摘要:The relevance of using the provisions of the fuzzy logic theory in assessing the readiness of the
enterprise to change is substantiated. This made it possible to objectively assess the current state of
the enterprise, using the nonlinear principles of forming conclusions, to simulate the experts' reflections
on the level of enterprise readiness to change. It is proposed to use the McKinsey 7S model
in the process of structuring the enterprise's internal environment. On the basis of this model, a
fuzzy-multiplier model for evaluating the enterprise's readiness for changes, is constructed, which
is presented in the form of a hierarchical relationship between the input variables, groups of input
variables; integral characteristics of the elements of the enterprise that characterize its activities, in
particular: strategy, structure, systems, style/culture, staff, skills, common values and output variable
and they characterize the integral indicators of enterprise readiness for changes. Questionnaires
are developed and a survey of gas transportation company staff is conducted on readiness for
changes. The result of the expert opinions elaboration is received on the basis of the following
methods application; namely statistical processing of expert opinions; the method of pairwise comparisons
and the method of fuzzy clusterization. The functions of all parameters membership of the
constructed system are obtained. On the basis of the averaged membership functions of input and
output parameter terms, the correspondence between membership functions and control rules according
to Zadeh is created and the structure of the Mamdani type in the MATLAB system is synthesized.
As a result, the assessment of the gas transportation enterprises readiness level to change
is obtained. It is substantiated that the obtained results are considered as the basis for further effective
decision making in order to ensure the development in the conditions of the instability of the
functioning environment.
关键词:Changes;
Readiness for change;
Fuzzy logic;
Model McKinsey 7S;
Enterprise