Enhancing Spare Parts Management through Support Vector Regression: A Case Study in the Service and Maintenance Industry - Riwayadi, Eko; Suprajitno, Herry; Miswanto, Miswanto (Moneter: Jurnal Keuangan dan Perbankan, 2024)
This study investigates the effectiveness of Support Vector Regression (SVR) in accurately predicting spare part usage within the service and maintenance industry, with a specific focus on coolers and freezers. Leveraging historical data from Hutama spanning January 2019 to December 2023, the SVR model successfully forecasts future spare parts demand with remarkable precision. Through rigorous parameter tuning using the grid search with optimization method, the SVR model achieves optimal performance, yielding reliable predictions with MAPE 8.55% and RMSE 9.43. Despite its effectiveness, limitations include the study's narrow focus on coolers and freezers within the service and maintenance sector, potential influences of external factors on prediction accuracy, and assumptions regarding the linear or nonlinear patterns in spare parts usage data.