Stock Forecasting using Daily Sales Transaction at Hundred Smoke Outlets with the Trend Moment Method

Aditya Prakasa; Sentot Achmadi; Joseph Dedy Irawan

doi:10.47794/jesica.v3i1.36

Aditya Prakasa Software Engineering Department, Malang National Institute of Technology, Indonesia
Sentot Achmadi Software Engineering Department, Malang National Institute of Technology, Indonesia
Joseph Dedy Irawan Software Engineering Department, Malang National Institute of Technology, Indonesia

DOI: https://doi.org/10.47794/jesica.v3i1.36

Keywords: Bill of materials, forecasting, MAPE, trend moment, website

Abstract

Hundred Smoke Outlet is a culinary business experiencing fluctuating customer demand that changes weekly, creating a risk of imbalance between raw material inventory and actual needs. This research aims to develop a web-based forecasting system using the Trend Moment method that processes daily sales data and converts it into a Bill of Materials (BOM) structure to more accurately predict raw material requirements. The system is designed with two user types: admin and staff, who can manage sales data, inventory, and run the forecasting process. Based on black-box testing results for 11 scenarios across various system features, all functions performed as expected with a 100% success rate. Forecast accuracy was evaluated using the Mean Absolute Percentage Error (MAPE), which showed a maximum error of 0.50, a minimum error of 0, and an average error of 22.62%. These results indicate that the system can provide a fairly good level of accuracy in supporting raw material requirement planning.

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