Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization
In today’s volatile supply chain (SC) environment, competition has shifted beyond individual companies to the entire SC ecosystem. Reducing overall SC costs is crucial for success and benefits all participants. One effective approach to achieve this is through digital transformation, enhancing SC co...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/182107 |
| _version_ | 1826124865190494208 |
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| author | Zhang, Bo Tan, Wen Jun Cai, Wentong Zhang, Allan N. |
| author2 | College of Computing and Data Science |
| author_facet | College of Computing and Data Science Zhang, Bo Tan, Wen Jun Cai, Wentong Zhang, Allan N. |
| author_sort | Zhang, Bo |
| collection | NTU |
| description | In today’s volatile supply chain (SC) environment, competition has shifted beyond individual companies to the entire SC ecosystem. Reducing overall SC costs is crucial for success and benefits all participants. One effective approach to achieve this is through digital transformation, enhancing SC coordination via information sharing, and establishing decision policies among entities. However, the risk of unauthorized leakage of sensitive information poses a significant challenge. We aim to propose a Privacy-preserving Multi-agent Reinforcement Learning (PMaRL) method to enhance SC visibility, coordination, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning with additional SC connectivity information to improve training performance. The simulation-based evaluation results illustrate that the PMaRL method surpasses traditional optimization methods in achieving cost performance comparable to full visibility methods, all while preserving privacy. This research addresses the dual objectives of information security and cost reduction in SC inventory management, aligning with the broader trend of digital transformation. |
| first_indexed | 2025-03-09T13:53:02Z |
| format | Journal Article |
| id | ntu-10356/182107 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T13:53:02Z |
| publishDate | 2025 |
| record_format | dspace |
| spelling | ntu-10356/1821072025-01-08T01:18:47Z Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization Zhang, Bo Tan, Wen Jun Cai, Wentong Zhang, Allan N. College of Computing and Data Science School of Mechanical and Aerospace Engineering Singapore Institute of Manufacturing Technology, A*STAR Computer and Information Science Digital transformation Data-driven decision making In today’s volatile supply chain (SC) environment, competition has shifted beyond individual companies to the entire SC ecosystem. Reducing overall SC costs is crucial for success and benefits all participants. One effective approach to achieve this is through digital transformation, enhancing SC coordination via information sharing, and establishing decision policies among entities. However, the risk of unauthorized leakage of sensitive information poses a significant challenge. We aim to propose a Privacy-preserving Multi-agent Reinforcement Learning (PMaRL) method to enhance SC visibility, coordination, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning with additional SC connectivity information to improve training performance. The simulation-based evaluation results illustrate that the PMaRL method surpasses traditional optimization methods in achieving cost performance comparable to full visibility methods, all while preserving privacy. This research addresses the dual objectives of information security and cost reduction in SC inventory management, aligning with the broader trend of digital transformation. National Research Foundation (NRF) Published version This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2022-031). 2025-01-08T01:18:47Z 2025-01-08T01:18:47Z 2024 Journal Article Zhang, B., Tan, W. J., Cai, W. & Zhang, A. N. (2024). Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization. Sustainability (Switzerland), 16(22), 9996-. https://dx.doi.org/10.3390/su16229996 2071-1050 https://hdl.handle.net/10356/182107 10.3390/su16229996 2-s2.0-85210588393 22 16 9996 en AISG-RP-2022-031 Sustainability (Switzerland) © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Computer and Information Science Digital transformation Data-driven decision making Zhang, Bo Tan, Wen Jun Cai, Wentong Zhang, Allan N. Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization |
| title | Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization |
| title_full | Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization |
| title_fullStr | Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization |
| title_full_unstemmed | Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization |
| title_short | Leveraging multi-agent reinforcement learning for digital transformation in supply chain inventory optimization |
| title_sort | leveraging multi agent reinforcement learning for digital transformation in supply chain inventory optimization |
| topic | Computer and Information Science Digital transformation Data-driven decision making |
| url | https://hdl.handle.net/10356/182107 |
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