The term "stochastic process model" is particularly at home in the fields of big data and smart data, artificial intelligence as well as Industry and Factory 4.0. A stochastic process model is a method used to describe processes in which chance and uncertainty play a role. This means that not everything can be planned or predicted, but development always has a certain "chance factor".
In practice, stochastic process models are used, for example, to predict machine failures in a factory or to analyse how a customer moves 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 production robot can work or fail on any given day. A stochastic process model can be used to calculate the probability of the robot breaking down in a week or how often it should be serviced. In this way, stochastic process models help companies to make better decisions, assess risks and optimise processes - even with incomplete information.















