417/emisa.19.5Keywords:
Ontology, Supply Chain Modeling, Synthetic Data, Benchmarking, SustainabilityAbstract
Supply Chain (SC) integrated modeling is required for visibility and proactive monitoring of members and processes across the SC network. Recent works have established SC models incorporating core relations and structures. However, such models are still rather isolated, thus preventing a holistic view of the SC. We identify a lack of End-to-End (E2E) SC data that enables integrated analysis of the SC. Existing logs or data from one company are not enough to validate the E2E SC models. We present SENS, a standardized integrated semantic model that provides an overall view of SCOR E2E SC structure and flows. This vocabulary is used to generate synthetic SC data compensating for the scarcity of the overall benchmarking data via SENS-GEN. The evaluation shows that the significantly improved simulation and analysis capabilities, enabled by SENS, facilitate grasping, controlling and ultimately enhancing SC behavior and increasing resilience in disruptive scenarios.
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Copyright (c) 2024 Nour Ramzy, Sören Auer, Hans Ehm, Baptiste Perier
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