Decisions for information or information for decisions? Optimizing information gathering in decision-intensive processes

Decision-intensive business processes are performed by decision makers who gather different pieces of information to reach the process objective: a final decision of high quality, for instance, the final price of a quote or the diagnosis of a failure of a hightech machine, as a result of an information-gathering process with minimum costs and efforts. Gathering all possible pieces of information results in high quality decisions, but also yields high costs and efforts. Therefore, decision makers require decision support to determine which information to gather to make the best final decision. This paper introduces an approach that supports a decision maker in the continuous trade-off between the effective acquisition of more information and cost-efficient decision making. The approach uses a well-defined modeling notation for decision-intensive processes, CMMN, and links it to a standard optimization technique, Markov Decision Processes. The approach calculates an optimal informationgathering solution, such that the expected result of the main decision minus the process cost for collecting information is optimized. The approach uses the solution to configure a run-time recommendation tool for the decision maker. The approach is flexible and allows that a decision maker ignores the advice; it then continues to offer recommendations in the subsequent states. We show the feasibility and effectiveness of our approach on a real-world quote process.