Models for Business Process Management under Uncertainty

Abstract

The modern business environment is rapidly evolving in the context of digital transformation, accompanied by uncertainty, dynamic changes, and competitive pressure. Traditional analytical methods often fail to address the challenges associated with managing large volumes of unstructured data. Consequently, the implementation of innovative methods based on probabilistic modeling and artificial intelligence becomes increasingly relevant, allowing business decision-making to be not only fast and flexible but also reliable and transparent.

In this context, Bayesian models gain particular importance, as their essence is based on managing uncertainty, updating knowledge, and adapting forecasts by incorporating new data. Bayesian theory enables the determination of the probability of events by synthesizing existing knowledge with new information, creating an effective framework for optimizing business processes.

Accordingly, the integrated use of Bayesian methods and machine learning algorithms plays a significant role. Such a synthesis enhances forecasting accuracy, reduces information gaps, and ensures multifactorial data analysis. In particular, models based on Bayesian networks are successfully used in analyzing customer behavior and forecasting their demand.