Dutch Journal of Finance and Management

Dutch Journal of Finance and Management, 2025 - Volume 8 Issue 2, Article No: 39599
https://doi.org/10.55267/djfm/17901

Published Online: 13 Feb 2026

Views: 10 | Downloads: 3

Even if measuring the ROI (Returns-on-Investment) of predictive equipment maintenance is essential for discerning whether the manufacturing entity is overspending or underspending on equipment maintenance, most manufacturing executives often do not bother to measure the ROI of their equipment maintenance. This affects decisions on the improvement initiatives that can be adopted. To address such a problem, this study used integrative review to evaluate insights from the existing studies about the techniques, values, and limitations of measuring the ROI of predictive manufacturing equipment’s maintenance. The research was a qualitative study based on content analysis of articles retrieved primarily through Google searches as the major search engine. After predictive maintenance, findings from the analysis indicated the financial metrics to measure the financial gains obtained since the introduction of predictive machine maintenance. It evaluates the benefits and advantages so far attained as compared to the costs incurred in the application of predictive maintenance. Apart from ROI, some of the commonly used financial metrics were found to encompass cost-benefit analysis and net present value (NPV). ROI analysis seeks to evaluate the benefits gained against the costs incurred in the use of predictive machine maintenance. However, findings indicated the major inhibitors of measuring the ROI of predictive machine maintenance to often arise from cost, poor data utilization culture, and ignoring predictive maintenance. Unless management is able to deal with such challenges, they may never get to understand the returns on investment generated from the expenditure on predictive equipment maintenance. From these findings, this study has contributed to changing the general perception of predictive maintenance as an expenditure rather than an investment.

Predictive Maintenance Equipment Maintenance Preventive Maintenance Reactive Maintenance

• Aivaliotis, P., Xanthakis, E., & Sardelis, A. (2020). Machines' behaviour prediction tool (BPT) for maintenance applications. IFAC-PapersOnLine, 53(3), 325–329. https://doi.org/10.1016/j.ifacol.2020.11.085
• Alfionita, S., & Ikhwanul Alifin, F. (2023). Preventive maintenance analysis based on mean time between failure (MTBF) and mean time to repair (MTTR). Angkasa: Jurnal Ilmiah Bidang Teknologi. Published November 6, 2023. ejournals.itda.ac.id
• Ameta. (2024). Boosting ROI with predictive maintenance cost savings. New Delhi: Ameta. https://www.ameta.no/post/boosting-roi-with-predictive-maintenance-cost-savings
• Baur, M., Albertelli, P., & Monno, M. (2020). A review of prognostics and health management of machine tools. International Journal of Advanced Manufacturing Technology, 107, 2843–2863. https://doi.org/10.1007/s00170-020-05203-0
• Berrabah, F. Z., Belkacemi, C., & Zemmouchi-Ghomari, L. (2022). Essential and new maintenance KPIs explained. International Journal of Education and Management Engineering, 12(6), 11–20. https://doi.org/10.5815/ijeme.2022.06.02
• Booyse, W., Wilke, D. N., & Heyns, S. (2020). Deep digital twins for detection, diagnostics and prognostics. Mechanical Systems and Signal Processing, 140, 106612. https://doi.org/10.1016/j.ymssp.2020.106612
• Duarte, A. L. C. M., & Santiago Scarpin, M. R. S. (2023). Maintenance practices and overall equipment effectiveness: Testing the moderating effect of training. Journal of Quality in Maintenance Engineering, 29(2), 442–459. https://doi.org/10.1108/JQME-04-2021-0033
• En-Nhaili, L., Muchiri, P., & Pintelon, L. (2021). Resource overall equipment cost loss indicator to assess equipment performance and product cost. International Journal of Productivity and Performance Management. Emerald.
• Gates, E. F., Vidueira, P., Komakhidze, M., Aldrich, C., & Shim, C. (2025). A critical, integrative review on evaluating systems change and transformation, Part One: 2011–2021. Evaluation, 0(0). https://doi.org/10.1177/13563890251363232
• Heim, S., Clemens, J., Steck, J. E., Basic, C., Timmons, D., & Zwiener, K. (2020). Predictive maintenance on aircraft and applications with digital twin. 2020 IEEE International Conference on Big Data (Big Data), 4122–4127. https://doi.org/10.1109/BigData50022.2020.9378398
• HexaState. (2025). Investing in the future: How predictive maintenance can drive ROI. New York: HexaState.
• Keller, S., & Owen, A. (2025). A comprehensive cost-benefit analysis of preventive maintenance versus corrective maintenance: Assessing the financial impact and operational benefits in engineering. Berlin: IU International University of Applied Sciences.
• Khan, S., Farnsworth, M., McWilliam, R., & Erkoyuncu, J. (2020). On the requirements of digital twin-driven autonomous maintenance. Annual Reviews in Control, 50, 13–28. https://doi.org/10.1016/j.arcontrol.2020.09.003
• Knafl, K., & Whittemore, R. (2017). Top 10 tips for undertaking synthesis research. Research in Nursing & Health, 40(3), 189–193. https://doi.org/10.1002/nur.21790
• Limble. (2025). Creating a predictive maintenance program that is right for you. New York: Limble. https://limblecmms.com/learn/predictive-maintenance/start-program/
• Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020). A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robotics and Computer-Integrated Manufacturing, 65, 101974. https://doi.org/10.1016/j.rcim.2020.101974
• Moss, J. (2025). The ROI of predictive maintenance: Why it’s a game-changer. Philadelphia: The Standard. https://www.standardelectricsupply.com/contact-us
• Oermann, M. H., & Knafl, K. A. (2021). Strategies for completing a successful integrative review. Nurse Author & Editor. https://doi.org/10.1111/nae2.30
• Oxmaint. (2025). Predictive maintenance in manufacturing: ROI guide & implementation steps. California: Oxmaint.
• Rabah, S., Assila, A., Khouri, E., Maier, F., Ababsa, F., Bourny, V., Maier, P., & Merienne, F. (2021). Towards improving the future of manufacturing through digital twin and augmented reality technologies. Procedia Manufacturing, 54, 185–190. https://doi.org/10.1016/j.promfg.2021.07.030
• Rahman, F., Sugiono, S., Sonief, A. A., & Novareza, O. (2024). Optimization maintenance performance level through collaboration of overall equipment effectiveness-machine effectiveness and machine reliability. Journal of Applied Engineering Science, 20, 917–936. https://doi.org/10.5937/jaes0-35189
• Reliable Plant. (2025). How to prove and maximize ROI in predictive maintenance. https://www.reliableplant.com/Read/30367/internet-of-things
• Sankar, S. (2025). Predictive maintenance ROI for manufacturing executives. Dubai: Hakunah Matata.
• Sensemore. (2025). The key factors impacting ROI in predictive maintenance. London: Sensemore. https://sensemore.io/the-key-factors-impacting-roi-in-predictive-maintenance/?srsltid=AfmBOopfTRFFCu1lu3C1QVbOe3FbdBkq0K2JwXhDD1n0g3Lz735Gi2RP
• Siemens. (2025). Maximising your ROI with scalable, predictive maintenance: A guide on optimizing your ROI potential by scaling predictive maintenance, utilizing data effectively, and tapping into the capabilities of the Senseye Predictive Maintenance ROI Calculator. Chicago: Siemens.
• Sun, X., Bao, J., Li, J., Zhang, Y., Liu, S., & Zhou, B. (2020). A digital twin-driven approach for the assembly-commissioning of high precision products. Robotics and Computer-Integrated Manufacturing, 61, 101839. https://doi.org/10.1016/j.rcim.2019.101839
• Veerappan, G. (2025). A guide to calculating maintenance and reliability initiative ROI. New Delhi: Cryotos. https://www.cryotos.com/blog/calculating-maintenance-and-reliability-initiative-roi
• Villa, V., Naticchia, B., Bruno, G., Aliev, K., Piantanida, P., & Antonelli, D. (2021). IoT open-source architecture for the maintenance of building facilities. Applied Sciences, 11(11), 5374. https://doi.org/10.3390/app11115374
• West, J., Siddhpura, M., Evangelista, A., & Haddad, A. (2024). Improving equipment maintenance—Switching from corrective to preventative maintenance strategies. Buildings, 14(11), 3581.
• Yan, T., Lei, Y., Li, N., Si, X., Pintelon, L., & Dewil, R. (2022). Online joint replacement-order optimization driven by a nonlinear ensemble remaining useful life prediction method. Mechanical Systems and Signal Processing, 173, 109031. https://doi.org/10.1016/j.ymssp.2022.109031
• Zhong, D., Xia, Z., Zhu, Y., & Duan, J. (2023). Overview of predictive maintenance based on digital twin technology. Heliyon, 9(4), e14534. https://doi.org/10.1016/j.heliyon.2023.e14534
• Zonta, T., da Costa, C. A., Righi, R. D., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889. https://doi.org/10.1016/j.cie.2020.106889

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.