Modelling the Level of the Enterprise’ Resource Security Using Artificial Neural Networks

Abstract

Significant attention is paid to increasing the efficiency of using resources by business entities due to the growing dependence between economic growth and the number of consumed resources, problems with access to various types of resources on the market, as well as their exhaustion in the face of growing needs. At the same time, various digitization tools are widely used to solve these problems. This paper considers artificial neural networks as a tool for modelling and forecasting the level of resource security in the economic activity of an enterprise, which is divided into separate functional blocks (production, personnel, finance). To this end, a multi-layer perceptron model (MLP) was used by constructing and training a network on several possible architectures in order to select the one with the highest classification quality. In the process of training, testing and verification of MLP networks, 32 indicators were used as input data, characterizing the state and efficiency of using various types of enterprise resources, for 85 enterprises over the five years of their operation. The initial data were the values of the safety zone, which were set separately for each indicator, subsystem and enterprise using economic-mathematical modelling on the basis of determining the acceptable limits of indicator fluctuations. As a result, four MLP networks were selected (one network for each of the three functional subsystems, as well as one for the enterprise as a whole), which were characterized by the highest value of quality at each stage of calculations (training, testing, verification). The performed calculations proved that artificial neural networks can be a useful and convenient tool for determining the security level of an enterprise in various directions of its economic activity (types of consumed or involved resources), and therefore can be more widely used by business entities to increase the validity of management decisions.

Keywords

• artificial neural networks
• enterprise resource security
• modelling
• multilayer perceptron
• prediction