Inventory Management of Primary Transmission Equipments in an Electric Power Company: A Comparative Study of Continuous Review and Periodic Review Methods
DOI:
https://doi.org/10.21070/prozima.v10i1.1784Keywords:
Continuous Review, Inventory Management, Periodic Review, Primary Transmission Equipments, Service LevelAbstract
This study investigates the inventory management system of primary transmission equipment (MTU) in an electric power company and compares the effectiveness of continuous and periodic review methods. This study is motivated by a surge in the failure rate of critical components in 2022, as well as procurement demand patterns dominated by intermittent (98 items) and lumpy (56 items) characteristics with total procurement value above Rp 2.1 trillion. The methodology consists of material classification, combination of the ADI ‑ CV and ABC methods, a calculation of inventory parameters through continuous review (s, Q) model for lumpy materials and periodic review (R, s, S) model for intermittent materials with a review interval (R) of six months, chosen to align with the company's semiannual stock opname policy, and validation using a Monte Carlo simulation on a sample of 20 MTUs from three main transmission units: UIT JBB, UIT JBT, and UIT JBM. The findings demonstrate that the current condition has reached a service level of 0.00% for all materials. The results of implementing the models show an increase in service levels to averages of 63.19% (UIT JBB), 69.31% (UIT JBT), and 66.21% (UIT JBM), with an overall service level of 68.82%. Service level standard deviations varied from 7.65% to 27.01% and depended on the material properties for inventory parameters. The study found that intermittent materials are better suited to a periodic review, while lumpy materials are more effectively managed with a continuous review. Furthermore, the Monte Carlo simulation is required to validate the parameters. The recommended policies are phased implementation beginning with the best-performing material (CVT150‑RFQ 007, service level 76.13%), limited joint inventory for high‑value items, and the creation of a real‑time inventory information system.
References
[1] D. Ermawanto, “Pengelolaan Spare Parts Inventory dengan Metode Continuous Review, Periodic Review dan Reliability di Warehouse PT. XYZ,” Institut Teknologi Sepuluh Nopember, Surabaya, 2020.
[2] M. Rizma, “Peramalan dan Pengendalian Persediaan Material pada Industri Distribusi Listrik,” Institut Teknologi Sepuluh Nopember, 2023.
[3] W. F. Riskianto, “Pengendalian Persediaan Consumable Material dan Spare Part pada Sistem Transmisi Tenaga Listrik,” Institut Teknologi Sepuluh Nopember, Surabaya, 2021.
[4] E. A. Silver, D. F. Pyke, and D. J. Thomas, Inventory and Production Management in Supply Chain, 4th ed. CRC Press, 2017.
[5] R. J. Tersine, Principles of Inventory and Materials Management, 4th ed. Prentice Hall, 1994.
[6] W. J. Kennedy, J. W. Patterson, and L. D. Fredendall, “An Overview of Recent Literature on Spare Parts Inventories,” Int. J. Production Economics, vol. 76, pp. 201–215, 2002.
[7] D. Waters, Inventory Control and Management, 2nd ed. John Wiley & Sons, 2003.
[8] R. M. Monczka, R. B. Handfield, L. C. Giunipero, and J. L. Patterson, Purchasing and Supply Chain Management, 4th ed. South-Western Cengage Learning, 2009.
[9] A. A. Ghobbar and C. H. Friend, “Evaluation of Forecasting Methods for Intermittent Parts Demand in the Field of Aviation: A Predictive Model,” Comput. Oper. Res., vol. 30, pp. 2097–2114, 2003.
[10] A. A. Syntetos and J. E. Boylan, “The Accuracy of Intermittent Demand Estimates,” Int. J. Forecast., vol. 21, no. 2, pp. 303–314, Apr. 2005.
[11] 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.
[12] S. Axsäter, Inventory Control, 3rd ed. Springer International Publishing, 2015.
[13] P. H. Zipkin, Foundations of Inventory Management. McGraw-Hill, 2000.
[14] R. J. I. Basten and G. J. van Houtum, “System-Oriented Inventory Models for Spare Parts,” Elsevier Science B.V., 2014, doi: 10.1016/j.sorms.2014.05.002.
[15] D. Simchi-Levi, P. Kaminsky, and E. Simchi-Levi, Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. McGraw-Hill Education, 2000.
[16] G. Hadley and T. M. Whitin, Analysis of Inventory Systems. Prentice-Hall, 1963.
[17] G. S. Fishman, Monte Carlo: Concepts, Algorithms, and Applications. Springer Science & Business Media, 2013.
[18] W. D. Kelton, R. P. Sadowski, and N. B. Zupick, Simulation with Arena, 6th ed. McGraw-Hill Education, 2015.
[19] A. M. Law, Simulation Modeling and Analysis. McGraw-Hill Education, 2015.
[20] R. Y. Rubinstein and D. P. Kroese, Simulation and the Monte Carlo Method. Wiley, 2017.
[21] S. Chopra and P. Meindl, Supply Chain Management: Strategy, Planning, and Operation. Pearson, 2016.
[22] S. Cavalieri, M. Garetti, M. MacChi, and R. Pinto, “A Decision-Making Framework for Managing Maintenance Spare Parts,” Production Planning and Control, vol. 19, no. 4, pp. 379–396, Jun. 2008.
[23] J. Huiskonen, “Maintenance Spare Parts Logistics: Special Characteristics and Strategic Choices,” Int. J. Production Economics, vol. 71, pp. 125–133, 2001.
[24] F. R. Jacobs and R. B. Chase, Operations and Supply Chain Management, 15th ed. McGraw-Hill Education, 2018.
[25] S. Nahmias and T. L. Olsen, Production and Operations Analysis, 7th ed. Waveland Press, 2022.
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