Engineering knowledge-intensive business processes

The course introduces students to Knowledge-intensive Processes (KiPs), which are business processes performed by knowledge workers who need to perform interconnected decision-making tasks. KiPs are data-driven, semi-structured business processes that require substantial flexibility to deal with uncertainty in their environment. The course first elaborates the conceptual foundations of KiPs and BPM solutions for KiPs. Next, different AI-based techniques for improving BPM support of KiPs are discussed, based on recent scientific papers in this field.

The taught concepts are applied in a project, in which groups of students (4-5 persons) have to do two assignments
 

• In the first assignment, students analyze a case study KiP by elaborating the organizational context and structure of the KiP.
• In the second assignment, students analyze a publicly available data set of a KiP using AI-based techniques. Based upon the analysis, they indicate improvements for the KiP.

General objective of this course is to introduce students to Knowledge-intensive Processes (KiPs), and to show how techniques from AI and BPM can be used in concert to model and analyze KiPs. For doing the project assignments, programming skills are essential.

After finishing this course, a student can

• Explain the concepts of knowledge-intensive BPM and the differences with classical BPM.
• Explain the application of AI-based techniques in knowledge-intensive BPM.
• Analyse a KiP by applying AI-based techniques.
• (Re)design a KiP based on these analysis results.