Article

Received 11.01.2026

Revised 19.05.2026

Accepted 25.06.2026

Published 04.07.2026

Retrieved from Vol. 29, No. 1, 2026

Pages 39 -49

Kulbovskyi, I., & Staruk, O. (2026). Systemic assessment of the technical condition of vehicles as a tool for optimising production processes of motor transport enterprises. Transport Systems and Technologies, 29(1), 39-49. https://doi.org/10.32703/2617-9040-2026-47-3

Systemic assessment of the technical condition of vehicles as a tool for optimising production processes of motor transport enterprises

Ivan Kulbovskyi*, Oleksandr Staruk

i.kulbovskyi@ntu.edu.ua

This study aimed to develop a methodological framework for the systematic assessment of vehicle technical condition and determine its role in optimising production processes at motor transport enterprises. The research methodology was based on a quantitative design that combined system analysis, simulation modelling, comparative analysis, multi-criteria assessment, and binary logistic regression to evaluate the relationship between vehicle condition and failure probability. A simulated dataset representing the operation of 50 vehicles over a 12-month period was generated using Monte Carlo simulation to reproduce stochastic operational variability typical for medium-sized motor transport enterprises. The dataset included technical, operational, reliability, and economic parameters. To ensure comparability of heterogeneous variables, min-max normalisation was applied, followed by the development of an integrated technical condition indicator using dynamic weighting coefficients based on expert evaluation and correlation analysis. Model validation was conducted using Nagelkerke R², Mean Absolute Error, Root Mean Square Error and Wald statistical tests to determine the significance of regression coefficients. The results demonstrated that the proposed model enables comprehensive classification of vehicles according to technical readiness and significantly improves maintenance planning efficiency compared with traditional scheduled maintenance approaches. The analysis showed that technical deterioration combined with high operational load substantially increases failure probability. The proposed framework reduced unplanned vehicle downtime by 42%, decreased maintenance costs by 20%, and improved prediction accuracy from 62% to 85%. The regression model confirmed a strong inverse relationship between the integrated technical condition indicator and vehicle failure probability. The practical significance of the study lies in providing motor transport enterprises with a data-driven tool for predictive maintenance planning, resource allocation, fleet modernisation decisions, and improving operational reliability under dynamic transport conditions

predictive maintenance; fleet management; production processes; intelligent diagnostics; model; efficiency
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https://doi.org/10.32703/2617-9040-2026-47-3