University Links: Home Page | Site Map
Covenant University Repository


Adekitan, Aderibigbe I. (2018) DATA BASED ANALYTICAL IDENTIFICATION OF VEHICLE MAINTENANCE COST COMPONENTS AND USAGE DATA TRENDS. International Journal of Mechanical Engineering and Technology (IJMET), 9 (8). pp. 691-701. ISSN 0976-6359

[img] PDF
Download (1042Kb)


Learning from past trends is vital for efficient planning and future decisions. Maintenance expenditure is a significant portion of the total operational cost of an organization. In corporate organizations, selected vehicles are dedicated as pool cars for conveying employees as they pursue their business objectives. For optimal performance of pool cars, adequate fund must be available for maintenance purposes and the usage and maintenance profile of each car should be adequately tracked by the maintenance team. The data, statistically analysed in this article contains the maintenance cost, fuel volume and cost, and mileage coverage data for 25 pool cars of maintenance cost showing the major faults, the frequency of occurrence and the associated maintenance and repair costs. Monthly trends were developed and maintenance cost predictive analysis using the fuel expenses, fuel volume and mileage targets and predictors using Classification and Regression (C&R) Tree node analysis. The result shows that a significant correlation exists between the mileage covered by the vehicles and the maintenance cost, this therefore emphasizes the need for mileage based periodic maintenance of vehicles. This study serves as a basis and motivation for corporate organizations to collect relevant vehicle usage and maintenance data that can reveal vital information and trends about their vehicle maintenance culture and management.

Item Type: Article
Uncontrolled Keywords: Classification and Regression (C&R) Tree, Data pattern recognition, fault frequency tracking, predictive cost analysis, road transportation, vehicle maintenance
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 11 Sep 2018 09:50
Last Modified: 11 Sep 2018 09:50

Actions (login required)

View Item View Item