Amar Ma'ruf

The skincare business in Southeast Asia is rapidly expanding, requiring SMEs to adopt digital tools that support more accurate and data-driven decision-making. This study presents the integration of the Trend Moment forecasting method into a web-based e-commerce system developed for a skincare SME. Using 24 months of historical sales data, the system automatically generates short-term demand forecasts to assist inventory planning, stock replenishment, and production scheduling. The model achieved a forecasting accuracy of 9.54% MAPE, demonstrating that simple statistical methods can provide reliable operational insights when embedded directly into digital platforms. By linking transactional data with predictive analytics, the system enables business owners to transition from intuition-based decision-making to structured, data-informed workflows. The proposed solution illustrates a practical, lightweight approach to initiating digital transformation for skincare SMEs, offering scalability toward more advanced analytical capabilities in future developments.