Transformation of light industry production in connection with the transition to the digital economy

The article discusses modern ways of using AI technologies, digitalization, and automation in light industry, thanks to which it is possible to significantly increase economic indicators, reduce production costs and reduce the required amount of human labor. The problems of light industry production before the advent of solutions using automation technologies and digitalization of data arrays are analyzed. It explains the need for an integrated approach to the implementation of improvements and the introduction of new technologies, taking into account competition in the global market. The technological process of sewing production, as the main component of production, is divided into stages. For the modernization of each of which its own artificial intelligence technologies are proposed. It is revealed that the main part of the stages is carried out according to technological schemes that are compiled by technologists in production. This process is time-consuming and takes a lot of time. To solve this problem, the scheme of the program and the content of its modules are proposed as part of the creation of an expert recommendation system for the compilation of technological sequences by technologists in the garment industry. Such a system can offer technological operations based on previous experience (knowledge base), in response to the entered keywords, giving out the most relevant, in its opinion, results (logical conclusion). The tasks of the technologist in the case of using such a system will be reduced to the introduction of keywords, checking the results and, if necessary, correcting them. The author consistently describes the stages of creating such a system. The scheme and modules of the expert recommendation system proposed in the article for the work of technologists at the enterprise, if implemented as a model using artificial intelligence, can significantly reduce the time spent on drawing up technological maps for sewing new clothing models at a light industry enterprise. In the context of sewing production, this will increase productivity and free up resources for such work that artificial intelligence cannot yet take over.