Financial interactions play a critical role in inter-organizational transactions, particularly in complex supply chains where cash flow, risk sharing, and access to credit are vital for operational continuity. However, the integration of financial mechanisms into supply chain decision-making remains underexplored, especially for small and medium-sized enterprises (SMEs) that often face significant resource constraints. This study addresses this gap by developing a dual-objective optimization model for production chain financing that balances logistical efficiency with financial viability. The proposed model employs a fuzzy ideal programming approach, allowing for decision-making under uncertainty by incorporating both operational and financial constraints into a unified mathematical framework.

The research adopts a quantitative and applied methodology, with empirical data drawn from Koroosh Food Industries, a representative SME operating in a dynamic and resource-limited environment. The model is validated through a numerical case study using LINGO optimization software, which demonstrates how different supply chain structures can influence optimal financing strategies. Results show a strong relationship between supply chain configuration and financial performance, emphasizing the importance of integrated planning in capital-constrained settings.

This study contributes to both theory and practice by providing a robust decision-support tool that aligns production planning with financial considerations. Its findings are particularly relevant for SME managers, policymakers, and supply chain analysts seeking to enhance financial resilience and operational agility. The model's flexibility also makes it adaptable to various industries where uncertainty and financing challenges intersect.