COMPUTER SYSTEM FOR MODELING AND FORECASTING OF LOGISTICS AND MAINTENANCE OF ROLLING STOCK AT CEMENT PLANTS

Abstract

The paper considers modern approaches to the digitalization of logistics processes and maintenance systems of railway rolling stock used in cement industry enterprises. The expediency of integrating logistics models with predictive maintenance systems based on the analysis of operational data and the use of intelligent data-processing algorithms is substantiated. The proposed approach involves continuous collection and analysis of operational data from sensor systems installed on freight wagons. The application of machine learning methods for estimating the remaining useful life and failure probability enables a transition from time-based maintenance to condition-based and predictive maintenance strategies.

It is established that the use of big data analytics algorithms contributes to reducing the number of unplanned downtimes, increasing the utilization rate of rolling stock, optimizing operating costs, and reducing the life-cycle cost of logistics operations. Special attention is paid to the analysis of rolling stock maintenance strategies with a comparison of time-oriented and condition-oriented approaches; the limitations of traditional scheduled models under stochastic load conditions and degradation processes of freight wagons are demonstrated, and the advantages of using real-time data for maintenance decision-making are substantiated.

Using the proportional hazards model as an example, the possibilities of estimating the remaining useful life of critical rolling stock components and supporting decisions in reliability-centered maintenance systems are demonstrated. It is shown that the combination of technical condition forecasting with logistics modeling provides a basis for the development of intelligent computer-based systems for cement transportation management under uncertainty and variable operating parameters.

The obtained results can be applied in the design and implementation of information and analytical decision-support systems in the field of logistics and maintenance management at cement enterprises, especially under conditions of increasing economic uncertainty and higher requirements for energy efficiency and sustainable development.