HIERARCHIC CLUSTERING OF PRODUCTION FACILITIES

Background. The proposed research is devoted to the urgent problem of improving the sustainability of businesses using resources of competitive products effectively. Characteristic feature of the modern market economy is acceleration of the integration process. Economic theory and practice offer a large number of options and forms of integration of business, including cluster association. Review of scientific sources on selected subject shows that there is not a complete study of the processes of construction clustering based on structural and logical sequence. Most of the existing works focus around narrowed consideration of a cluster and do not notice the existence of a number of other components and constituents. However, the scientific definition of the above neglect is undoubtedly important, and forms the basis of objective research. Results. The central aspect of analysis carried out in this work is the mechanism of uniting companies into clusters based on functions of intercluster distance and density of enterprise location. Investigation of the use of cluster shapes to improve the competitiveness of its members through cooperation in cluster research, innovation, education and government support, is the formation and functioning of clusters aimed at accelerating dynamic and effective socio-economic development of all enterprises in the region, which provides a higher standard of living in this region. The paper noted that most of the elements, indicators and resources used in general are determined by the efficiency of functioning of enterprises in the cluster. Complementing the work is the detailed etymological analysis of cluster shape, which allows participants to retain legal independence and compete against each other like associations. Each participant considers cluster membership therein as security for their own competitive advantage, as constantly evolving it provides a guaranteed support, information about the market and receives additional sources of profitable trades coordination instead, creating a positive synergistic effect. Conclusions. The study, according to the author, can become part of a more complete understanding of the cluster. Identifying clustering quality through clustering distance allows you to select the optimal clustering. The use of this feature eliminates the need to enter as an input parameter of threshold similarity which is usually appointed by an expert.