Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics
<i>Background</i>: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in red...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Logistics |
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| Online Access: | https://www.mdpi.com/2305-6290/5/4/84 |
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| author | Ahmed Zainul Abideen Veera Pandiyan Kaliani Sundram Jaafar Pyeman Abdul Kadir Othman Shahryar Sorooshian |
| author_facet | Ahmed Zainul Abideen Veera Pandiyan Kaliani Sundram Jaafar Pyeman Abdul Kadir Othman Shahryar Sorooshian |
| author_sort | Ahmed Zainul Abideen |
| collection | DOAJ |
| description | <i>Background</i>: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. <i>Methods</i>: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. <i>Results</i>: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. <i>Conclusion</i>: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm. |
| format | Article |
| id | doaj-art-c4ac4a9f2d06435e96c3f054bf5fce75 |
| institution | DOAJ |
| issn | 2305-6290 |
| language | English |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Logistics |
| spelling | doaj-art-c4ac4a9f2d06435e96c3f054bf5fce752025-08-20T03:20:13ZengMDPI AGLogistics2305-62902021-11-01548410.3390/logistics5040084Digital Twin Integrated Reinforced Learning in Supply Chain and LogisticsAhmed Zainul Abideen0Veera Pandiyan Kaliani Sundram1Jaafar Pyeman2Abdul Kadir Othman3Shahryar Sorooshian4Institute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, MalaysiaInstitute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, MalaysiaInstitute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, MalaysiaInstitute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, MalaysiaDepartment of Business Administration, University of Gothenburg, 41124 Gothenburg, Sweden<i>Background</i>: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. <i>Methods</i>: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. <i>Results</i>: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. <i>Conclusion</i>: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.https://www.mdpi.com/2305-6290/5/4/84digital twindata-driven technologylean manufacturingsupply chain 4.0reinforced learningsimulation modelling |
| spellingShingle | Ahmed Zainul Abideen Veera Pandiyan Kaliani Sundram Jaafar Pyeman Abdul Kadir Othman Shahryar Sorooshian Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics Logistics digital twin data-driven technology lean manufacturing supply chain 4.0 reinforced learning simulation modelling |
| title | Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics |
| title_full | Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics |
| title_fullStr | Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics |
| title_full_unstemmed | Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics |
| title_short | Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics |
| title_sort | digital twin integrated reinforced learning in supply chain and logistics |
| topic | digital twin data-driven technology lean manufacturing supply chain 4.0 reinforced learning simulation modelling |
| url | https://www.mdpi.com/2305-6290/5/4/84 |
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