The term „stochastic process model“ is primarily found in the fields of Big Data and Smart Data, Artificial Intelligence, and Industry and Industry 4.0. A stochastic process model is a method used to describe processes where randomness and uncertainty play a role. This means that not everything runs according to plan or is predictable; rather, the development always has a certain “random factor”.
In practice, stochastic process models are used, for example, to predict machine failures in a factory or to analyse how a customer navigates through an online shop. The model helps to find patterns and calculate probabilities – even if individual steps are uncertain or unclear.
A simple example: A manufacturing robot can function or fail on any given day. With a stochastic process model, it's possible to calculate the probability of the robot failing within a week or how often it should be maintained. In this way, stochastic process models help companies make better decisions, assess risks, and optimise processes – even with incomplete information.
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