Spare parts inventories for heavy equipment fleets are difficult to manage because demand is erratic, lead times are long, and the financial impact of stockouts is high. In many Indonesian operators, planning still relies on simple intermittent time series. This study develops an end-to-end framework that combines modern machine learning models with interactive visual analytics to support spare parts demand planning. The framework integrates data from historical withdrawals, maintenance work orders, equipment characteristics, and logistics records into a single pipeline for cleaning, feature construction, forecasting, and decision support. Using an illustrative sample of 36 monthly observations for 60 Stock-Keeping Units (SKUs) with different criticality levels, eight forecasting families are benchmarked: ARIMA/SARIMA, Croston-type models, Random Forest, XGBoost, support vector regression, Long Short-Term Memory (LSTM) networks, a CNN-LSTM hybrid, and an ensemble stacking model. Forecast accuracy is evaluated with MAPE, RMSE, and MAE, while the managerial effect is quantified through safety stock, inventory value, and service-level indicators. Additional diagnostic analyses convert forecast errors into risk classes so that confusion matrices and ROC curves can be used to assess classification performance. The results indicate that deep learning and especially ensemble stacking outperform classical methods on intermittent series, leading to safety stock reductions of more than 40 percent at the same target service level. The visual dashboard makes these gains transparent for planners and offers a practical blueprint for heavy equipment firms that wish to industrialise data-driven spare parts management.
Research Article | Open Access | Download Full Text
Volume 5 | Issue 2 | Year 2026 | Article Id: DST-V5I2P102 DOI: https://doi.org/10.59232/DST-V5I2P102
Machine Learning Modeling and Interactive Visualisation for Heavy Equipment Spare Parts Demand Forecasting
Emirul Bahar, Happy Siringoringo
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 18 Jan 2026 | 17 Feb 2026 | 20 Mar 2026 | 30 Apr 2026 |
Citation
Emirul Bahar, Happy Siringoringo. “Machine Learning Modeling and Interactive Visualisation for Heavy Equipment Spare Parts Demand Forecasting.” DS Journal of Digital Science and Technology, vol. 5, no. 2, pp. 15-26, 2026.
Abstract
Keywords
Forecasting, Interactive Visualisation, Intermittent Demand, Machine Learning, Spare Parts.
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