Large Language Models for Intelligent Decision Support in Inventory and Supply Chain Operations: A Systematic Literature Review
DOI:
https://doi.org/10.63158/journalisi.v8i2.1540Keywords:
Decision Support, Generative AI, Inventory Management, Large Language ModelsAbstract
Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), is increasingly explored to strengthen decision support in supply chain and inventory management by improving interpretability and access to analytics. However, prior work is scattered across optimization, simulation, logistics, and governance discussions, limiting clear system design guidance. This study conducts a Systematic Literature Review (SLR) following PRISMA 2020 across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar, yielding 200 records, of which 34 studies were included in qualitative synthesis. Results show that LLMs are predominantly positioned as orchestration and explanatory layers operating alongside structured components such as optimization solvers, simulation engines, and digital twins, rather than as autonomous decision-makers. Governance, organizational readiness, and trust emerge as central considerations for operational deployment. This review provides an evidence map linking LLM roles and integration architectures across supply chain and inventory contexts. While LLMs offer strong augmentation capabilities, direct empirical validation for specific contexts such as web-based inventory systems remains limited; design implications for such systems are derived from the broader corpus, underscoring the need for standardized evaluation benchmarks and targeted empirical studies.
Downloads
References
[1] L. Chen, A. Bhaumik, X. Wang, and J. Wu, “The Effects of Inventory Management on Business Efficiency,” J. Multidiscip. Res., p., 2023, doi: 10.36948/ijfmr.2023.v05i04.4877.
[2] K. Angellin, R. Oetama, and M. Amri, “Web-based inventory and sales information system: Indonesian micro small medium enterprise case study,” JOINS (J. Inf. Syst.), vol. 8, no. 1, pp. 57–66, 2023, doi: 10.33633/joins.v8i1.7977.
[3] Ö. Albayrak Ünal, B. Erkayman, and B. Usanmaz, “Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature,” Arch. Comput. Methods Eng., vol. 30, no. 4, pp. 2605–2625, 2023, doi: 10.1007/s11831-022-09879-5.
[4] K. Douaioui, R. Oucheikh, O. Benmoussa, and C. Mabrouki, “Machine Learning and Deep Learning Models for Demand Forecasting in Supply Chain Management: A Critical Review.,” Appl. Syst. Innov. (ASI), vol. 7, no. 5, 2024, doi: 10.3390/asi7050093.
[5] R. Bergsma, C. de Ruijt, and S. Bhulai, “A systematic review of machine learning approaches in inventory control optimization,” Oper. Res. Perspect., vol. 15, p. 100367, 2025, doi: 10.1016/j.orp.2025.100367.
[6] Z. Bahroun, A. Saihi, R. As’ad, and M. Tanash, “A systematic analysis of generative artificial intelligence for supply chain transformation,” Supply Chain Anal, vol. 13, p. 100188, 2026, doi: 10.1016/j.sca.2025.100188.
[7] D. Anh-Hoang, V. Tran, and L.-M. Nguyen, “Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior,” Front. Artif. Intell., vol. Volume 8-2025, 2025, doi: 10.3389/frai.2025.1622292
[8] A. R. Teixeira, J. V. Ferreira, and A. L. Ramos, “Intelligent Supply Chain Management: A Systematic Literature Review on Artificial Intelligence Contributions,” Information, vol. 16, no. 5, 2025, doi: 10.3390/info16050399.
[9] K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” Inf. Softw. Technol., vol. 64, Aug. 2015, doi: 10.1016/j.infsof.2015.03.007.
[10] B. Kitchenham, P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering-A systematic literature review,” Inf. Softw. Technol., vol. 51, pp. 7–15, Jan. 2009, doi: 10.1016/j.infsof.2008.09.009.
[11] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, 2021, doi: 10.1371/journal.pmed.1003583.
[12] I. Jackson, M. Jesus Saenz, and D. Ivanov, “From natural language to simulations: applying AI to automate simulation modelling of logistics systems,” Int. J. Prod. Res., vol. 62, no. 4, pp. 1434–1457, Feb. 2024, doi: 10.1080/00207543.2023.2276811.
[13] M. Shamsuddoha, E. A. Khan, M. M. H. Chowdhury, and T. Nasir, “Revolutionizing Supply Chains: Unleashing the Power of AI-Driven Intelligent Automation and Real-Time Information Flow,” Information, vol. 16, no. 1, 2025, doi: 10.3390/info16010026.
[14] A. Gezdur and J. Bhattacharjya, “Innovators and transformers: Enhancing supply chain employee training with an innovative application of a large language model,” Int. J. Phys. Distrib. Logist. Manag., vol. 55, no. 4, pp. 394–408, Jan. 2025, doi: 10.1108/IJPDLM-12-2023-0492.
[15] S. Ieva et al., “Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations,” Sensors, vol. 25, no. 9, 2025, doi: 10.3390/s25092696.
[16] B. Nicoletti and A. Appolloni, “The Impact of AI Foundation models on the future of digital engineering for logistics and supply Chain,” Digit. Eng., vol. 7, p. 100058, 2025, doi: 10.1016/j.dte.2025.100058.
[17] H. Fan, X. Liu, J. Y. H. Fuh, W. F. Lu, and B. Li, “Embodied intelligence in manufacturing: leveraging large language models for autonomous industrial robotics,” J. Intell. Manuf., vol. 36, no. 2, pp. 1141–1157, 2025, doi: 10.1007/s10845-023-02294-y.
[18] K. Du et al., “LLM-MANUF: An integrated framework of Fine-Tuning large language models for intelligent Decision-Making in manufacturing,” Adv. Eng. Inform., vol. 65, p. 103263, 2025, doi: 10.1016/j.aei.2025.103263.
[19] Y. Wang, J. Wang, and Z. Chu, “Multi-agent large language models as evolutionary optimizers for scheduling optimization,” Comput. Ind. Eng., vol. 206, p. 111197, 2025, doi: 10.1016/j.cie.2025.111197.
[20] M. Elbasheer et al., “Natural language-driven production planning: integrating large language models with automatic simulation model generation in manufacturing systems,” J. Intell. Manuf., 2025, doi: 10.1007/s10845-025-02732-z.
[21] J. Li, R. Wickman, S. Bhatnagar, R. K. Maity, and A. Mukherjee, “Abstract Operations Research Modeling Using Natural Language Inputs,” Information, vol. 16, no. 2, 2025, doi: 10.3390/info16020128.
[22] C. Huang et al., “Orlm: A customizable framework in training large models for automated optimization modeling,” Oper. Res., 2025, doi: 10.1287/opre.2024.1233.
[23] C. Chen, K. Zhao, J. Leng, C. Liu, J. Fan, and P. Zheng, “Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities,” Robot. Comput. Integr. Manuf., vol. 94, p. 102982, 2025, doi: 10.1016/j.rcim.2025.102982.
[24] A. Cimino, F. Longo, V. Solina, and P. Veltri, “Integrating large language models with industrial simulation for multi-level decision support: an innovation management perspective in Industry 5.0,” Eur. J. Innov. Manag., vol. 29, no. 11, pp. 27–53, Dec. 2025, doi: 10.1108/EJIM-10-2024-1246.
[25] M. Zhao, O. Hussain, Y. Zhang, M. Saberi, and A. Leshob, “Benchmarking large language models for supply chain risk identification: an extended evaluation within the LARD-SC framework,” Serv. Oriented Comput. Appl., 2025, doi: 10.1007/s11761-025-00474-7.
[26] L. Li, W. Zhu, L. Chen, and Y. Liu, “Generative AI usage and sustainable supply chain performance: A practice-based view,” Transp. Res. E Logist. Transp. Rev., vol. 192, p. 103761, 2024, doi: 10.1016/j.tre.2024.103761.
[27] R. Dubey, A. Gunasekaran, and T. Papadopoulos, “Benchmarking operations and supply chain management practices using Generative AI: Towards a theoretical framework,” Transp. Res. E Logist. Transp. Rev., vol. 189, p. 103689, 2024, doi: 10.1016/j.tre.2024.103689.
[28] G. Malhotra and R. Manzoor, “Generative artificial intelligence adoption for achieving supply chain efficiency, circularity and sustainability,” J. Enterp. Inf. Manag., pp. 1–23, Sep. 2025, doi: 10.1108/JEIM-02-2025-0072.
[29] V. Singh, L. Hughes, M. A. Albashrawi, I. Jeon, and Y. K. Dwivedi, “Generative artificial intelligence (GenAI) in procurement and supply chain management: applications, opportunities and challenges,” J. Syst. Inf. Technol., vol. 28, no. 1, pp. 123–144, Sep. 2025, doi: 10.1108/JSIT-03-2025-0139.
[30] A. El jaouhari, A. Samadhiya, A. Kumar, and S. Luthra, “Integrating generative artificial intelligence into green logistics: A systematic review and policy-oriented research agenda,” J. Clean. Prod., vol. 519, p. 146018, 2025, doi: 10.1016/j.jclepro.2025.146018.
[31] S. Moica, T. Lucian, V. Kostopoulos, A. Gligor, and N. A. Mostafa, “GenAI Technology Approach for Sustainable Warehouse Management Operations: A Case Study from the Automative Sector,” Sustainability, vol. 17, no. 20, 2025, doi: 10.3390/su17209081.
[32] B. Ji, Y. Hwang, D. Kim, J. Park, M. Ryu, and Y. Cho, “A Lightweight AI System to Generate Headline Messages for Inventory Status Summarization,” Systems, vol. 13, no. 9, 2025, doi: 10.3390/systems13090741.
[33] H. Wu, G. Li, and D. Ivanov, “The transformative power of generative AI for supply chain management: Theoretical framework and agenda,” Front. Eng. Manag., vol. 12, no. 2, pp. 425–433, 2025, doi: 10.1007/s42524-025-4240-x.
[34] A. Sharma, P. Kakkar, S. Hooda, and M. S. Kaswan, “How Does Gen- AI Reshape Supply Chain, Logistics, and Manufacturing? A Systematic Bibliometric Review,” Oper. Res. Forum, vol. 7, no. 1, p. 5, 2025, doi: 10.1007/s43069-025-00582-2.
[35] T. Boone, B. Fahimnia, R. Ganeshan, D. M. Herold, and N. R. Sanders, “Generative AI: Opportunities, challenges, and research directions for supply chain resilience,” Transp. Res. E Logist. Transp. Rev., vol. 199, p. 104135, 2025, doi: 10.1016/j.tre.2025.104135.
[36] A. Handler, K. R. Larsen, and R. Hackathorn, “Large language models present new questions for decision support,” Int. J. Inf. Manage., vol. 79, p. 102811, 2024, doi: 10.1016/j.ijinfomgt.2024.102811.
[37] S. K. Srivastava, S. Routray, S. Bag, S. Gupta, and J. Z. Zhang, “Exploring the Potential of Large Language Models in Supply Chain Management:,” J. Glob. Inf. Manag., vol. 32, no. 1, 2024, doi: 10.4018/JGIM.335125.
[38] S. Fosso Wamba, C. Guthrie, M. M. Queiroz, and S. Minner, “ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management,” Int. J. Prod. Res., vol. 62, no. 16, pp. 5676–5696, Aug. 2024, doi: 10.1080/00207543.2023.2294116.
[39] L. Li, Y. Liu, Y. Jin, T. C. E. Cheng, and Q. Zhang, “Generative AI-enabled supply chain management: The critical role of coordination and dynamism,” Int. J. Prod. Econ., vol. 277, p. 109388, 2024, doi: 10.1016/j.ijpe.2024.109388.
[40] I. Jackson, D. Ivanov, A. Dolgui, and J. Namdar, “Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation,” Int. J. Prod. Res., vol. 62, no. 17, pp. 6120–6145, Sep. 2024, doi: 10.1080/00207543.2024.2309309.
[41] M. Kmiecik, “Creating a genetic algorithm for third-party logistics’ warehouse delivery scheduling via a large language model,” J. Model. Manag. vol. 20, no. 4, pp. 1138–1162, Jan. 2025, doi: 10.1108/JM2-06-2024-0192.
[42] T. Berlec, M. Corn, S. Varljen, and P. Podržaj, “Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept,” Appl. Sci., vol. 15, no. 10, 2025, doi: 10.3390/app15105734.
[43] R. Wu, “Inventory optimization under supply chain disruptions: Leveraging large language models for human-AI collaborative decision-making,” J. King Saud Univ. Comput. Inf. Sci., 2026, doi: 10.1007/s44443-025-00458-9.
[44] S. Fosso Wamba, M. M. Queiroz, C. J. Chiappetta Jabbour, and C. (Victor) Shi, “Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence?,” Int. J. Prod. Econ., vol. 265, p. 109015, 2023, doi: 10.1016/j.ijpe.2023.109015.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
