Abstract
JNE Kopma UNY's logistics demand increases 2% per week, the manual courier allocation system is unable to keep up, predictions are accurate and real-time is needed. However, there is no exact accuracy for this case. The study was conducted to determine the accuracy of the smallest Mean Percentage Error (MPE) at JNE Kopma UNY by observing sales data for 2022 to predict demand. The data was analyzed using the Naive Approach, SMA and WMA methods, then measured using the MPE to determine the best forecasting method. The software used is Microsoft Excel 2016. The 3 Month (SMA) produces the highest sales prediction (Rp. 38,668,850) for January 2023 compared to Naive Approach (Rp. 35,086,330) and 3 Month WMA (Rp. 37,993,380). 3 Month SMA is proven to be superior with the lowest MPE (12.07%) and consistent performance. Naive Approach is inaccurate (MPE 20.26%) and 3 Month WMA performance is unstable (MPE 14.27%). This research recommends 3 Month SMA for JNE Kopma UNY to increase the accuracy of sales predictions and optimize stock. Opportunities for further research are open regarding the application of 3 Month SMA in various contexts.
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