Application of discriminant analysis for avoiding the risk of quarry operation failure

• Published in: Journal of risk and financial management Vol. 13; no. 10; pp. 1 - 14
• Main Authors: Csikosova, Adriana, Janoskova, Maria, Culkova, Katarina
• Format: Journal Article
• Language: English
• Published: Basel MDPI 01.10.2020; MDPI AG
• Subjects: acquisition; Artificial intelligence; Bankruptcy; classification of localities; Construction; Discriminant analysis; economic results; Mathematical programming; Mining industry; multidimensional normality; prediction of bankruptcy; Raw materials; Regression analysis; Roads & highways; Eastern Europe
• ISSN: 1911-8074; 1911-8066; 1911-8074
• DOI: 10.3390/jrfm13100231

Activity in the mining industry is based on the profitability principle similar to other business sectors. In the case of stone pits, gravel and sand quarries, it presents a very complex task, mainly due to the fact that the economy of localities is influenced greatly by natural conditions, which cannot be changed. The presented contribution deals with the problem of how mining companies, realizing the surface extraction of construction materials, could be profitable in the future. The main research method of this contribution presents regression and correlation analyses with the goal of determining parameters with a decisive influence on the future economic development of the locality. A complex system of stone pit, gravel and sand quarries demanded discriminant analysis to evaluate individual localities with the goal of dividing them into profitable and not profitable localities. The results of the contribution divide localities of quarry mining among profitable or not profitable, serving for predicting the future development of the company, based on discriminant analysis. The results of maximally possible measures respect assumptions, enabling the correct application of such multivariate statistical methods. A further orientation of the research in an area of model creation for predicting the future development of the company is possible in the application of logistic regression and neuron nets.