INVENTORY FORECASTING INFORMATION SYSTEM USING THE WEIGHTED MOVING AVERAGE METHOD AT TITA'S STORE

Abstract

Inventory management is a crucial factor in retail operations as it influences cost efficiency, sales continuity, and customer satisfaction. In small-scale retail businesses, inventory planning is often performed manually, increasing the risk of overstock and stockout conditions. This study aims to develop a web-based inventory forecasting information system using the Weighted Moving Average (WMA) method to support effective inventory planning. The system integrates item data management, sales transaction recording, and demand forecasting within a single platform. The WMA method is applied to 12 months of historical monthly sales data using a three-period forecasting window with an optimized weight configuration of 5–1–7 to emphasize recent demand patterns. Forecasting accuracy is evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). A case study conducted at Toko Tita shows that the WMA method outperforms the Simple Moving Average method by producing lower MAD and MAPE values, indicating better responsiveness to short-term demand fluctuations. The results demonstrate that the proposed system provides reliable quantitative information to support inventory procurement decisions, reduces manual calculation errors, and improves operational efficiency. Although forecasting errors increase during extreme demand changes, the system is practical and effective for daily inventory management in small retail businesses.