minimizing overprocessing waste in business processes via predictive activity ordering

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

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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!)

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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

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Processing effort and overprocessing waste

• Minimum processing effort:

• (actual) Processing effort:

• Overprocessing:

How can we know the actual processing effort?

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Expected processing effort

• Knockout section with three activities:

• Reject probability of an activity

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Expected processing effort (cont’d)

• Knockout section with three activities:

• Knockout section with N activities:

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Our approach

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Our approach

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Our approach

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Our approach

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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

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Evaluation of predictive models: ROC

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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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Evaluation – reduction in # of checks

Avg # of checks reduced with our approach

Overprocessing is reduced

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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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Thank you

Q&A