Abstract
One of the main objectives of strategic management is the development and selection of strategies to achieve the desired results. The main goal of this paper is the analysis of the main domains or areas of machine learning application to support the process of strategic planning and decision making. The scientific methodology of the research studies is methods and procedures of modeling and intelligent analysis. This is theoretical and empirical paper in equal measure. This paper deals with the issues of machine learning implementation and how intellectual models and systems can be used to support the process of strategic planning. At the preprocessing stage on the basis of a modeled base of examples of strategy options, the use of clustering methods for forming groups of similar parameters that influence the choice of strategies and groups of similar enterprise objects, each of which has a certain type of strategy, is demonstrated. On the next step the selection of ranked characteristics that affect the choice of strategy is made. At the stage of solving the problem of choosing strategies, module Classifier Learner from MatLab 2018b is used.
Highlights
This paper represents an extended and largely revised report of the authors [1]
The methods used in artificial intelligence (AI) for data handling are included in machine learning, which is a subset of AI
Even huge data sets can be tackled: training may be slow in these cases, but once this is done, new data can be mapped to the Self-Organising Map (SOM) very quickly
Summary
This paper represents an extended and largely revised report of the authors [1]. For any company, the strategy selection is an important task, which determines further activities. One of the essential decision-making tools in various areas of activity is machine learning, which is becoming an increasingly popular concept in the modern world since its most. Research has shown that companies perform better when they apply data-driven decision-making. This creates an incentive to introduce intelligent, data-based decision models, which are comprehensive and support the interactive evaluation of decision options necessary for the business environment [4]. Data pre-processing using cluster analysis is briefly described, the method of dimensionality reduction is specified, tools used in the implementation of company development strategies are analysed, assessments for the selection of the company management method using the Classifier Learner module are given. The final part of the article contains the discussion of the results obtained, and the directions of further research
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