DR-ARMA Method for Predicting Seblak Safety Stock - Indri Sari Dewi; Jimmy Tjen (bit-Tech, 2025)
The rapid growth of the small culinary sector intensifies competition and operational complexity, particularly in managing inventory levels to ensure product availability and customer satisfaction. This study focuses on Warung Seblak Uwak, which uses a dual demand structure: a customizable prasmanan model and predetermined Seblak bundled packages. This research specifically analyzes the demand for these bundled packages, which, despite being standardized, still exhibit complex and volatile daily patterns influenced by overall store traffic. Accurate stock management for these items is crucial for maintaining profit margins and minimizing ingredient spoilage. To address the challenge of this unpredictable demand and optimize inventory in the small F&B context, this study pioneers the application of the Demand Response-AutoRegressive Moving Average (DR-ARMA) model. This sophisticated time-series methodology, previously confined to industrial or financial risk assessment, is novel in its capacity to adapt its forecast to recent sales anomalies in a dynamic culinary setting, offering superior predictive performance over standard methods. This application fills a critical gap in F&B forecasting literature. The research analyzes inventory risks and determines the optimal safety stock for the bundled packages using DR-ARMA (1,3). The methodology utilized 127 days of sales transaction records from Warung Seblak Uwak, followed by rigorous testing. The model achieved a RMSE of 5.9310, demonstrating high predictive acacuracy. The resulting safety stock recommendations offer a quantified and robust strategy for micro and small culinary enterprises, specifically concerning their standardized products, to significantly mitigate stockout risks and reduce waste, thereby improving operational efficiency and profitability.