4. Distribution of Papers @ BPM
4
Recker & Mendling: State-of-the-Art of Business Process Management Research as Published in the BPM conference (2015)
32. BPM’2012 use cases
• Design model
• Discover model from event
data
• Select model from collection
• Merge models
• Compose model
• Design configurable model
• Refine model
• Enact model
• Log event data
• Monitor
• Adapt while running
• Analyze performance based
on model
• Verify model
• Check conformance using
event data
• Analyze performance using
event data
• Repair model
• Extend model
• Improve model 3
33. BPM’2020 use cases
• Improve process using…
• Improve process using…
• Improve process using…
• Improve process using…
• Improve process using…
• …
• Improve BPM practice…
• Improve BPM practice…
• Improve BPM practice…
• Improve BPM practice…
• Improve BPM practice…
• …
33
50. Research Methods @ BPM
50
Recker & Mendling: State-of-the-Art of Business Process Management Research as Published in the BPM conference (2015)
51. Template for BPM’2016-2020 papers
This paper addresses the problem of optimizing
[variations | interactions | decisions | …) in business
processes [with respect to what criteria]
Based on [interviews | survey | Delphi | lit review], a
[model | method | …] is proposed…
The proposal is validated using [N case studies | user
experiments | performance experiments | …]
The results show that the proposal improves business
processes [how?, to what extent? w.r.t what baseline?]
51
The implementation of business process reengineering
Elzinga 1995
K. Tumay: Business process simulation
Georgakopoulos 1995
Traditionally focus was on predictable and highly repetitive processes. Typical example insurance and banking processes; still there is the need for adaptation, evolution and variability. Nowadays PAIS support is extended towards less predictable processes like innovation processes or processes for investigating the crime scene. These are typical examples for knowledge intensive processes. For these processes it cannot be predefined how the exact course of action will look like – since they are unpredictable. The exact course of action depends on situation-specific parameters which vary from case to case. Moreover, the process can be characterized as emergent. By executing activities knowledge is created which determines how to proceed. Many processes are however not on one of these extremes, but they are more in the middle of the spectrum like healthcare processes.
Unpredictability: Course of action depends on situation-specific parameters
Non-repeatability: Two process instances hardly look the same
Emergence: Future course of action depends on knowledge gained through activity execution