Balancing performance and innovation in AI-driven supply chains through temperature-scaled hallucination control

This study investigates how temperature scaling in generative AI (GenAI) models optimises decision-making in supply chain management by balancing accuracy and creativity. It addresses the challenge of tailoring AI-generated outputs for diverse supply chain tasks, spanning demand forecasting, inventory management, strategic planning, and process innovation. The research conducts nine experiments across key areas, evaluating AI models at varying temperature settings (low, moderate, and high) to assess their impact on accuracy, feasibility, and innovation. Results show that lower temperatures enhance precision and reliability, supporting operational efficiency, while higher temperatures foster creativity and innovation, benefiting strategic applications. Moderate temperatures strike an effective balance, enhancing adaptability in dynamic environments. The study identifies temperature scaling as a critical mechanism for improving AI-driven supply chain strategies, enabling managers to fine-tune AI models according to specific objectives. It contributes to the growing literature on AI in supply chain management by offering a structured approach to maximise AI’s value in both operational and strategic decision-making.