Formation and selection methodology of digital transformations programs for an industrial enterprise using machine learning algorithms

Abstract

Abstract The article developed a Methodology for the formation and selection of digital transformation programs of an industrial enterprise using machine learning algorithms. The method allows creating a set of valid alternative options for digital transformation programs for the production activity of an industrial enterprise by a set of workarounds for a multilayer graph model. The choice of the optimal program is carried out by assessing the weights of the workarounds of the graph by a set of Key Perfomance Inticators (KPIs). The method is based on the presentation of the structure of an industrial enterprise by weighted oriented graphs which determine the structure of a neural network, and its subsequent training on arrays of source information. Each hidden layer of the graph model determines any component of the production structure of the enterprise, and the neurons in them are the components of this structure. As a basic part of the digital transformation is determined a automated workstation and a cyber-physical system. The PyTorch library for the Python 3.8 programming language is used as the main tool for implementing the method.