These days, companies and manufacturers experience a need for faster and automated adaptions or re-configurations of their workflows and business processes. By means of context- and situation-aware systems, these desires are partially achievable, however only in a reactive manner. This situation based reactive behavior leads to expensive, non-efficient and time-consuming delays within workflows and business processes and hence asks for more research. Current state of the art in the topic of machine learning enables new approaches to investigate in, for workflow adaptions by predicting situations and allow proactive measurements for workflow adaptions and re-configurations. In this thesis an abstract concept is developed, that allows to turn reactive workflow adaptions into proactive adaptions within situation-aware workflow management systems, by predicting situations. This is achievable through computation of situation confidence scores by means of machine learning models and algorithms. Further, this concept allows using these algorithms without expert knowledge in the topic of machine learning, by hiding the implementation details from the user. The concept of predicting situation confidence is tested on a use case scenario for real orders of a manufacturing company and compares the classification approaches Support Vector Machine, Multilayer Perceptron and Random Forest for the prediction of orders. Results show only good performance for the Random Forest classifier, but also a concomitant possible applicability of the concept. More algorithms need to be tested and the tested algorithms need improvements, to fortify the applicability of the developed concept.