A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics
As supply chains evolve toward the adoption of the Industrial Internet of Things (IIoT), vast amounts of data are collected by different systems across the manufacturing, logistics and transportation value chain. John G Russell (Transport) is a UK-based company involved in multiple lines of business...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/601 |
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| author | Ibrahim Abdullahi Hadi Larijani Dimitrios Liarokapis James Paterson David Jones Stewart Murray |
| author_facet | Ibrahim Abdullahi Hadi Larijani Dimitrios Liarokapis James Paterson David Jones Stewart Murray |
| author_sort | Ibrahim Abdullahi |
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| description | As supply chains evolve toward the adoption of the Industrial Internet of Things (IIoT), vast amounts of data are collected by different systems across the manufacturing, logistics and transportation value chain. John G Russell (Transport) is a UK-based company involved in multiple lines of business in the supply chain. As the company adopts the utilization of data intelligence as a way to collect, process and utilize data for insights, this presents an opportunity for applying artificial intelligence (AI) approaches such as reinforcement learning (RL), to identify trends, and offer recommendations for improving the sustainability and efficiency of its logistics. Preliminary results show that we can achieve up to a 20–30% reduction in carbon emissions from the fleet of a segment of the transport business lines of the Russell Group. This paper presents a holistic framework for achieving sustainable supply chains, reducing costs as well as achieving operational efficiency using a supply chain digital twin. |
| format | Article |
| id | doaj-art-6908504003cb489f87a8a482c9d99261 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6908504003cb489f87a8a482c9d992612025-01-24T13:20:00ZengMDPI AGApplied Sciences2076-34172025-01-0115260110.3390/app15020601A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in LogisticsIbrahim Abdullahi0Hadi Larijani1Dimitrios Liarokapis2James Paterson3David Jones4Stewart Murray5SMART Technology Research Center, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UKSMART Technology Research Center, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UKSMART Technology Research Center, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UKSMART Technology Research Center, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UKJohn G Russell (Transport) Limited, Deanside Road, Hillington, Glasgow G52 4XB, UKJohn G Russell (Transport) Limited, Deanside Road, Hillington, Glasgow G52 4XB, UKAs supply chains evolve toward the adoption of the Industrial Internet of Things (IIoT), vast amounts of data are collected by different systems across the manufacturing, logistics and transportation value chain. John G Russell (Transport) is a UK-based company involved in multiple lines of business in the supply chain. As the company adopts the utilization of data intelligence as a way to collect, process and utilize data for insights, this presents an opportunity for applying artificial intelligence (AI) approaches such as reinforcement learning (RL), to identify trends, and offer recommendations for improving the sustainability and efficiency of its logistics. Preliminary results show that we can achieve up to a 20–30% reduction in carbon emissions from the fleet of a segment of the transport business lines of the Russell Group. This paper presents a holistic framework for achieving sustainable supply chains, reducing costs as well as achieving operational efficiency using a supply chain digital twin.https://www.mdpi.com/2076-3417/15/2/601Internet of Things (IoT)digital twinsmachine learning (ML)reinforcement learning (RL)sustainabilitydata integration |
| spellingShingle | Ibrahim Abdullahi Hadi Larijani Dimitrios Liarokapis James Paterson David Jones Stewart Murray A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics Applied Sciences Internet of Things (IoT) digital twins machine learning (ML) reinforcement learning (RL) sustainability data integration |
| title | A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics |
| title_full | A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics |
| title_fullStr | A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics |
| title_full_unstemmed | A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics |
| title_short | A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics |
| title_sort | data intelligence driven digital twin framework for improving sustainability in logistics |
| topic | Internet of Things (IoT) digital twins machine learning (ML) reinforcement learning (RL) sustainability data integration |
| url | https://www.mdpi.com/2076-3417/15/2/601 |
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