Innovation management companies in the fuzzy set theory

He problem of management of uncertainties in making business decisions is a relatively new science and practice. However, the question of development strategy innovation for companies in a non-obvious consequences of every deci-sion is important. It is known that there are uncertainties in the outer, inner and an adjacent environment, furthermore, installed and timing of their occurrence, and is defined as a regional factor. The question remains open on the man-agement of innovation processes in the company, taking into account the impact of each specific type of uncertainty.To solve the identified problem has been specifically developed an approach based on the theory of fuzzy sets. Thus was created the algorithm processing expert survey of entrepreneurs (more than 6700 units), which are in its pro-duction portfolio of innovative projects. In the framework of each of uncertainty determined by the following pa-rameters: at what time each uncertainty manifest itself; which will serve as a condition of occurrence of uncertainty (internal, external, or conjugate organizational environment); is it possible to predict in advance whether or not this uncertainty; that it will stimulate the appearance of one or another of uncertainty (the nature of occurrence).For example, to predict the emergence of a situation of uncertainty can only be in the following generating their nature: the market, the system of relations, technical and technological, legal, financial and timely. In the case of organizational, informational, labor uncertainty is impossible to predict the nature.The article also presents the ratio of occurrence of designated parameters uncertainties in the implementation of innovations in time, the environment, and the possibility of predicting the nature of existence itself uncertainties.Research study financed from Russian Foundation for Basic Research in the year 2014-2015 as research project No. 14-06-00051 «The implementation of innovative projects and processes under conditions of uncertainty