Abstract: Neural networks have recently been gaining popularity in the business practice. Research has even confirmed their better performance over traditional methods. This paper gives an overview of one of the types of neural networks, generalized regression neural networks. These are then used to establish a plan for the future sales of a company. However, generalized regression neural networks also have their drawbacks. They are oversized and have a long computation time. Despite these disadvantages the article searches for, based on data from the profit and loss accounts of the food company Friall, s.r.o. from the years 1995-2015, the dependence of revenues on production factors. 1000 random neural structures are generated, from which the 5 most appropriate are preserved using the method of least squares. Additionally, a sensitivity analysis is conducted to determine how the individual production factors affect the firm’s ability to generate revenue. The proposed neural network is potentially applicable in practice when compiling the financial plan of a company derived from the amount of sales.
Authors: Jan Mareček
Keywords: Generalized Regression Neural Networks, enterprise’s sales, production factors, neural structure