Evidence-Based Business Process Management


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Talk on evidence-based business process management delivered by Marlon Dumas at the Leading Practice Conference, Charleston, SC, USA, 5 March 2014

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Evidence-Based Business Process Management

  1. 1. Trends in Business Process Management The Era of Evidence-Based Business Process Management Marlon Dumas University of Tartu, Estonia In collaboration with Wil van der Aalst, Marcello La Rosa and Fabrizio Maggi Charleston, SC, USA 5-6 March 2014 LEAD the Way
  2. 2. Are you watching yourself? And your business processes?
  3. 3. 3 months later
  4. 4. Back to basics… 1. Any process is better than no process 2. A good process is better than a bad process 3. Even a good process can be improved 4. Any good process eventually becomes a bad process – …unless continuously cared for Michael Hammer
  5. 5. Business Process Intelligence (BPI) Business Process Intelligence BAM Process Analytics Reports & Dashboards Process Mining
  6. 6. Process Analytics: Dashboards Process Cycle Time of Order Processing Process Frequency of Order Processing Process Cycle Time of Order Processing split up to different Plants ARIS (Software AG)
  7. 7. Process Mining Sta rt Re gis te r or de r Pre pa re s hipme nt Event log (Re )s e nd bill Organization model Ship goods Conta ct cus t ome r Re ce ive paym e nt Archive orde r End Process model Disco, ProM, QPR, Celonis, Aris PPM, Perceptive Reflect Social network Performance dashboards 10 Slide by Ana Karla Alves de Medeiros
  8. 8. Automated Process Discovery CID Task Time Stamp … 13219 Enter Loan Application - 13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 - 13220 Enter Loan Application 2007-11-09 T 11:22:40 - 13219 Compute Installments 2007-11-09 T 11:22:45 - 13219 Notify Eligibility 2007-11-09 T 11:23:00 - 13219 Approve Simple Application 2007-11-09 T 11:24:30 - 13220 Compute Installements … 2007-11-09 T 11:20:10 2007-11-09 T 11:24:35 - … … … Notify Rejection Retrieve Applicant Data Enter Loan Application Approve Simple Application Compute Installments Notify Eligibility 11 Approve Complex Application
  9. 9. Process Mining: Value Proposition Understand your processes as they are • Not as you imagine them Back your hypotheses with evidence • Not only with intuitions and beliefs Quantify the impact of redesign options • Before and after
  10. 10. Process Mining: Where is it used?  Insurance –Suncorp Australia  Health –AMC Hospital, The Netherlands –São Sebastião Hospital, Portugal –Chania Hospital, Greece –EHR Workflow Inc., USA  Transport –ANA Airports, Portugal  Electronics –Phillips, The Netherlands  Government, banking, construction … You next?
  11. 11. How to?  Exploratory method –Discover models –Visualize performance over models –Discover and compare variants  Question-driven method –Identify a problem in a process –Decompose into questions –Measure and analyze questions
  12. 12. The L* Method 1. Plan & Frame the Problem 2. Collect the Data 3. Analyze: Look for Patterns 4. Interpret & Create Insights Create Business Impact Wil van der Aalst. “Process Mining”. Springer, 2012.
  13. 13. 1. Plan and Frame Problem  Frame the problem, e.g. as a top-level question or phenomenon –How and why does customer experience with our order-to-cash processes diverge (geographically, product-wise, temporally)? –Why does the process perform poorly (bottlenecks, slow handovers)? –Why do we have frequent defects or performance deviance?  Refine problem into: –Sub-questions –Identify success criteria and metrics  Identify needed resources, get buy-in, plan remaining phases
  14. 14. Planning step – Suncorp Case  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  Define what a “simple claim” is.  Create awareness of the extent of the problem
  15. 15. 2. Collect the data  Find relevant data sources –Information systems, SAP, Oracle (Celonis), BPM Systems –Identify process-related entities and their identifiers and map entities to relevant processes in the process architecture  Extract traces –Collect records associated to process entities (perhaps from multiple sources) –Group records by process identifier to produce “traces” –Export traces into standard format (XES)  Clean –Filter irrelevant events –Combine equivalent events –Filter out traces of infrequent variants if not relevant
  16. 16. 3. Analyze – Find Patterns  Discover the real process from the logs  Calculate process metrics –Cycle times, waiting times, error rates  Explore frequent paths  Identify and explore ``deviance‟‟  Discover “types of cases” –Classify e.g. by performance
  17. 17. Suncorp Case Not Ideal Expected Performance Line OK OK Good
  18. 18. Discriminative Model Discovery Simple “timely” claims Simple “slow” claims Main result Nailed down key activities/patterns associated with slower performance!
  19. 19. WHAT’S THE CATCH?
  20. 20. There you are!
  21. 21. Process Mining: Mastering Complexity  Filter –Filter out events (tasks) –Filter out traces  Divide by variants (trace clustering) –Many process models rather than one  Abstract (zoom-out) –Focus on most frequent tasks or paths –Identify subprocesses and collapse then down  Discover rules rather than models
  22. 22. Trace clustering G. Greco et al., Discovering Expressive Process Models by Clustering Log Traces
  23. 23. Zoom-out: ProM’s Fuzzy Miner
  24. 24. Extract Subprocesses ProM’s two-phase miner Bose, Veerbeck & van det Aalst: Discovering Hierarchical Process Models using ProM
  25. 25. Chania Hospital Use Case Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach
  26. 26. Chania Hospital Use Case Most frequent paths Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach
  27. 27. Chania Hospital Use Case Trace clustering Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach
  28. 28. Trace Clustering – General Principle
  29. 29. Do we really want models… Or do we want understanding? www.interactiveinsightsgroup.com
  30. 30. Discovering Business Rules Decision rules • Why does something happen at a given point in time? Descriptive (temporal) rules • When and why does something happen? Discriminative rules • When and why does something wrong happen?
  31. 31. Discovering Decision Rules CID Amount Installm Salary Age Len Task 13210 20000 2000 2000 25 1 NR 13220 25000 1200 3500 35 2 NE 13221 9000 450 2500 27 2 NE 13219 8500 750 2000 25 1 ASA 13220 25000 1200 3500 35 2 ACA 13221 9000 450 2500 27 2 ASA … … … … … … … Decision Miner installment > salary or …. Notify Rejection amount ≤ 10000 or … Approve Simple Application installment ≤ salary or … Notify Eligibility Approve Complex Application amount ≥ 10000 or … 34
  32. 32. Discovering Descriptive Rules ProM’s DeclareMiner
  33. 33. Oh no! Not again!
  34. 34. What went wrong?  Not all rules are interesting  What is “interesting”? –Generally not what is frequent (expected) –But what deviates from the expected  Example: –Every patient who is diagnosed with condition X undergoes surgery Y But not if the have previously been diagnosed with condition Z
  35. 35. Interesting Rules – Deviance Mining Something should have “normally” happened but did not happen, why? Something should normally not have happened but it happened, why? Something happens only when things go “well” Something happens only when things go “wrong”
  36. 36. Now it’s better… Maggi et al. Discovering Data-Aware Declarative Process Models from Event Logs
  37. 37. Discriminative Rule Mining Bose and van der Aalst: Discovering signature patterns from event logs.
  38. 38. Take-Home Messages  BPM is moving from intuitionistic to evidence-based –Like marketing in the past two decades  Convergence of BPM & BI  Business Process Intelligence  Increasing number of successful case studies  Maturing landscape of process mining tools and methods  Next steps: –More sophisticated tool support, e.g. automated deviance identification –Predictive monitoring: detect deviance at runtime
  39. 39. Table of Contents 1. Introduction 2. Process Identification 3. Process Modeling 4. Advanced Process Modeling 5. Process Discovery 6. Qualitative Process Analysis 7. Quantitative Process Analysis 8. Process Redesign 9. Process Automation 10. Process Intelligence http://fundamentals-of-bpm.org
  40. 40. Want to know more?  Task force on process mining (case studies, events, etc.) –http://www.win.tue.nl/ieeetfpm/  Process mining portal and ProM toolset –http://processmining.org  Process Mining LinkedIn group –http://www.linkedin.com/groups/Process-Mining-1915049  BPM‟2014 Conference, Israel, 8-11 Sept. 2014 –http://bpm2014.haifa.ac.il/
  41. 41. Questions? Marlon Dumas University of Tartu E-Mail: marlon.dumas@ut.ee For more information: www.fundamentals-of-bpm.org
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