Supply chain efficiency and economic sustainability in the food processing industry

Introduction The food processing industry is essential to global food security and economic stability, yet its supply chains are increasingly exposed to operational complexity, fluctuating demand, and stricter sustainability requirements. Existing supply chain management approaches often struggle to respond effectively to these challenges because they lack sufficient adaptability and cannot adequately capture uncertainty in dynamic operating environments. This study aims to develop an integrated optimization framework that improves supply chain efficiency while supporting long-term economic sustainability in the food processing industry. Methods The proposed framework is designed to address key practical problems, including resource allocation, routing adjustment, and uncertainty-aware decision-making under changing supply and demand conditions. To achieve this goal, we introduce the Probabilistic Agent Planner, which combines Constraint-guided Optimization, event-driven Routing, and Bayesian Uncertainty Modeling within a unified planning architecture. The framework is further enhanced by a Probabilistic Supply Chain Decision Refinement strategy that iteratively updates decisions using uncertainty information and adaptive optimization mechanisms. Through this design, the proposed method provides a coordinated solution that links feasibility control, real-time responsiveness, and probabilistic reasoning, rather than treating them as isolated procedures. Results and Discussion This integrated structure improves the robustness and scalability of supply chain planning and makes the framework more suitable for complex food processing scenarios. Experimental results show that the proposed method consistently improves resource utilization, routing accuracy, and system resilience compared with conventional approaches, with operational efficiency gains of up to 25%. These results indicate that the framework can effectively reduce inefficiencies caused by uncertainty and dynamic disruptions while maintaining sustainable economic performance. The study therefore offers a practical and extensible solution for advancing more efficient, resilient, and sustainability-oriented supply chain management in the food processing industry.