Development of a Machine Learning-Based Predictive Model for Forecasting Spare Part Requirements at the Warehouse of PT. Setia Karya Transport (Great Giant Foods) Way Lunik
Abstract
Efficient spare part management plays a crucial role in supporting operational continuity at PT. Setia Karya Transport (Great Giant Foods). The current spare part forecasting process is still reactive and relies on periodic evaluations, resulting in potential inefficiencies in procurement planning. This study aims to develop a machine learning-based predictive model to forecast spare part requirements using historical transaction data from January to July 2025. The research applied three modeling scenarios: (1) a hybrid model combining Support Vector Regression (SVR), Random Forest, and Statistical methods; (2) pure statistical methods with zero-ratio classification; and (3) the XGBoost algorithm with zero-ratio classification. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. The results showed that the hybrid approach achieved the best performance with an MAE of 2.919 and an RMSE of 8.056, indicating higher prediction accuracy compared to other models. The findings demonstrate that integrating machine learning with statistical approaches can effectively enhance forecasting accuracy and support data-driven decision-making in warehouse management.
Keywords : Machine Learning, Forecasting, Spare Part, Random Forest, Support Vector Regression
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