Using Markov Theory to Deliver Informed Decisions in Partially Observable Business Processes Operation

This article explores the stochastic capabilities offered by Markov theories combined with business transaction models, from the Enterprise Engineering field, to contribute to the decision-making body of knowledge. An agro-food case study shows the utility of this solution and the evaluation argues the management decisions value in situations where is not possible to fully observe the state of the reality, or to be fully aware about it. A full policy graph that forecasts the belief states from observations and enacted actions is delivered.