The Changing Role of Logistics and Supply Chain in The Digital World
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https://doi.org/10.14419/v5xj8865
Received date: July 19, 2025
Accepted date: September 11, 2025
Published date: September 22, 2025
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Supply Chain Digitalization; Generative AI; Large Language Model; Logistics Optimization; Knowledge Automation; SCM Innovation -
Abstract
Purpose – The use of generative artificial intelligence (GenAI) can potentially revolutionize logistics and supply chain management (SCM) practices. This research provides an analysis of how GenAI, huge language models (LLMs), are redefining SCM's operational and strategic aspects.
Design/methodology/approach – A mixed-methods approach, incorporating case studies, industry surveys, and performance data analysis, is employed to investigate the inclusion of LLMs in logistics and SCM operations across various industries, including retailing, manufacturing, and healthcare.
Findings – The preliminary findings suggest that incorporating GenAI and LLMs into SCM systems enhances the speed of decision-making by 35%, accuracy of demand forecasting by 25%, and lowers logistics costs by 18%. Major innovations are dynamic route optimization, real-time inventory management, and customized customer service through AI-powered interfaces.
Originality/value – The research makes an original contribution towards the digital evolution of SCM and logistics by presenting a systematic approach to using LLMs. It highlights strategic recommendations for managers who wish to future-proof supply chains.
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References
- Baryannis, G., Dani, S., & Antoniou, G. (2019). Predictive analytics and artificial intelligence in supply chain management. https://doi.org/10.1007/978-3-030-03813-7_4.
- Bessant, J., Kaplinsky, R. and Lamming, R. (2003), "Putting supply chain learning into practice", International Journal of Operations and Produc-tion Management, Vol. 23 No. 2, pp. 167-184, https://doi.org/10.1108/01443570310458438.
- Brockhaus, S., van Hoek, R. and DeNunzio, S. (2023), "Future-proofing supply chain education", Transportation Journal, Vol. 62 No. 4, pp. 355-368, https://doi.org/10.5325/transportationj.62.4.0355.
- Brockhaus, S., Knemeyer, M., & Taylor, D. (2023). Amplifying training performance with digital solutions.
- Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G.J., Beltran, J.R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., Dey, P.K., Guest, D., Knoblich, A.J., Malik, A., Paauwe, J., Papagiannidis, S., Patel, C., Pereira, V., Ren, S., Rogelberg, S., Saunders, M.N.K., Tung, R.L. and Varma, A. (2023), "Human resource management in the age of generative artificial intelligence: perspectives and research directions on ChatGPT", Human Resource Management Journal, Vol. 33 No. 3, pp. 606-659, https://doi.org/10.1111/1748-8583.12524.
- Cangelosi, V.E. and Dill, W.R. (2016), "Organizational learning: observations toward a theory linked references are available on JSTOR for this article: organizational learning: observations toward a theory", Vol. 10 No. 2, pp. 175-203, https://doi.org/10.2307/2391412.
- Chase, H. (2023), "LangChain: power your applications with large language models", available at: https://www.langchain.com/ (accessed 18 July 2023).
- Choi, T.-M., Wallace, S. W., & Wang, Y. (2021). Big Data Analytics in Operations and Supply Chain Management.
- Dale, R. (2022), "$NLP: how to spend a billion dollars", Natural Language Engineering, Vol. 28 No. 1, pp. 125-136, https://doi.org/10.1017/S1351324921000450.
- Doshi, R.H., Bajaj, S.S. and Krumholz, H.M. (2023), "ChatGPT: temptations of progress", American Journal of Bioethics, Vol. 23 No. 4, pp. 1-3, https://doi.org/10.1080/15265161.2023.2180110.
- Durach, C.F. and Gutierrez, L. (2024), "'Hello, this is your AI co-pilot' – operational implications of artificial intelligence chatbots", International Journal of Physical Distribution and Logistics Management, Vol. 54 No. 3, pp. 229-246, https://doi.org/10.1108/IJPDLM-01-2024-0031.
- Edureka (2022), "Supply chain modeling: types, models and best practices", Edureka, available at: https://www.edureka.co/blog/supply-chain-modeling/.
- Freifield, L. (2023), "2023 training industry report", TrainingMag, available at: https:// trainingmag.com/2023-training-industry-report/ (accessed date August 17, 2024).
- FossoWamba, S., Queiroz, M.M., ChiappettaJabbour, C.J. and (Victor) Shi, C. (2023), "Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence?", International Journal of Production Economics, Vol. 265 August, 109015, https://doi.org/10.1016/j.ijpe.2023.109015.
- Gagliardi, A.R., Festa, G., Usai, A., Dell'Anno, D. and Rossi, M. (2023), "The impact of knowledge management on the digital supply chain – a bibliometric literature review", International Journal of Physical Distribution and Logistics Management, Vol. 53 Nos 5/6, pp. 612-627, https://doi.org/10.1108/IJPDLM-07-2022-0206.
- Garg, R., Kiwelekar, A.W., Netak, L.D. and Ghodake, A. (2021), "i-Pulse: a NLP based novel approach for employee engagement in logistics or-ganization", International Journal of Information Management Data Insights, Vol. 1 No. 1, 100011, https://doi.org/10.1016/j.jjimei.2021.100011.
- Garg, A., Sharma, R., & Roy, R. (2021). Artificial Intelligence in Corporate Learning. https://doi.org/10.1201/9781003140351.
- Long, J. (2023). AI's role in supply chain digital transformation.
- Lee, U., Jung, H., Jeon, Y., Sohn, Y., Hwang, W., Moon, J. and Kim, H. (2023), "Few-shot is enough: exploring ChatGPT prompt engineering method for automatic question generation in English education", Education and Information Technologies, Vol. 29 No. 9, pp. 11483-11515, https://doi.org/10.1007/s10639-023-12249-8.
- Lee, J., woo, Yoo, I.S., Kim, J.H., Kim, W.T., Jeon, H.J., Yoo, H.S., Shin, J.G., Kim, G.H., Hwang, S.J.
- https://www.igi-global.com/article/exploring-the-potential-of-large-language-models-in-supply-chain-management/335125
- https://www.marketingeye.com/blog/marketing/case-study-how-ai-transforms-logistics-marketing-roi.html
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5370043
- https://ttms.com/improving-employee-training-with-ai-powered-solutions/
- https://www.politesi.polimi.it/retrieve/9f1da09d-256f-428c-8bae-a94d529df3e6/2024_07_Dhara_Delgado.pdf
- https://hbr.org/2025/01/how-generative-ai-improves-supply-chain-management
- https://www.sciencedirect.com/org/science/article/pii/S1062737524000489
- https://camoinassociates.com/resources/ai-in-action-part-1/
- https://document360.com/blog/technical-writing-tips/
- https://writety.hashnode.dev/the-role-of-simplification-in-technical-writing
- https://www.tredence.com/blog/llm-governance
- https://digital-library.theiet.org/doi/pdf/10.1049/icp.2025.2589?download=true
- https://dzone.com/articles/llmops-privacy-data-governance-best-practices
- https://arxiv.org/html/2404.12736v1
- https://dl.acm.org/doi/10.1145/3713081.3731747
- https://arxiv.org/pdf/2505.18597.pdf
- https://www.zendata.dev/post/choosing-the-right-data-governance-framework
- https://www.acceldata.io/blog/data-governance-model-how-leading-companies-ensure-compliance-and-security
- https://www.sciencedirect.com/org/science/article/pii/S1062737524000489
- https://genai.owasp.org/llmrisk/llm03-training-data-poisoning/
- https://www.tigera.io/learn/guides/llm-security/
- Transportation Journal, Vol. 62 No. 4, pp. 355-368, https://doi.org/10.5325/transportationj.62.4.0355.
- https://www.teradata.com/insights/ai-and-machine-learning/llm-training-costs-roi
- https://www.binadox.com/blog/llm-for-data-analysis-tools-costs-and-implementation-guide/
- https://www.vintly.com/blog/ethical-considerations-in-supply-chain-ai-implementation-navigating-the-challenges-of-automation
- https://lumenalta.com/insights/data-shows-how-logistics-leaders-turn-ai-into-roi
- https://www.fepbl.com/index.php/ijarss/article/view/1391/1627
- https://www.emerald.com/ijpdlm/article/55/4/394/1246686/Innovators-and-transformers-Enhancing-supply-chain
- Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., Pan, S. and Wen, Q. (2024), "Foundation models for time series analysis: a tutorial and survey", ArXiv, pp. 6555-6565, https://doi.org/10.1145/3637528.3671451.
- Long, M.C. (2023), "Can you trust AI to revolutionize corporate training?", Reworked, available at: https://www.reworked.co/learning-development/can-you-trust-ai-to-revolutionize-corporate- training/ (accessed 17 August 2024).
- Lund, B.D., Wang, T., Mannuru, N.R., Nie, B., Shimray, S. and Wang, Z. (2023), "ChatGPT and a new academic reality: artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing", Journal of the Association for Information Science and Technology, Vol. 74 No. 5, pp. 570-581, https://doi.org/10.1002/asi.24750.
- Oliva, R. (2019), "Intervention as a research strategy", Journal of Operations Management, Vol. 65 No. 7, pp. 710-724, https://doi.org/10.1002/joom.1065.
- O'Leary, D.E. (2023), "An analysis of three chatbots: BlenderBot, ChatGPT and LaMDA", Intelligent Systems in Accounting, Finance and Man-agement, Vol. 30 No. 1, pp. 41-54, https://doi.org/10.1002/isaf.1531.
- Park, S. and Kim, Y.J. (2024), "Development of AI-generated medical responses using the ChatGPT for cancer patients", Computer Methods and Programs in Biomedicine, Vol. 254 January, 108302, https://doi.org/10.1016/j.cmpb.2024.108302.
- Qian, C., Yu, K., Chen, N., Shen, W., Hou, S. and Lei, Y. (2023), "When to adopt a new process management standard? An organizational learning perspective", International Journal of Production Economics, Vol. 263 May, 108939, https://doi.org/10.1016/j.ijpe.2023.108939.
- Rahimi, F. and TalebiBezminAbadi, A. (2023), "ChatGPT and publication ethics", Archives of Medical Research, Vol. 54 No. 3, pp. 272-274, https://doi.org/10.1016/j.arcmed.2023.03.004.
- Richey, R., Morgan, T., &Tokman, M. (2023). Competitive advantage through AI-driven SCM.
- Rathore, B. (2023), "Future of textile: sustainable manufacturing & prediction via ChatGPT", Eduzone: International Peer Reviewed, Vol. 12 No. 1, pp. 52-62, https://doi.org/10.56614/eiprmj.v12i1y23.253.
- Richey, R.G., Chowdhury, S., Davis-Sramek, B., Giannakis, M. and Dwivedi, Y.K. (2023), "Artificial intelligence in logistics and supply chain management: a primer and roadmap for research", Journal of Business Logistics, Vol. 44 No. 4, pp. 532-549, https://doi.org/10.1111/jbl.12364.
- van Hoek, R. (2024), "Insight from industry-early lessons learned about AI adoption in core procurement processes, directions for managers and researchers", Supply Chain Management, Vol. 29 No. 4, pp. 794-803, https://doi.org/10.1108/SCM-02-2024-0143.
- von Hoek, R. (2024). Future-Proofing Supply Chains through Technology.
- Zhu, Q., Krikke, H. and Cani€els, M.C.J. (2018), "Supply chain integration: value creation through managing inter-organizational learning", Interna-tional Journal of Operations and Production Management, Vol. 38 No. 1, pp. 211-229, https://doi.org/10.1108/IJOPM-06-2015-0372.
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How to Cite
Kiranmayi , D. C. ., Sreenivas , P. T. ., Shaik , D. K. ., Devi , D. S. A. ., Subramanyam , D. M. ., & Mabunni , D. S. . (2025). The Changing Role of Logistics and Supply Chain in The Digital World. International Journal of Accounting and Economics Studies, 12(5), 885-892. https://doi.org/10.14419/v5xj8865
