Analytical Framework for Spare Parts Inventory Control Using K-Means and Adaptive Forecasting
Keywords:
Inventory Control, K-Means Clustering, Intermittent Demand, Adaptive ForecastingAbstract
The increasing complexity of power plant operations has made spare parts inventory management increasingly critical. At PLTU XYZ, inventory value increased from IDR 11.85 billion in 2023 to IDR 20.79 billion in 2025, while inventory turnover decreased from 4.23 to 1.99 times per year and deadstock remained around IDR 3–4 billion. The existing system still relies on material classification and engineering evaluation, while demand forecasting has not been systematically applied. This study develops an analytical framework based on K-Means clustering and adaptive forecasting. Clustering of 1,215 items using criticality, availability, usage value, ADI, and CV² produced four clusters with a mean silhouette coefficient of 0.45. In the forecasting stage, nine sample items were evaluated using SES, Croston, SBA, TSB, mSBA, and mTSB through grid search. The results show that SBA performed best for four items, mSBA for three items, and Croston and TSB for one item each. Rolling-origin evaluation indicated that five items achieved method dominance of 85.7%–100%. This framework supports adaptive inventory control and Inventory Policy development.
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[1] J. D. Croston, “Forecasting and Stock Control for Intermittent Demands,” Journal of the Operational Research Society, vol. 23, no. 3, pp. 289–303, Sep. 1972, doi: 10.1057/jors.1972.50.
[2] J. E. Boylan and A. A. Syntetos, “On the bias of intermittent demand estimates,” 2001.
[3] R. H. Teunter, A. A. Syntetos, and M. Z. Babai, “Intermittent demand: Linking forecasting to inventory obsolescence,” Eur. J. Oper. Res., vol. 214, no. 3, pp. 606–615, 2011, doi: 10.1016/j.ejor.2011.05.018.
[4] M. Z. Babai, Y. Dallery, S. Boubaker, and R. Kalai, “A new method to forecast intermittent demand in the presence of inventory obsolescence,” Int. J. Prod. Econ., vol. 209, pp. 30–41, Mar. 2019, doi: 10.1016/j.ijpe.2018.01.026.
[5] Ç. Pinçe, L. Turrini, and J. Meissner, “Intermittent demand forecasting for spare parts: A Critical review,” Omega (Westport)., vol. 105, p. 102513, Dec. 2021, doi: 10.1016/J.OMEGA.2021.102513.
[6] M. Z. Babai, H. Chen, A. A. Syntetos, and D. Lengu, “A compound-Poisson Bayesian approach for spare parts inventory forecasting,” Int. J. Prod. Econ., vol. 232, p. 107954, Feb. 2021, doi: 10.1016/J.IJPE.2020.107954.
[7] J. Mehringer et al., “Forecasting Spare Parts Demand with Clustering Approaches and Functional Approximations,” 2019.
[8] E. Balugani, F. Lolli, R. Gamberini, B. Rimini, and A. Regattieri, “Clustering for inventory control systems,” in IFAC-PapersOnLine, Elsevier B.V., Jan. 2018, pp. 1174–1179. doi: 10.1016/j.ifacol.2018.08.431.
[9] X. Zhuang, Y. Yu, and A. Chen, “A combined forecasting method for intermittent demand using the automotive aftermarket data,” Data Science and Management, vol. 5, no. 2, pp. 43–56, Jun. 2022, doi: 10.1016/j.dsm.2022.04.001.
[10] X. Tian, H. Wang, and E. E, “Forecasting intermittent demand for inventory management by retailers: A new approach,” Journal of Retailing and Consumer Services, vol. 62, Sep. 2021, doi: 10.1016/j.jretconser.2021.102662.
[11] G. Chawla and V. M. Miceli, “Demand Forecasting and Inventory Management for Spare Parts,” 2019.
[12] A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 651–666, Jun. 2010, doi: 10.1016/j.patrec.2009.09.011.
[13] M. Caserta and L. D’Angelo, “Intermittent demand forecasting for spare parts with little historical information,” Journal of the Operational Research Society, vol. 76, no. 2, pp. 294–309, 2025, doi: 10.1080/01605682.2024.2349734.
[14] A. A. Syntetos, J. E. Boylan, and J. D. Croston, “On the Categorization of Demand Patterns,” J. Oper. Res. Soc., vol. 56, no. 5, pp. 495–503, 2005, [Online]. Available: http://www.jstor.org/stable/4102103
[15] Srilekha S and Adhilakshmi, “Comparative Evaluation of K-Means, Hierarchical Clustering, and DBSCAN in Blood Donor Segmentation,” 2024. [Online]. Available: www.ijfmr.com
[16] S. D. Prestwich, S. A. Tarim, and R. Rossi, “Intermittency and obsolescence: A Croston method with linear decay,” Int. J. Forecast., vol. 37, no. 2, pp. 708–715, Apr. 2021, doi: 10.1016/j.ijforecast.2020.08.010.
[17] Y. Yang, C. Ding, S. Lee, L. Yu, and F. Ma, “A modified Teunter-Syntetos-Babai method for intermittent demand forecasting,” Journal of Management Science and Engineering, vol. 6, no. 1, pp. 53–63, Mar. 2021, doi: 10.1016/j.jmse.2021.02.008.
[18] D. Annie Rose Nirmala, V. Kannan, M. Thanalakshmi, S. Joe Patrick Gnanaraj, and M. Appadurai, “Inventory management and control system using ABC and VED analysis,” Mater. Today Proc., vol. 60, pp. 922–925, Jan. 2022, doi: 10.1016/j.matpr.2021.10.315.
[19] K. Yang, Y. Wang, S. Fan, and A. Mosleh, “Multi‐criteria spare parts classification using the deep convolutional neural network method,” Applied Sciences (Switzerland), vol. 11, no. 15, Aug. 2021, doi: 10.3390/app11157088.
[20] I. A. Hendra Putri and D. S. Gabriel, “ABC-FSN Analysis for Inventory Management Policy: A Case Study in Retail Industry,” IEOM Society International, Sep. 2024. doi: 10.46254/na09.20240185.
[21] H. Hewamalage, K. Ackermann, and C. Bergmeir, “Forecast evaluation for data scientists: common pitfalls and best practices,” Data Min. Knowl. Discov., vol. 37, no. 2, pp. 788–832, Mar. 2023, doi: 10.1007/s10618-022-00894-5.
[22] N. Paranthaman, H. N. Perera, N. T. Thalagala, and D. Kosgoda, “Evaluating intermittent demand forecasting techniques for spare part supply chains,” IFAC-PapersOnLine, vol. 59, no. 10, pp. 136–141, 2025, doi: 10.1016/j.ifacol.2025.09.025.
[23] L. Li, Y. Kang, F. Petropoulos, and F. Li, “Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications,” Aug. 2022, [Online]. Available: http://arxiv.org/abs/2204.08283
[24] L. Fan, X. Liu, W. Mao, K. Yang, and Z. Song, “Spare Parts Demand Forecasting Method Based on Intermittent Feature Adaptation,” Entropy, vol. 25, no. 5, May 2023, doi: 10.3390/e25050764.
[25] K. Sanguri, S. Patra, K. Nikolopoulos, and S. Punia, “Intermittent demand, inventory obsolescence, and temporal aggregation forecasts,” Int. J. Prod. Res., vol. 62, no. 5, pp. 1663–1685, 2024, doi: 10.1080/00207543.2023.2199435.
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