Presentation by Ilya Verenich at the CAiSE'2016, Ljubljana, 15 June 2016

1. 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
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• 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
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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:
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How can we know the actual processing effort?

5. Expected processing effort
• Knockout section with three activities:
• Reject probability of an activity
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6. Expected processing effort (cont’d)
• Knockout section with three activities:
• Knockout section with N activities:
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7. Our approach
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8. Our approach
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9. Our approach
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10. Our approach
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11. Datasets
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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

12. Evaluation of predictive models: ROC
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13. Number of checks required
• 1, if there will be at least one activity that will reject the case
OR
• 3, otherwise
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14. Evaluation – reduction in # of checks
Avg # of checks reduced with our approach
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Overprocessing is reduced

15. 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.)
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16. Thank you
Q&A
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