Integrating third-party logistics (3PL), forecast accuracy and emission management in triadic supply chains − a large language model-based approach

This article presents an innovative and sustainable approach to enhancing triadic collaboration in supply chains by integrating forecast accuracy and transport emission management with the support of Large Language Models (LLMs). The study analyzed operational, forecasting, and environmental data from 22 triads managed by a single 3PL provider over a three-month period. The Gemini model was applied to detect anomalies, generate strategic recommendations, and support SQL-based data aggregation, enabling a holistic assessment of triadic structures. The results demonstrate that closed and concentred triads are associated with higher forecast accuracy, while forecast quality alone does not directly determine emission efficiency. The LLM successfully identified hidden inefficiencies and suggested structural transformations, such as shifting from derived to concentred or from open to closed triads, which were positively validated by an expert panel. The findings are interpreted through Resource-Based View, Dynamic Capabilities, and Network Governance, highlighting that LLMs function not only as analytical tools but also as integrators of resources and coordination mechanisms. The study contributes to theory by bridging forecasting, sustainability, and governance perspectives, and to practice by offering actionable guidelines for logistics managers. While limited by its single-case scope and the absence of financial data, the research provides a replicable methodological framework and opens avenues for applying LLMs in managing both operational performance and sustainability in supply chains.