Structural Model of the Decision Support System for Managing the Supply of Material Resources of a Machine-Building Enterprise

In the context of digital transformation, the problem of supply management remains one of the urgent problems of any machine-building enterprise, requiring both theoretical and practical solutions. The potential of artificial intelligence to improve the efficiency of supply management in companies is obvious, but the level of implementation of solutions based on it remains low today. Enterprises are not ready to invest and allocate significant resources to implement elements of artificial intelligence in their activities, which is facilitated by the lack of transparency regarding the requirements for the implementation of artificial intelligence in the supply system of enterprises and experience with such technologies. The article proposes a structural model of a decision support system for supply management of machine-building enterprises, which assumes the possibility of making decisions automatically. Thanks to the use of artificial intelligence elements, the speed of the system’s response to changes in the initial data, both in the external and internal environment of the enterprise, will be significantly increased. This model has a number of advantages, the most significant of which are the integrated management of the supply, taking into account the features of its two constituent blocks: procurement management and supplier management, improving the quality of decisions made in the supply management due to the use of accumulated experience in the system, which will reduce the time and material costs of enterprises, as well as transaction costs arising when solving the problem of finding a supplier and supplies, negotiating, and concluding contracts with suppliers. The system will also allow solving new, complex problems in the field of management of the supply, using or adapting existing solutions in the precedent base of the decision support system, and explaining the decisions obtained. The proposed intelligent system makes decisions automatically, freeing up managers’ time to make strategic decisions in supply in other areas of the enterprise. The results presented in the article can be useful to researchers and managers in the field of supply activities, which can be used for more effective enterprise management in the process of digital transformation.