Smart Manufacturing Decision Support Using LLM-Based Domain Knowledge Memory and Industrial Query Intent Analysis
Keywords:
smart manufacturing, large language models, domain knowledge memory, query intent analysis, decision support systems, industrial artificial intelligenceAbstract
The digital transformation of manufacturing has generated vast repositories of operational data, engineering documents, and process knowledge. Yet, frontline decision-makers often struggle to retrieve and apply this information effectively due to the complexity and implicit nature of industrial queries. Large language models offer new opportunities for reasoning over unstructured knowledge, but their generic pretraining limits their reliability in domain-specific production environments. This paper proposes a decision support framework that couples an LLM-based domain knowledge memory with industrial query intent analysis. The domain knowledge memory encodes manufacturing standards, failure case histories, and equipment specifications into retrievable memory units, maintained under a governance framework that ensures traceability, versioning, and factual consistency. The query intent analysis module employs multi-stage natural language understanding to disambiguate technical jargon, incomplete inputs, and layered operator goals. By dynamically linking inferred intent with the knowledge memory, the system generates context-aware, evidence-grounded recommendations. The paper examines system architecture, memory construction pipelines, intent modeling strategies, and integration with industrial control and execution systems. Emphasis is placed on structural trade-offs involving latency, scalability, robustness, fairness, and long-term knowledge sustainability. Deployment considerations across edge-cloud infrastructure, data security, and human-in-the-loop validation are discussed. Policy implications spanning workforce augmentation, algorithmic transparency, and cross-organizational knowledge sharing are analyzed. The framework is evaluated conceptually against performance dimensions of decision accuracy, response latency, and operational resilience. This work highlights how the fusion of persistent domain memory and interpretable intent analysis can overcome the brittleness of generic AI assistants, paving the way for more trustworthy, explainable, and sustainable decision support in smart manufacturing ecosystems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
