Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
Small and medium-sized enterprises (SMEs) face growing challenges in optimizing their sustainable supply chains because of fragmented logistics data and changing regulatory requirements. In particular, globally operating manufacturing SMEs often lack suitable tools, resulting in manual data collecti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/8001 |
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| author | Manuel Felder Matteo De Marchi Patrick Dallasega Erwin Rauch |
| author_facet | Manuel Felder Matteo De Marchi Patrick Dallasega Erwin Rauch |
| author_sort | Manuel Felder |
| collection | DOAJ |
| description | Small and medium-sized enterprises (SMEs) face growing challenges in optimizing their sustainable supply chains because of fragmented logistics data and changing regulatory requirements. In particular, globally operating manufacturing SMEs often lack suitable tools, resulting in manual data collection and making reliable accounting and benchmarking of transport emissions in lifecycle assessments (LCAs) time-consuming and difficult to scale. This paper introduces a novel hybrid AI-supported knowledge graph (KG) which combines large language models (LLMs) with graph-based optimization to automate industrial supply chain route enrichment, completion, and emissions analysis. The proposed solution automatically resolves transportation gaps through generative AI and programming interfaces to create optimal routes for cost, time, and emission determination. The application merges separate routes into a single multi-modal network which allows users to evaluate sustainability against operational performance. A case study shows the capabilities in simplifying data collection for emissions reporting, therefore reducing manual effort and empowering SMEs to align logistics decisions with Industry 5.0 sustainability goals. |
| format | Article |
| id | doaj-art-1a76c31fea1e4b738a1efd72d498ffbb |
| institution | Matheson Library |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1a76c31fea1e4b738a1efd72d498ffbb2025-07-25T13:12:56ZengMDPI AGApplied Sciences2076-34172025-07-011514800110.3390/app15148001Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven ApproachManuel Felder0Matteo De Marchi1Patrick Dallasega2Erwin Rauch3Sustainable Manufacturing Lab, Industrial Engineering and Automation (IEA), Faculty of Engineering, Free University of Bozen-Bolzano, Europastr. 9, 39031 Bruneck, ItalySustainable Manufacturing Lab, Industrial Engineering and Automation (IEA), Faculty of Engineering, Free University of Bozen-Bolzano, Europastr. 9, 39031 Bruneck, ItalySustainable Manufacturing Lab, Industrial Engineering and Automation (IEA), Faculty of Engineering, Free University of Bozen-Bolzano, Europastr. 9, 39031 Bruneck, ItalySustainable Manufacturing Lab, Industrial Engineering and Automation (IEA), Faculty of Engineering, Free University of Bozen-Bolzano, Europastr. 9, 39031 Bruneck, ItalySmall and medium-sized enterprises (SMEs) face growing challenges in optimizing their sustainable supply chains because of fragmented logistics data and changing regulatory requirements. In particular, globally operating manufacturing SMEs often lack suitable tools, resulting in manual data collection and making reliable accounting and benchmarking of transport emissions in lifecycle assessments (LCAs) time-consuming and difficult to scale. This paper introduces a novel hybrid AI-supported knowledge graph (KG) which combines large language models (LLMs) with graph-based optimization to automate industrial supply chain route enrichment, completion, and emissions analysis. The proposed solution automatically resolves transportation gaps through generative AI and programming interfaces to create optimal routes for cost, time, and emission determination. The application merges separate routes into a single multi-modal network which allows users to evaluate sustainability against operational performance. A case study shows the capabilities in simplifying data collection for emissions reporting, therefore reducing manual effort and empowering SMEs to align logistics decisions with Industry 5.0 sustainability goals.https://www.mdpi.com/2076-3417/15/14/8001sustainable manufacturingIndustry 5.0lifecycle assessmentsustainable supply chainsmall and medium sized enterprises |
| spellingShingle | Manuel Felder Matteo De Marchi Patrick Dallasega Erwin Rauch Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach Applied Sciences sustainable manufacturing Industry 5.0 lifecycle assessment sustainable supply chain small and medium sized enterprises |
| title | Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach |
| title_full | Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach |
| title_fullStr | Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach |
| title_full_unstemmed | Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach |
| title_short | Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach |
| title_sort | smart routing for sustainable supply chain networks an ai and knowledge graph driven approach |
| topic | sustainable manufacturing Industry 5.0 lifecycle assessment sustainable supply chain small and medium sized enterprises |
| url | https://www.mdpi.com/2076-3417/15/14/8001 |
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