Economic-Mathematical Model and Mathematical Methods for Substantiating the Choice of the Company Innovation Strategy

Objectives: The study considers mathematical methods applied to strategic management. The objective of this study is to develop economic-mathematical model to justify the innovation strategy selected by a company. Methods: Based on the suggested economic-mathematical model, the study develops a decision-making methodology for selecting the innovation strategy of a company. In the course of modeling, an innovation strategy is presented as an aggregate that consists of two-element formations that include the name of the strategy and the certainty factors that determine the degree of reliability of the selected strategy. Findings: Depending on the type of the company (production focused or scientific and innovative type), innovation strategies can be split into two sub-aggregates. In each of those sub-sets, innovation strategies are ranked based on their degree of aggressiveness. The choice of the innovation strategy is founded on the production rules of fuzzy logic, where the parameters are represented by the type of the organization and by the primary metrics of the innovation strategy components. Innovation strategy is decomposed into elementary indicators in such a way that the components of the company’s innovative potential are represented by the investment, scientific and resource potentials. In turn, such categories as administrative, financial, company geopolitical and foreign economic risks are regarded as probable strategic risks of the innovative development. Economic conditions of the company are predetermined by the industry sector development indicators and by the investment attractiveness of the host region. It has been proved that the informed decisions on innovation strategy can be made relying on formalized expertise and on precision of fuzzy algorithms. Applications/Improvements: Suggested methods could be implemented employing the relevant software. The study aims to help businesses operate under the conditions of high risks and uncertainties related to the available information.