Assessing the quality level of technological processes at car service enterprises

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.200332

Keywords:

auto-service enterprise, quality level, morphological analysis, linear multiple regression, fuzzy logical derivation

Abstract

This paper addresses the problem of the level of maintenance and repair of motor vehicles depending on the parameters that take into consideration the internal state of a car service enterprise and the external factors that characterize the environment of its functioning, as well as the cars serviced by the enterprise. The course of the study involved a morphological analysis of the auto service system, based on which the functional elements of the system were determined, as well as the essential morphological attributes of these elements and the variants for their implementation. In order to identify the degree of influence exerted by these morphological attributes on the quality of technological processes performance, a survey of the typical Ukrainian car service enterprises has been carried out and a mathematical model of the system has been built in the form of a linear multiple regression equation. The preliminary verification of the input parameters of the system model for multicollinearity based on the Farrar-Glober algorithm has made it possible to separate the independent ones among them and to reduce the complexity of further calculations. The regression equation coefficients characterize the degree of importance of considering the appropriate parameters when designing an automated quality management system. To improve the adequacy of the model and to reduce the complexity of the simulation process, the source data array has been divided into the training and control samples using an algorithm based on computing the values for the sampling variance. In order to obtain the most adequate model, the nonlinear models of the examined system of the Mamdani and Sugeno type have been constructed. To this end, the MATLAB software suite was employed with its Fuzzy Logic Toolbox. The input and output parameters' membership functions were set in a trapezoidal form. The nonlinear models have been implemented for various defuzzification methods of the output parameter. The smallest root mean square error of the resultant characteristic was obtained when implementing the Sugeno-type model; it was 1.07 %. This indicates the expediency of integrating the specified model into a quality management system in order to determine the optimal operating modes. The study results could be applied to assess the quality of the services rendered by car service systems at the micro- and macro levels

Author Biographies

Liudmyla Tarandushka, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

PhD, Associate Professor, Head of Department

Department of Vehicles and Technology of Their Operation

Vasil Mateichyk, National Transport University М. Omelianovycha-Pavlenka str.,1, Kyiv, Ukraine, 01010

Doctor of Technical Sciences, Professor

Department of Ecology and Safety of Vital Functions

Nataliia Kostian, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

PhD

Department of Vehicles and Technology of Their Operation

Ivan Tarandushka, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

Assistant

Department of Vehicles and Technology of Their Operation

Maksym Rud, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

PhD, Associate Professor

Department of Vehicles and Technology of Their Operation

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Published

2020-04-30

How to Cite

Tarandushka, L., Mateichyk, V., Kostian, N., Tarandushka, I., & Rud, M. (2020). Assessing the quality level of technological processes at car service enterprises. Eastern-European Journal of Enterprise Technologies, 2(3 (104), 58–75. https://doi.org/10.15587/1729-4061.2020.200332

Issue

Vol. 2 No. 3 (104) (2020): Control processes

Section

Control processes

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Copyright (c) 2020 Liudmyla Tarandushka, Vasil Mateichyk, Nataliia Kostian, Ivan Tarandushka, Maksym Rud

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