Minimizing Overprocessing Waste in Business Processes via

Predictive Activity Ordering

Ilya Verenich, Marlon Dumas, Marcello La Rosa, Fabrizio Maggi, Chiara Di Francescomarino

Presentation at CAiSE’2016 – Ljubljana, 15 June 2016

2

Knockout section • One activity with a negative outcome “knocks-out” the case

• To avoid overprocessing, we should execute first the activity that will knock-out the case (if we knew it!)

3

Minimizing overprocessing waste Execute highly selective tasks first.

Execute tasks that raise problems first Postpone expensive tasks until the end

Design-time approach (Aalst 2001) Our approach

Order checks by probability of case rejection and mean effort

• Reject probabilities and effort and constant for each case

• Does not take into account specifics of each case

• These values are specific for each case

• They are estimated via predictive models

4

Processing effort and overprocessing waste

• Minimum processing effort:

• (actual) Processing effort:

• Overprocessing:

How can we know the actual processing effort?

5

Expected processing effort

• Knockout section with three activities:

• Reject probability of an activity

6

Expected processing effort (cont’d)

• Knockout section with three activities:

• Knockout section with N activities:

7

Our approach

8

Our approach

9

Our approach

10

Our approach

13

Datasets

Bondora online P2P lending:• > 45K process cases• Knockout section with 3 independent activities,

P=(0.08,0.03,0.05)• > 30 case attributes

Environmental permit log (CoSeLoG project):• ca 1400 process cases• Knockout section with 3 semi-independent activities,

P=(0.01,0.01,0.61)• 4 case + 2 event attributes

14

Evaluation of predictive models: ROC

15

Number of checks required

• 1, if there will be at least one activity that will reject the case OR

• 3, otherwise

16

Evaluation – reduction in # of checks

Avg # of checks reduced with our approach

Overprocessing is reduced

17

Conclusion

• Using predictive models reduces overprocessing• Performance depends on the difference between average

rejection rate of checks• More experiments are needed for real-world scenarios (checks

can be dependent, etc.)

18

Thank you

Q&A