Web-Based F&B Lazatto Product Sales and Stock Prediction System with Double Moving Average (DMA) Method - Zarasky, Dzira Faza; Alda, Muhamad (Journal of Applied Informatics and Computing, 2025)
This study aims to develop a web-based sales and stock prediction system for Lazatto, a Food and Beverage (F&B) company, using the Double Moving Average (DMA) method. The background of this research is based on issues stock requirement planning is still done conventionally, where the head of the restaurant places stock orders solely based on personal experience and intuition, without utilizing past sales data as a basis for decision-making, which often result in overstocking or stockouts. By implementing a web-based forecasting information system, the company can obtain real-time and structured data. This study uses sales data from April 2024 to March 2025. The prediction results show a downward trend in sales for the "Kentang" (Potato) product, with a forecasted value of 107.33 for April 2025, compared to an actual value of 95. Model evaluation indicates an average MAPE of 21.19%, which is considered a "fair" level of forecasting accuracy. Additionally, the time required for weekly stock planning was reduced, and interviews with staff revealed increased user satisfaction and ease of use. The developed system has proven to support more accurate and efficient decision-making in inventory management.