| Title: | Using large language models (LLMs) to support simulation-based optimization in supply chain management |
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| Authors: | ID Wiśniewski, T. (Author) |
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| Files: |  APEM20-4_491-506.pdf (998,48 KB)
MD5: 7F3E2FAFDAF67DF9478CD7D75323BCC3
 https://apem-journal.org/Archives/2025/APEM20-4_491-506.pdf
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | The emergence of Artificial Intelligence (AI) in Supply Chain Management (SCM) heralds a transformative shift, breaking traditional barriers and unlocking new opportunities for optimization and efficiency. This study explores the impact of artificial intelligence, particularly large language models (LLMs), on simulation-based optimization applications in supply chain management. The novelty of LLMs lies in their ability to enhance both the technical and practical aspects of simulation-based optimization. On the technical side, LLMs can assist in constructing and fine-tuning optimization models by analyzing historical data, identifying patterns, and generating recommendations for optimal strategies. On the practical side, these models have the potential to simplify complex methodologies, making them more comprehensible and actionable for practitioners without extensive expertise in AI or advanced analytics. The article presents practical implications of LLMs in the form of a ChatGPT-based application, in which users express their supply chain challenges in natural language, and the model responds with tailored optimization strategies or simulation scenarios. The presented examples demonstrate how LLMs can automatically generate simulation models and support optimization processes in typical supply chain management scenarios. These results are preliminary and highlight both the potential of this approach and its current limitations, including occasional inaccuracies in the generated code. |
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| Keywords: | supply chain management, simulation-based optimization, artificial intelligence, large language models, LLMs, generative AI, conversational AI, ChatGPT, AI-driven decision-making |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 03.07.2025 |
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| Article acceptance date: | 15.12.2025 |
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| Publication date: | 31.12.2025 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 491-506 |
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| Numbering: | Vol. 20, no. 4 |
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| PID: | 20.500.12556/DKUM-96728  |
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| UDC: | 004.8 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 266140419 |
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| DOI: | 10.14743/apem2025.4.554 |
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| Publication date in DKUM: | 26.01.2026 |
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| Views: | 239 |
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| Downloads: | 6 |
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| Metadata: |    |
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| Categories: | Misc.
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