Fine-tuning collaboration in a Co-opetitive manufacturing supply chain

In recent years, fine-tuned Large Language Models (LLMs) have become increasingly important in manufacturers' improvement of production processes. Because of the heavy fine-tuning cost burden, firms within a supply chain have been witnessed to engage in fine-tuning collaboration, sharing efforts and results to reduce costs. Such collaboration, however, raises concerns about free-riding problems, which can be very typical in a co-opetitive supply chain. Therefore, we examine a manufacturing system including a supplier with self-brand and a manufacturer who both deploy their LLMs in production and make efforts on fine-tuning. We reveal that a higher intensity of fine-tuning collaboration creates a “suppression effect” which hampers stakeholders’ fine-tuning efforts out of the free-riding problems, and simultaneously a “synergy effect” which indicates that the overall market potential can be expanded by fine-tuning effort spillover, motivating them to invest more in fine-tuning. Driven by the dueling effects, the benefits of both the supplier and the manufacturer from fine-tuning collaboration exhibit non-monotonic relationships with respect to the collaboration intensity and the base product quality of the supplier. We further show that fine-tuning collaboration generally benefits the supplier more than the manufacturer in the co-opetitive relationship. Their incentives for collaboration are aligned if both the base product quality of the supplier and the collaboration intensity are relatively high.