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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.
The purpose of this paper is to test the applicability of artificial neural networks for solving economic problems in sourcing, in particular, for solving the “make or buy” problem. “Make or Buy” Problem The task “make or buy” is central to the assessment of the feasibility of using sourcing models and, despite the fact that it characterizes the polarity in the decision-making process, in particular, the company’s management weighs the possibilities of developing its own production with the possibilities of using third-party suppliers based on solving this problem, new methodological approaches arise, allowing to take into account other sourcing models, for example, such as varieties of sourcing models, hybrid sourcing models and prob. The authors in previously published papers supplemented the “make or buy” task with a hybrid model of cosourcing, and thereby defined a new task – “make and / or buy” [1, 2]; however, in this paper, as a platform for testing the applicability of artificial neural networks for solving problems within the framework of sourcing economics, we use the classic “make or buy” problem and the most common method of solving it – the outsourcing matrix. To date, in domestic and foreign scientific and practical literature presents a large number of different outsourcing matrices, briefly consider some of them.
The results of this work demonstrate the possibility of using artificial neural networks to solve economic problems, in particular, the problem of “make or buy”, and building tools to assess the feasibility of using sourcing models based on intelligent mathematical methods is a promising direction in the development of theory of modeling the use of resources.
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