<![CDATA[CV. Anugerah Ajitama, a building material distribution company, faces inventory management challenges due to stock determination that is still conducted manually based on subjective estimation. This condition has led to recurring overstock and stockout issues, resulting in increased storage costs, product damage risks, and lost sales opportunities. This study aims to design and develop a web-based inventory forecasting system utilizing the Single Exponential Smoothing (SES) method, as well as to evaluate the forecasting accuracy and system functionality. System development employs the Rapid Application Development (RAD) method, encompassing the stages of requirement planning, user design, construction, and cutover. The research data consists of historical sales data of three main products water tanks, turbine ventilators, and metal roof tiles from the January 2023 to December 2025 period. The forecasting process involves testing alpha values from 0.1 to 0.9 through a trial and error approach, with the best alpha selected based on the smallest Mean Squared Error (MSE), while Mean Absolute Deviation (MAD) serves as supplementary information. The results show that the optimal alpha values are 0.1 for water tanks (MSE: 13.70, MAD: 2.60), 0.2 for turbine ventilators (MSE: 5.12, MAD: 2.02), and 0.3 for metal roof tiles (MSE: 4932.58, MAD: 53.60). The system was successfully implemented as a web-based application built with Laravel and React, featuring product data management, stock realization, sales data management, automated forecasting, report generation, notifications, and role-based access control. Furthermore, functional testing using Black Box Testing across three user roles (Super Admin, Business Owner, and Admin Staff) with a total of 133 test scenarios was successfully executed with a 100% pass rate. Based on these findings, this application can effectively serve as a decision support tool for inventory planning at CV. Anugerah Ajitama.]]>
