<![CDATA[Material inventory management at a telecommunication industry warehouse frequently experiences imbalances due to unpredictable monthly demand fluctuations, triggering the risk of material shortages or excessive stockpiling. This study aims to implement predictive computation into the supply chain information system to forecast the volume of material requirements for maintenance work. The quantitative forecasting method used is the Single Moving Average (SMA) algorithm, which extracts actual historical expenditure data. The accuracy level of the system's projection results is evaluated mathematically using the Mean Absolute Deviation (MAD) instrument. Furthermore, the system was developed using the Software Development Life Cycle (SDLC) Waterfall model and the CodeIgniter framework. Testing was conducted by comparing the moving average parameters for a three-month (=3) and a six-month (=6) period. The system's computation results on operational data show that the algorithm can dynamically calculate projections with MAD error values that vary depending on the fluctuation of the material type, such as ODP forecasting (=3) recording a MAD deviation of 1.06, and Iron Poles (=6) with a MAD of 0.83. This study proves that integrating the SMA algorithm into the warehouse database can serve as a reliable reference parameter for management in determining measurable material procurement volumes.]]>
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