LLM-MANUF: An integrated framework of Fine-Tuning large language models for intelligent Decision-Making in manufacturing

Intelligent decision-making is pivotal for unlocking the value of industrial knowledge and enhancing the manufacturing sector across diverse scenarios. However, traditional decision-making methods in manufacturing fail to fully capture the complex interrelationships among various components, often resulting in biased decisions. As a novel productivity tools, large language models (LLMs) have strong contextual semantic parsing capabilities. Therefore, this paper proposes a fine-tuned LLMs integration framework for intelligent decision-making in manufacturing. The framework enables the extraction of decision-making information from diverse feature subspaces through multiple parallel fine-tuned LLMs, which generate several preliminary decision-making plans. Subsequently, the framework models the probabilities of these plans to derive a ranked list of candidates. It then employs RoBERTa and a Dynamic Weighted Mixture of Experts Ranking Method (DWMOE) to perform multi-dimensional feature extraction and candidate ranking, guided by a multi-metric head. Finally, the best fine-tuned LLM is used to fuse the top-ranked candidates, minimizing bias in the final decision-making process. To evaluate the efficacy of LLM-MANUF, we construct a dataset of manufacturing product equipment operation and maintenance texts based on a specific automotive enterprise. The results indicate that the LLM-MANUF not only outperforms individual fine-tuned LLMs but also matches the performance of LLMs with 30B parameters, achieving a BLEU-4 score of 83.37 points, which demonstrates exceptional reliability and effectiveness. LLM-MANUF provides a powerful intelligent decision-making support tool for manufacturing decision-making models.