Comparative Analysis of Moving Average and Single Exponential Smoothing Methods for Optimizing Inventory Forecasting

Abstract

Inventory management in small retail businesses is often affected by demand uncertainty because sales recording and stock monitoring are still performed using simple procedures. This condition occurs at Toko Ipung and can lead to both overstock and stockout situations. This study aims to implement and compare the Moving Average and Single Exponential Smoothing methods to produce more measurable inventory forecasts. A quantitative comparative approach was combined with web-based system development using the Waterfall model. The main data consist of historical sales from January to December 2025; the manual calculation simulation presents 15 product types, while the overall evaluation in the study is reported for 60 product types. The system was developed using PHP and MySQL, with Moving Average and Single Exponential Smoothing using a smoothing constant of 0.2 as the forecasting algorithms. Forecast accuracy was evaluated using Mean Absolute Percentage Error, while application functions were verified through Black Box Testing. The results show that all functional test scenarios were successfully executed and that the overall average Mean Absolute Percentage Error was 15.14%, which falls into the good category. For the Beras Rojo Lele example, Moving Average produced 12.55% and Single Exponential Smoothing produced 12.50%, making the latter slightly more accurate. The system therefore provides an objective basis for procurement planning and for reducing stockout and overstock risks.