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Organizing Supply Chain Data, Services, and Showcases through Knowledge-Driven Federated Management

Year: 2025
Type: conference-paper
Abstract: Supply chains are the backbone of modern economies, relying increasingly on accurate, diverse, and up-to-date data sources to optimize logistics, reduce costs, and enhance resilience. In today's interconnected world, the strategic use of supply chain data is essential for operational excellence, risk mitigation, and innovation. Robust data management infrastructures are necessary to ensure that supply chain data remains accessible, interoperable, and reusable. Projects such as CoyP and PAIRS address these challenges by publishing metadata and providing the basis for a data mesh approach. ResilienMesh complements these efforts by focusing on federated data integration, enabling collaboration between data providers, domain experts, and stakeholders across heterogeneous infrastructures. Managing data in a federated, mesh-oriented environment introduces new challenges, particularly in the supply chain domain. Datasets and services originate from diverse projects that require uniform metadata descriptions to enable integration and reuse. The Leibniz Data Manager (LDM) has been adapted to meet these demands within ResilienMesh. LDM provides a data management framework that collects, organizes, and publishes Research Digital Objects (RDOs), including datasets and services, while ensuring compliance with the FAIR data principles. Resilience-Mesh-LDM addresses key challenges of supply chain data management by harmonizing metadata across projects and enabling semantic interoperability. It incorporates standardized vocabularies (e.g., DCAT) to describe datasets, services, and organizations uniformly. By leveraging Knowledge Graphs (KGs), Resilience-Mesh-LDM dynamically enriches metadata, establishing semantic connections between related RDOs from different sources. This approach facilitates integrated search, discovery, and recommendation mechanisms across federated catalogs, supporting interdisciplinary supply chain research and analysis. A key innovation is that Resilience-Mesh-LDM does not only manage isolated datasets but enables the construction of a knowledge-driven data ecosystem. This ecosystem connects datasets, services, and showcases across projects and institutions, fostering collaboration between previously siloed stakeholders. Through semantic annotations and knowledge graph federation, LDM supports the dynamic linking of datasets with computational services, ensuring that data products can be consumed, extended, and reused across different research and operational workflows. Stakeholders from multiple projects can publish, discover, and integrate resources seamlessly, facilitating agile responses to emerging supply chain challenges. An important feature is the introduction of Showcases. Showcases allow users to create curated, thematic collections of datasets and services tied to specific use cases, such as supply chain disruptions triggered by natural disasters. These showcases provide a unified and stylized presentation of resources, enhancing dissemination, reuse, and the visibility of interconnected assets across the ecosystem. Moreover, the linkages established within and across showcases contribute to a continuously evolving and machine-actionable knowledge space for supply chain research and innovation. In conclusion, the Resilience-Mesh-LDM emerges as a critical enabler for building federated, resilient, and supply chain data ecosystems. By harmonizing metadata descriptions, enriching datasets through semantic connections, linking services and datasets, and fostering the creation of showcases, Resilience-Mesh-LDM empowers stakeholders to navigate the increasing complexity of supply chains. It facilitates more transparent, reusable, and interoperable data infrastructures, ultimately supporting data-driven innovation and resilience in global supply chain networks.
Authors: Iglesias, Enrique, Sakor, Ahmad, Vidal, Maria-Esther
Language: English
Open Access status: green
OpenAlex ID: W6931114087
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