Comparative Analysis of Demand Forecasting Accuracy in Sajiku Seasoned Flour Product with Software POM-QM - Fadilah Artanti Rahmania; Nur Rahmawati (Yayasan Kita Menulis, 2025)
Indonesia's growing wheat flour consumption requires precise demand forecasting to optimize supply chain management. This study evaluates the forecasting accuracy of Sajiku seasoned flour demand using three methods: Single Exponential Smoothing, Moving Average, and Linear Regression. Data processing and forecasting error calculations were performed using POM-QM software. The analysis reveals that the Linear Regression method yields the lowest forecasting error, making it the most reliable approach for predicting future demand. This study emphasizes the importance of selecting suitable forecasting techniques to improve the accuracy of demand predictions, which can enhance customer satisfaction and contribute to the long-term sustainability of businesses. The findings underscore the significance of accurate demand planning in maintaining a well-balanced supply chain and addressing market fluctuations effectively.