Engineering the Decision-Making Process Using Multiple Markov Theories and DEMO

In the current fast-changing and turbulent operational environments, the organizations are continually being pressured by many endogenous and exogenous environmental variables. Many and complex effects occur simultaneously and large volumes of data are available. For this reason, in a process-based organization, when change is demanded (e.g., business processes re-engineering) it is difficult to collect, and interpret, the complete information about the current state of the organization. Therefore, a problem is how to decide which design actions should be enacted with the incomplete information available from the executed business processes. In this context, this paper combines information systems engineering (DEMO business transactions design) and operation research (Markov theories) to contribute to the decision-making body of knowledge. As the result, this solution enforces the organization with resiliency capabilities that are triggered whenever any misalignment occurs. The proposed solution is evaluated through argumentation and by a qualitative comparison between two Markov theories (MDP and POMDP) based on a real-world case study.