Development of a Predictive System Model Using Big Data in the Transport Sector

Abstract

This article discusses the possibility of creating a predictive system model using smart innovative technologies to generate a request for spare parts for the automotive industry using big data analysis, which is quite relevant for many transport, logistics and repair companies. The authors seek to generalize and systematize research in this area, with the possibility of integrating practical knowledge and big data for the implementation of urgent management and logistics tasks that will allow economic growth. In addition, the relevance of timely diagnostics and rapid leveling of vehicle defects is explored in light of current market conditions. Using data from the domestic market, a hypothesis has been proposed for specific parts and assemblies that may fail for a given car model. The top-selling models on the Russian second-hand market, consisting of 9 brands and 27 models from foreign manufacturers, were selected. An algorithm for analyzing the selected data set was developed, implemented using the Python programming language in Jupiter Notebook. Following a comprehensive investigation of the car's flaws, stacked diagrams were created to show the flaws, and the relevance of the 27 most popular model's flaws and their structural makeup were determined according to the country of origin. A model for predicting used car spare parts based on the results of the analysis of the structure of defects has been developed. The model combines data from revocable companies, feedback from car owners, maintenance services and component suppliers. Also, this article establishes the direction for future research in this area.