A Hybrid Exponential Smoothing–DRP– Framework for Sustainable Distribution Optimization in a Traditional Food Rengginang

Purpose: Distribution Requirement Planning (DRP), and route optimization using the Saving Matrix method. The goal is to enhance inventory accuracy, minimize logistics costs, and improve delivery efficiency under fluctuating market demand. Methodology: A quantitative–descriptive analysis was conducted using primary data (field observation and interviews) and secondary data (production and sales records). Weekly demand forecasting was performed using the Exponential Smoothing method with α = 0.9, evaluated through Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The DRP model was used to determine gross and net requirements, while the Saving Matrix method was applied to design optimal delivery routes. Findings: The Exponential Smoothing model achieved a high predictive accuracy (MAPE = 10.33%), showing reliable short-term forecasting for MSMEs. DRP implementation with a one-week lead time and a 168-unit safety stock successfully balanced production capacity and customer demand. Integration of DRP and Saving Matrix resulted in approximately 30% reduction in total logistics cost and significant improvement in stock availability. Compared to 17 related studies (2021–2024), this hybrid model demonstrated superior efficiency and cost stability within traditional food industries. Practical Implications: The results provide MSMEs with a data-driven framework to synchronize production, inventory, and distribution planning, reducing decision-making errors and improving operational sustainability. The proposed model can serve as a replicable reference for traditional food SMEs facing fluctuating demand conditions. Value: The novelty of this study lies in combining Exponential Smoothing forecasting, DRP scheduling, and Saving Matrix routing into a unified optimization framework—rarely applied in Indonesia’s traditional food sectors. This integrative method strengthens both academic insight and managerial practice in MSME supply chain efficiency.