Stock Forecasting using Daily Sales Transaction at Hundred Smoke Outlets with the Trend Moment Method - Aditya Prakasa; Sentot Achmadi; Joseph Dedy Irawan (Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW, 2026)
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.