Web-Based Stock Overstock Warning System for Spare Parts Inventory Using Linear Regression - Rizky Fadillah Putra Pratama; R Wisnu Prio Pamungkas; Fried Sinlae (Jurnal Kecerdasan Buatan dan Teknologi Informasi, 2026)
Inventory management in the automotive spare parts industry faces a critical challenge in managing overstock conditions, which lead to increased storage costs and capital freezing. PT. Dipo Internasional Pahala Otomotif currently manages spare parts inventory manually, without any predictive alert system capable of detecting potential overstock based on sales data. This study develops a web-based overstock warning system using the Simple Linear Regression algorithm implemented in the Laravel framework to predict spare parts stock requirements and automatically trigger overstock alerts. The system was built following the Waterfall development methodology through seven sequential phases: planning, analysis, design, coding, testing, implementation, and maintenance. The linear regression model uses time period as the independent variable (X) and stock quantity as the dependent variable (Y ), forming the prediction equation Y = a + bX. Based on a simulation with n = 4 periods, the resulting equation Y = 7 + 2.7X predicted a stock of 20.5 units in period 5, which exceeded the defined overstock threshold. System evaluation using Black Box Testing confirmed that all functional modules operated correctly. The system successfully provides automated overstock detection and real-time alert notifications, enabling more accurate and data-driven inventory decisions at PT. Dipo Internasional Pahala Otomotif.