Integrating digitally enhanced data extraction and simulation modelling for AI-driven supply chain resilience: an operational research framework for strategic stockpiling of critical minerals

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dc.creatorNie, Wei
dc.creatorLi, Fangrui
dc.creatorTsolakis, Naoum
dc.creatorKumar, Mukesh
dc.date2026-01-10T00:30:53Z
dc.date2026-07-03
dc.date.accessioned2026-08-03T03:52:30Z
dc.descriptionSimulation modelling in Operations Research (OR) relies critically on data quality, yet multi-echelon supply chains (SC) often lack the timely, contextual information necessary for realistic model parameterisation and scenario generation. This research proposes the “Large Language Models (LLM)-Integrated Simulation Data Framework”, a domain-agnostic methodology that systematically integrates unstructured narratives with structured datasets to enhance simulation model realism and contextual relevance. The framework employs a Retrieval-Augmented Generation pipeline powered by LLMs to extract, structure, and validate intelligence from policy documents, news archives, and industry reports, transforming qualitative narratives into traceable model inputs for parameterisation, behavioural logic, and scenario specifications. We demonstrate the framework’s applicability through India’s strategic development of its lithium carbonate reserves processing 972 documents to generate 668 validated insights that inform agent behaviours, numerical parameters, and scenario specifications. The methodological contribution establishes a novel collaborative interface between OR and Generative Artificial Intelligence, demonstrating how automated extraction of contextual knowledge at scale enables the combination of quantitative baselines with qualitative intelligence to enhance simulation empirical grounding. The lithium stockpiling case validates the framework’s applicability in data-sparse, multi-echelon contexts, yielding policy insights on strategic reserve development whilst demonstrating broader applicability to geopolitically sensitive, resource-constrained SCs.
dc.descriptionDepartment for Science, Innovation and Technology
dc.formatapplication/pdf
dc.identifier0160-5682
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/394880
dc.identifierhttps://doi.org/10.17863/CAM.124629
dc.identifier1476-9360
dc.identifier.urihttps://repo.dare.co.zw/handle/123456789/181941
dc.languageeng
dc.publisherTaylor & Francis
dc.publisherDepartment of Engineering Student
dc.publisherhttps://doi.org/10.1080/01605682.2025.2612145
dc.rightsAttribution 4.0 International
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4609 Information Systems
dc.subject4602 Artificial Intelligence
dc.titleIntegrating digitally enhanced data extraction and simulation modelling for AI-driven supply chain resilience: an operational research framework for strategic stockpiling of critical minerals
dc.typeArticle

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