Models of Team Role Composition for Completing an IT Company on a Fuzzy Set Platform

At present, in the global economic crisis and the COVID-19 pandemic, the development and operation of distance learning and student education are more relevant than ever. The COVID-19 crisis, in addition to the world pandemic, has dealt a devastating blow to the economy of the state and organisations. Insufficient level of research support, imperfection of organisational structure and organisation of management of scientific and technical activities in IT projects necessitate revision and improvement of the existing management system of such projects. The purpose of the study is to develop comprehensive models to increase the objectivity of the role assessment of the applicants' competences during the staffing of specialists for the IT company in conditions of uncertainty on the platform of fuzzy sets. The following theoretical methods of scientific cognition were used in the study: method of synthesis and analysis of information, statistical method, method of maximisation and method of Max-disjunction. The study considers the developed model for assessing the applicants' competences in the staffing of the role of specialists of the IT company using the platform of fuzzy sets. The study analyses the current problems of recruitment, human or labour resources in corporations whose activities are related to the development of software in various fields. The approximate composition of roles in modern IT companies with the recommended socionic types of information metabolism for each specific executor of the IT project is given. The incentive mechanism in the context of specific constraints enables the improvement of the system of rewarding IT staff and makes it possible to differentiate such staff using project management tools. The authors of the study have developed a fuzzy model for assessing the competences of a candidate for any position in an IT corporation. The study provides linguistic variables, the basis of the rules of fuzzy products, and the conclusion of the dependences of the output variable on the input data of the developed fuzzy model.