Objective: This study aims to design, implement, and evaluate a localized bank credit management model for the fast-moving consumer goods (FMCG) supply chain in Iran to enhance supply chain efficiency and sustainability in high-risk, low-transparency markets by reducing financial costs, improving cash flow, and managing risk.

Method: This study was conducted through a pilot project involving 30 retailers. The proposed model, named the Integrated Credit Payment System (SIPA), was designed and evaluated by analyzing real operational data extracted from distributors’ financial records and banking reports. The data included purchase volumes, allocated credit amounts, financial costs, operational revenues, and cash flows over specified periods. Key performance indicators such as cash conversion cycle, accounts receivable turnover, credit utilization rate, and credit balances were calculated using computational models in Excel and iterative simulations. To validate the model, descriptive and inferential statistical analyses including mean comparison tests and analysis of variance were performed to examine significant differences in performance before and after implementation. Additionally, sensitivity analysis was conducted on critical parameters to assess the model’s robustness under varying economic and operational conditions. This approach enabled precise performance analysis at a small scale and estimation of its scalability to larger operations.

Findings: The proposed model successfully covered a substantial portion of retailers’ credit needs while maintaining credit balances at manageable levels, preventing unproductive accumulation of bank resources. By aligning financial and goods flows, this approach significantly increased cash flow velocity and notably shortened the receivables repayment period. Statistical analysis showed that performance indicators such as receivables turnover ratio and cash conversion cycle improved significantly after model implementation. This not only reduced financial pressure on supply chain stakeholders but also substantially improved payment transparency. The integrated SIPA system enabled precise monitoring of credit usage and provided tools for targeted default risk management by categorizing retailers based on credit behavior. High adoption rates among retailers demonstrated its operational viability as a daily financial tool. High credit dynamism in the operational cycle optimized bank resource allocation efficiency by preventing capital dormancy and resource lock-in. Moreover, the model generated sustainable economic returns for the operator and established a balance among different stakeholders’ interests. Sensitivity analysis revealed that the model remains stable against interest rate fluctuations and credit ceiling changes, maintaining performance under varying economic conditions. The system’s rigorous credit assessment mechanisms prevented credit allocation to high-risk requests, and the high charge-to-payment ratio indicated full integration of resources into the purchasing cycle. Financial costs, including interest and late penalties, were limited compared to total credit volume, confirming orderly repayments and effective default risk control. Heterogeneity in retailers’ credit behavior also demonstrated the model’s flexibility in covering diverse consumption patterns and emphasized the necessity of customer behavior-based policymaking.

Conclusion: The proposed model demonstrated its efficacy at pilot scale through liquidity enhancement, improved credit utilization monitoring, and significant reduction in default risk, showing scalability potential to broader retailer networks. This approach provides a sustainable solution for managing liquidity challenges in the FMCG supply chain under volatile economic conditions by balancing stakeholder interests, substantially reducing financial costs, and increasing operational transparency. The model ensures the financial sustainability of the chain by providing necessary tools for macro-level planning of banking resources and managing peak credit demand. Furthermore, it contributes to overall chain efficiency by reducing dependence on costly bank loans and preventing resource diversion to unrelated activities. The model’s flexibility in responding to diverse retailer needs and its ability to maintain dynamic cash flow make it an effective tool for credit policymaking in low-transparency environments. Statistical results indicate that localized models such as SIPA outperform standard global frameworks in high-risk economies, and sensitivity analysis confirms its robustness against fluctuations in key parameters, positioning it as an operational solution for improving the financial and operational efficiency of supply chains.