Abstract
The article looks at optimization of maintenance and repair services on the base of analysis of operational data as an implementation of real movement away from mileage/calendar maintenance towards the risk based condition based system, when servicing is prescribed not on the schedule, but on basis of actual operational behavior in actual service environment, proving, that data streams (telematic streams, OBD-II/CAN parameters, DTC codes, service history, repeat service records) build an “digital trace” of operation, which value is revealed when this trace is transformed into repetitive service operations (scheduling, further diagnosis, prevention maintenance or acceptable deferral with risk control) within a service loop (the data started to “work”). From a methodological viewpoint, the paper confirms an end-to-end proposal that mixes disparate data integration, time-series-based degradation-features engineering, anomaly detection, estimating the probability of a repair event over a certain horizon and an operations-and-economics model that determines a threshold for intervention, taking into account both downtime costs and costs of missed failure, capacity and logistics of service resources. In our opinion, it’s precisely the ‘price of error’ that gives the forecasting a practical meaning, defining when it’s useful to carry out early works and when the ‘cost-effective’ choice is to wait with an additional investigation during a short validation period or collect more features, thereby avoiding unnecessary jobs and not compromising confidence in suggestions. Scientific actuality would be the formalization of the movement from fragmented operational data toward a governed service process. Meanwhile we adopt standard vocabulary such as “symptom – diagnosis – work – result” and a feedback loop after repairing, because we need to update the system and account for changes in the data, like in the case of different software versions. Practical usefulness looks like being on determining rules on fleet segments, trigger levels, etc.
References
ADAC. (2025). ADAC Pannenstatistik 2025: Sind Elektroautos zuverlässiger? https://www.adac.de/rund-ums-fahrzeug/unfall-schaden-panne/adac-pannenstatistik
American Automobile Association. (2025). Your driving costs 2025: Fact sheet. https://newsroom.aaa.com/wp-content/uploads/2025/09/UPDATE-AAA-Fact-Sheet-Your-Driving-Cost-9.2025-1.pdf
Chen, F., Shang, D., Zhou, G., Ye, K., & Ren, F. (2024). Mileage-aware for vehicle maintenance demand prediction. Applied Sciences, 14(16), Article 7341. https://doi.org/10.3390/app14167341
Chen, F., Shang, D., Zhou, G., Ye, K., Ren, F., & Wu, G. (2025). Collaborative multiview time series modeling for vehicle maintenance demand prediction. Scientific Reports, 15(1), Article 13058. https://doi.org/10.1038/s41598-025-96720-1
Crespo del Castillo, A., & Parlikad, A. K. (2024). Evaluating investment in condition monitoring for fleet maintenance. IFAC-PapersOnLine, 58(8), 377-382. https://doi.org/10.1016/j.ifacol.2024.08.150
de Pater, I., Reijns, A., & Mitici, M. (2022). Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics. Reliability Engineering & System Safety, (221), Article 108341. https://doi.org/10.1016/j.ress.2022.108341
Driver and Vehicle Standards Agency. (2025, October 30). MOT testing data for Great Britain [Statistical data set]. https://www.gov.uk/government/statistical-data-sets/mot-testing-data-for-great-britain
Driver and Vehicle Standards Agency. (n.d.). MOT class 3 and 4 vehicles: Initial failures by defect category (data extract). https://www.gov.uk/csv-preview/68e682f5750fcf90fa6fff65/dvsa-mot-03-mot-class-3-and-4-vehicles-initial-failures-by-defect-category.csv
Errezgouny, A., Chater, Y., Barranco González, C. D., & Cherkaoui, A. (2025). An integrated deep learning approach for predictive vehicle maintenance. Decision Analytics Journal, (16), Article 100597. https://doi.org/10.1016/j.dajour.2025.100597
European Automobile Manufacturers’ Association. (2025). Economic and market report: Global and EU auto industry – Full year 2024. https://www.acea.auto/files/Economic_and_Market_Report-Full_year-2024.pdf
European Automobile Manufacturers’ Association. (2026, January). Vehicles on European roads. https://www.acea.auto/files/ACEA_Report-%E2%80%93-Vehicles_on_European_roads_2026.pdf
Eurostat. (2024). Key figures on European transport – 2024 edition. Publications Office of the European Union. https://doi.org/10.2785/9777356
Eurostat. (n.d.). Passenger cars by age (road_eqs_carage) [Data set]. https://ec.europa.eu/eurostat/databrowser/product/page/road_eqs_carage
Iqbal, M., Suhail, S., Matulevičius, R., Shah, F. A., Malik, S. U. R., & McLaughlin, K. (2025). IoV-TwinChain: Predictive maintenance of vehicles in internet of vehicles through digital twin and blockchain. Internet of Things, (30), Article 101514. https://doi.org/10.1016/j.iot.2025.101514
Michailidis, E. T., Panagiotopoulou, A., & Papadakis, A. (2025). A review of OBD-II-based machine learning applications for sustainable, efficient, secure, and safe vehicle driving. Sensors, 25(13), Article 4057. https://doi.org/10.3390/s25134057
National Highway Traffic Safety Administration. (2026, January 24). Recalls data (NHTSA campaigns) [Data set]. https://catalog.data.gov/dataset/recalls-data
Rahim, M. A., Rahman, M. M., Islam, M. S., Muzahid, A. J. M., Rahman, M. A., & Ramasamy, D. (2024). Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit. Expert Systems with Applications, (257), Article 125080. https://doi.org/10.1016/j.eswa.2024.125080
SAE International. (2021). J1979-2_202104 – E/E Diagnostic Test Modes: OBDonUDS. https://www.sae.org/standards/j1979-2_202104-e-e-diagnostic-test-modes-obdonuds
Theissler, A., Perez-Velazquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, (215), Article 107864. https://doi.org/10.1016/j.ress.2021.107864
World Bank. (n.d.). Motor vehicles (per 1,000 people) (indicator IS.VEH.NVEH.P3): Metadata and definition. World Development Indicators. https://databank.worldbank.org/metadataglossary/world-development-indicators/series/IS.VEH.NVEH.P3
Zheng, P., Zhao, W., Lv, Y., Qian, L., & Li, Y. (2023). Health status-based predictive maintenance decision-making via LSTM and Markov decision process. Mathematics, 11(1), Article 109. https://doi.org/10.3390/math11010109

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