My business processes are deviant! What should I do about it?
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My business processes are deviant! What should I do about it?

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Talk about deviance mining and predictive monitoring of business processes. Delivered at the Second European BPM Roundtable, Liechtenstein, 15 May 2014.

Talk about deviance mining and predictive monitoring of business processes. Delivered at the Second European BPM Roundtable, Liechtenstein, 15 May 2014.

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    My business processes are deviant! What should I do about it? My business processes are deviant! What should I do about it? Presentation Transcript

    • Doctor, my business processes are deviant! What should I do about it? Marlon Dumas University of Tartu, Estonia European BPM Roundtable, Liechtenstein, May 2014 1
    • DEVIANT? LIKE WHAT? 2
    • Deviant like… • Violation of compliance rules – Purchases without required quotes – Delivery without Purchase Order (PO) – Invoice issued before PO • Violations of SLA objectives – High defect rates (e.g. customer complains) – High number of missed deadlines • Deviations w.r.t. cost targets – Cases taking abnormally more effort to handle – Cases requiring abnormal amounts of re-work 3
    • HAVEN’T YOU STANDARDIZED? Don‟t you have process models? Don‟t you communicate your processes in your company? Don‟t you have guidelines and instructions for process workers? Haven‟t you automated your processes? Don‟t you monitor your processes? 4
    • DIAGNOSIS: ACUTE DEVIANCE SYNDROME Fact sheet • Endemic: present in 99% of the process population • Most process owners don‟t know it • Many opt to ignore it • Very few treat it • Nobody has ever been cured… • But we can put it in remission 5
    • Two-pronged therapy Detect and explain Predict to prevent Deviance Mining Predictive Monitoring 6
    • Deviance Mining and Predictive Monitoring 7
    • Something should have “normally” happened but did not happen, why? Something should normally not have happened but it happened, why? What increases the chances that things go “well” (normal)? What increases the chances that things go “wrong” (deviant)? Deviance Mining 8
    • 1. Frame the Problem • Define deviance (“normal” cases vs. “deviant” cases) • Quantify deviance and its impact 2. Collect the Data • Extract event logs, include relevant data attributes • Organize by traces (“normal” vs “deviant”) 3. Analyze • Extract model for “normal” vs “deviant” cases, compare • Use sequence mining to find discriminative patterns • Construct classifiers to explain deviance 4. Interpret & Create Insights • Inspect and interpret classifiers • Derive causes of deviance, devise resolutions Deviance Mining: Basic Method 9
    • Case Study 1: Suncorp (Australia) • Oftentimes „simple‟ claims take an unexpectedly long time to complete – To what extent does the cycle time of the claims handling process diverge? – What distinguishes the processing of simple claims completed on-time, and simple claims not completed on time? – What `early predictors‟ can be used to determine that a given `simple‟ claim will not be completed on time? • Team of analysts, relevant managers, IT experts • Started with defining what a “simple claim” is. S. Suriadi et al.: Understanding Process Behaviours in a Large Insurance Company in Australia: A Case Study. CAiSE 2013: 449-464 10
    • OK OK Good Not Ideal Expected Performance Line Suncorp Case 11
    • Main result Nailed down key activities/patterns associated with slower performance! Simple “timely” claims Simple “slow” claims Suncorp Case: Delta Analysis 12
    • Sequence mining Decision trees, class association rules Cross validation R.P.J.C. Bose and W.P. van der Aalst: Discovering signature patterns from event logs. CIDM'2013 13 Case Study 2: Philips Healthcare Discovering Patterns of Faulty Units
    • Case Study 3: Commercial bank, China Mining Anomalous Software Project Issues • Extract features from traces based on which events occur in the trace • Apply a contrasting itemset mining technique  features in one class and not in the other • Decision tree to construct readable rules C. Sun, J. Du, N. Chen, S.-C. Khoo, Y. Yang. Mining explicit rules for software process evaluation. ICSSP’2013. 14
    • Other Case Studies • Undisclosed EU financial institution – Problem: Anomalies in purchasing process – Approach: Association rules mining • Undisclosed U.S. healthcare provider – Discriminate between cases leading to positive vs. negative outcomes – Approach: delta analysis and sequence mining • Rabobank ICT – Find patterns in IT change implementations that correlate with increased/decreased interactions or increased/decreased incidents 15Swinnen et al. Process Deviation Analysis - A Case Study. BPM Workshops 2011 Lakshmanan et al. Investigating clinical care pathways correlated with outcomes. BPM'2013. BPI Challenge 2014: http://www.win.tue.nl/bpi/2014/challenge
    • How likely is it that a running (apparently normal) case will become deviant? Will this case end up in a negative outcome? Will this process fail to meet its Service Level Objectives in the next 24 hours? Will this case generate abnormal effort, costs or rework? 16 Beyond Deviance Mining: Predictive (Deviance) Monitoring
    • Predictive Monitoring Techniques • Predicting completion times & deadline violations – Use process mining to calculate “max expected time” after each activity – Trigger alerts if expected time exceeded • Predicting negative outcomes – Based on decision trees or other classifiers – Based on clustering, nearest-neighbours… 17
    • Case Study 4: Transportation Provider Predicting “Late Show” Events • Predicting differences between expected & actual time of delivery to a carrier (e.g. airline) • Approach: – Identify correlations between “late show” events, completion time of activities, and external variables (e.g. weather, traffic) – Manually derive event processing rules to generate alerts at runtime 18 Feldman, Fournier, Franklin, Metzger. Proactive event processing in action: a case study on the proactive management of transport processes. DEBS’2013.
    • Take-home messages • Recognize your deviance • Quantify it • Analyze it • Monitor it • Predict it • Preempt it 19 Every good process eventually becomes a bad process… unless continuously cared for After: Michael Hammer (Handbook of BPM, Springer)