Analyzing the critical success factors of the new production plans of automobile manufacturers and predicting their realization by combining the methods of factor analysis and adaptive neural-fuzzy system.
Subject Areas : Management
ali bardideh
1
(Islamic Azad University, Tabriz)
naser feghhi farahmand
2
(tabriz azad university)
mojtaba ramazani
3
(Assistant Professor, Department of Management, Bonab Branch, Islamic Azad University, Bonab, Iran)
Yaguoob Alavi Matin
4
(Department of Management, Tabriz Branch, Islamic Azad University. Tabriz, Iran)
Keywords: factor analysis, success prediction indicators, adaptive neuro-fuzzy system, new investment plans in automobile manufacturing,
Abstract :
The purpose of the research was to analyze the critical success factors of the new production plans of car manufacturers and to predict their realization by combining factor analysis methods and adaptive neural-fuzzy inference system. . The statistical population of the research was in two parts. In the first part, which is dedicated to identifying the critical factors of success, including 80 experts and experts with a sample of 66 people and the statistical population related to the use of multilayer neural networks, including 250 successful and unsuccessful investment projects (all projects implemented since 1390) has been in the automotive industry. Based on the results obtained, six main factors with 40 related indicators predicting the success of new investment projects in the automotive industry were identified and after describing the variables and testing the normality, using the confirmatory factor analysis of the variables, all the factors were from the factor analysis. had a suitable confirmation, then using linear regression and analysis of variance test, the effect of each factor on the success of new investment projects in the automobile industry was investigated, and the results of this test showed the confirmation of the effect of each factor, and in the following, the results show It was that the designed adaptive neural-fuzzy system model had the power to predict the success of new investment plans with an error of less than 5%, which shows the high predictive power of the model.
