Purpose of the article:To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings: Artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction. Conclusions: It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise. Conclusions resulting from the paper are beneficial for further research.

The purpose of this article is to verify the possibility of using artificial neural networks (ANN) in business management processes, primarily in the area of supply chain management. The author has designed several neural network models featuring different architectures to optimize the level of the company's inventory. The results of the research show that ANN can be used for managing a company's order cycle and lead to reduced levels of goods purchased and storage costs. Optimal neural networks show suitable results for subsequent prediction of the amount of items to be ordered and for achieving reduced inventory purchase and keeping costs down.

The purpose of this paper is to present an inventory balance model including an order-up-to IntroductionEvery organization, whether profitable or non-profitable one, manage certain assets. Managing and exploiting these assets for corporate purposes is an inseparable part of managerial work at all levels of management. The most significant component of current assets is inventory, which is typical for its perishability.The purpose of this paper is to introduce a model of inventory balance equation extended by an order up to replenishment policy with partial backlogging, described by means of an ordinary differential equation with delayed argument in a situation when goods are not replenished constantly, but only at pre-specified times.The economic theory that forms the essential basis for the model is explained in the introductory section and serves as a basis for the designing of the model. Methods of analysis, synthesis and differential calculus are also used. The equation of the model is then solved using the modern theory of so-called functional differential equations, a highly special part of which is the theory of linear differential equations with delayed arguments.The scientific aim is a solvability verification of such a problem using the theory of functional differential equations. The solution is demonstrated on a specific example in the application part. Computer simulations help to present the behaviour of the model in different situations. The graphical presentation was created in Maple system.Our findings are that due to the exact expression of the model and the availability of suitable software, it is possible to assess the effect of any changes in parameters.