Web-Based Stock Overstock Warning System for Spare Parts Inventory Using Linear Regression
DOI:
https://doi.org/10.69916/jkbti.v5i3.568Keywords:
Overstock, Linear Regression, Alert System, Spare Parts Inventory, LaravelAbstract
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.
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Copyright (c) 2026 Rizky Fadillah Putra Pratama, R Wisnu Prio Pamungkas, Fried Sinlae
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