The Use of Artificial Neural Networks to Solve the "Make or Buy" Problem

Abstract

Currently, the use of sourcing models is a very popular and promising tool for restructuring large industrial enterprises and optimizing companies in the world, but the experience of both domestic and foreign enterprises shows that not all sourcing has a positive effect on business performance, in particular, each model sourcing has its advantages and disadvantages, which, of course, must be considered. In this regard, in the scientific and practical literature there are a sufficient number of various tools and methodological approaches, one way or another to identify potential difficulties, however, these approaches and tools are not universal for all possible situations, especially since new (unknown) may arise in practice features of models. On the other hand, when solving economic problems, technologies come from other disciplines that can significantly expand the capabilities of existing management tools, such technologies include artificial neural networks. In this regard, it seems appropriate to study aimed at checking the applicability of artificial neural networks for solving certain problems within the framework of sourcing economics.

Outsourcing matrix of Isavnin A.G., Farkhoutdinov I.I. and standard model of artificial neuron were applied. The applicability of artificial neural networks to solve economic problems in the framework of sourcing is proved. The results of this work can serve as the basis for the formation of tools to assess the feasibility of using sourcing models through the construction of artificial neural networks.