Emerging BPM Technologies

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From the BPM Emerging Technology summit keynote that I gave at Building Business Capability 2012 in Fort Lauderdale. Provides an introduction to social BPM, dynamic case management, process simulation, predictive process analytics, and process mining.

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Emerging BPM Technologies

  1. 1. Emerging Technologies in BPM Keynote: Emerging BPM Techniques & Technology Summit Building Business Capability 2012Sandy Kemsley l www.column2.com l @skemsley
  2. 2. Emerging BPM Techniques &Technologies Summit l The “Hurricane Sandy” edition l Thinking on the Job: Adaptive Case Management in Practice [cancelled] l Modeling and Analytics for Process Excellence [speaker replaced] l Process Mining: BPM Upside-Down [speaker arriving from Europe 9pm tonight] Copyright Kemsley Design Ltd., 2012 2
  3. 3. Technology: Social BPMHow Social ChangesEverything Copyright Kemsley Design Ltd., 2012 3
  4. 4. Consumer Tools Set Expectations l Consumption l Participation l Creation l User experience l Access anywhere Copyright Kemsley Design Ltd., 2012 4
  5. 5. Social BPM Business Benefits l Weak ties/tacit knowledge exploitation l Knowledge sharing l Social feedback l Transparency l Participation l Activity and decision distribution (crowd- sourcing) Copyright Kemsley Design Ltd., 2012 5 Source: Brambilla et al, “A Notation for Social BPM”
  6. 6. Collaborative Process Modeling l Multiple people participate in process discovery, modeling and documentation l Internal and external participants l Technical and non-technical participants l Preserves institutional memory l Facilitates cross-silo collaboration and innovation Copyright Kemsley Design Ltd., 2012 6
  7. 7. Process Event Streams l Timeline of activity for social monitoring l Process models during creation l Process instances during execution l Publish/subscribe model to “watch” certain processes or event types l Direct link to underlying process model or instance for unsolicited participation l Usually mobile-enabled Copyright Kemsley Design Ltd., 2012 7
  8. 8. Technology: Dynamic/Adaptive Case ManagementThe Changing Nature of Work Copyright Kemsley Design Ltd., 2012 8
  9. 9. The Extremes Of Work Routine Knowledge Work Work Copyright Kemsley Design Ltd., 2012 9
  10. 10. Goals Of Work TypesRoutine Work Knowledge Workl Efficiency l Flexibilityl Accuracy l Assist human knowledgel Process improvement workl Automation l Collect artifactsl “Classic” BPM l Adaptive Case Management (ACM) / Production CM / Dynamic CM Copyright Kemsley Design Ltd., 2012 10
  11. 11. Characterizing The ExtremesRoutine Work Knowledge Workl A priori process model l No a priori modell Controlled participation l Collaboration on demandl Automatable, especially l Little automation, but with service integration, guided by rules and rules and events events Copyright Kemsley Design Ltd., 2012 11
  12. 12. The Structured/UnstructuredDebate If you can’t model Exceptions are the it up front, you just new normal: every don’t understand process is different the process Copyright Kemsley Design Ltd., 2011 12
  13. 13. But It’s Not That SimpleStructured Work Unstructured Workl Some process are that l Some processes have repeatable, especially sufficient variability that automated processes modelling is inefficientl Ad hoc process l Instrumentation of exceptions already exist, unstructured processes they’re just off the grid provides value Copyright Kemsley Design Ltd., 2011 13
  14. 14. Structure SpectrumStructured Structured with Unstructured with Unstructured• e.g., automated ad hoc pre-defined • e.g., investigations regulatory process exceptions fragments • e.g., financial back- • e.g., insurance office transactions claims Copyright Kemsley Design Ltd., 2012 14
  15. 15. Dynamic Process Runtime l User can add participants from own network or recommended expert l Non-participant can opt-in to process l Audit trail captured within BPMS l Eliminates uncontrolled email processes l Captures patterns for process improvement Copyright Kemsley Design Ltd., 2012 15
  16. 16. Technology: Process MiningDiscovering Hidden ProcessGems Copyright Kemsley Design Ltd., 2012 16
  17. 17. Process Mining – Sources Copyright Kemsley Design Ltd., 2012 17
  18. 18. BPMS Event Log Format Trans. ID Activity Start Time End Time Resource 8287 Enter customer 08:34:15 08:37:44 User jsmith data 8287 Check credit 08:37:52 08:38:05 Equifax service call 1399 Enter customer 08:37:59 08:44:40 User sjones data 8287 Enter order 08:38:09 08:38:39 ERP system call 1399 Check credit 08:44:58 08:45:06 Equifax service call 4283 Enter order 08:45:01 08:45:35 ERP system call 1399 Enter order 08:45:18 08:45:38 ERP system call Copyright Kemsley Design Ltd., 2012 18
  19. 19. Combining All Event Logs Trans. Activity Start End Resource ID Time Time 8287 Enter customer 08:34:15 08:37:44 User jsmith data 8287 Create 08:34:25 08:35:55 User jsmith customer record 8287 Create address 08:36:12 08:37:39 User jsmith record 8287 Check credit 08:37:52 08:38:05 Equifax service call 8287 Enter order 08:38:09 08:38:39 ERP system call 8287 Check PO 08:38:10 08:38:15 System 8287 Create order 08:38:18 08:38:31 System Copyright Kemsley Design Ltd., 2012 19
  20. 20. Generating A Process Model Copyright Kemsley Design Ltd., 2012 20
  21. 21. Generated Model Data Source: Fluxicon 21
  22. 22. Working With Process MiningResults l Actual flows, not idealized models l Frequency and duration of each path l Optimization: l Detect main flows and common variations l Detect loopbacks and other inefficiencies l Detect wait times l Analyze variations over time Copyright Kemsley Design Ltd., 2012 22
  23. 23. More On Process Mining Process Mining: BPM Upside-Down Thursday, 11:30am, Diplomat 5 Anne Rozinat Fluxicon Copyright Kemsley Design Ltd., 2012 23
  24. 24. Technology: Process SimulationCharting A Course In UncertainConditions Copyright Kemsley Design Ltd., 2012 24
  25. 25. Model-Simulate-Analyze-Optimize Copyright Kemsley Design Ltd., 2012 25
  26. 26. Simulation Goals l Test and validate process models l Establish path patterns l Estimate end-to-end times l Optimize resource utilization and SLA performance across peak/slack periods l During runtime, predict performance based on realtime analytics Copyright Kemsley Design Ltd., 2012 26
  27. 27. Simulation in the BPM Lifecycle Source: Lanner 27
  28. 28. More On Analytics And Simulation Modeling and Analytics for Process Excellence Thursday, 10:10am, Diplomat 5 Denis Gagné Workflow Management Coalition (replacing Robert Shapiro) Copyright Kemsley Design Ltd., 2012 28
  29. 29. Technology: Predictive Analytics/Process IntelligenceSmarter Processes forSmarter Outcomes Copyright Kemsley Design Ltd., 2012 29
  30. 30. Why Predictive Processes? “Predictive analytics is not just about forecasting what’s coming down the pike. It’s also about keeping the bad alternative futures from happening.” James Kobielus, Forrester Copyright Kemsley Design Ltd., 2012 30
  31. 31. Process + Analytics + Decisions =Intelligent Processes Business Process Business Business Rules Intelligence Copyright Kemsley Design Ltd., 2012 31
  32. 32. Process Analytics in a BPMSl Executing processl Realtime process dashboard Copyright Kemsley Design Ltd., 2012 32
  33. 33. What You Can Do WithProcess Analytics l Information to support manual decisions l E.g., display queue sizes to help manager to reallocate work l Data to trigger automated actions l E.g., spawn fraud detection process when series of events occur for same customer l Predict missed SLAs l E.g., compare history of activity timeline to estimate overall time to completion Copyright Kemsley Design Ltd., 2012 33
  34. 34. Focus On The Goal, Not The Task l Compare: l Current to baseline model l Current to historical l Analyze: l Process dependencies and critical path l Simulate to identify future problems l Act: l Self-adjust through feedback to decisioning l In-process user guidance Copyright Kemsley Design Ltd., 2012 34
  35. 35. Slides at www.slideshare.net/skemsley Sandy Kemsley Kemsley Design Ltd.email: sandy@kemsleydesign.comblog: www.column2.comtwitter: @skemsley Copyright Kemsley Design Ltd., 2012 35

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