IMPROVING APPROACHES TO IDENTIFYING PROBLEM BUSINESS PROCESSES BASED ON THE BAYESIAN METHOD

Abstract

In modern organizations, the complication of business processes and an increase in the amount of data make it difficult to make managerial decisions. Especially timely and accurate identification of problematic business processes is an important condition for improving the efficiency of the organization. Traditional deterministic and heuristic approaches do not show sufficient effectiveness in the face of uncertainty. In this article, the possibility of using the Bayesian probability method to improve approaches to identifying problem business processes is studied. The Bayesian method allows you to assess the problem probability of processes by combining a priori knowledge and observation data. The results of the study indicate a high level of flexibility and interpretation of the proposed approach. The proposed approach was tested on the basis of synthetic data and the ability to distinguish between problematic and normal business processes was evaluated. In the course of the study, a quantitative ranking of the risk level of processes was carried out by calculating aposteriory probabilities. The results obtained showed that the Bayesian model works stably in conditions of uncertainty compared to traditional methods. In addition, the interpretation of the results of the model allows us to support the management decision-making process. The presented method is suitable for practical application in systems for monitoring business processes and optimizing them.