<![CDATA[Inventory management in the automotive spare parts industry faces a criticalchallenge in managing overstock conditions, which lead to increased storagecosts and capital freezing. PT. Dipo Internasional Pahala Otomotif currentlymanages spare parts inventory manually, without any predictive alert systemcapable of detecting potential overstock based on sales data. This study developsa web-based overstock warning system using the Simple Linear Regressionalgorithm implemented in the Laravel framework to predict spare parts stockrequirements and automatically trigger overstock alerts. The system was builtfollowing the Waterfall development methodology through seven sequentialphases: planning, analysis, design, coding, testing, implementation, and maintenance.The linear regression model uses time period as the independentvariable (X) and stock quantity as the dependent variable (Y ), forming theprediction 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 period5, which exceeded the defined overstock threshold. System evaluation usingBlack Box Testing confirmed that all functional modules operated correctly.The system successfully provides automated overstock detection and real-timealert notifications, enabling more accurate and data-driven inventory decisionsat PT. Dipo Internasional Pahala Otomotif.]]>
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