Keynote Gartner Business Process Management Summit, February 2009, London

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Executive Keynote Gartner Business Process Management Summit
23 – 25 February 2009, London. Title "Process Mining: Beyond Business Intelligence" by Prof. dr. ir. Wil van der Aalst, Professor of Information Systems, Technische Universiteit Eindhoven.

This is something completely NEW, something people said wasn’t possible, that the data wasn’t there to allow systems that really could map out a process; they were wrong. Data is now everywhere; it is accessible, there is an abundance of data and it can provide you with insights you could never find just in interviews. The goal is to get away from workflow systems that are divorced from reality and from how people really work.



Today’s tools oversimplify reality when what you need is a view as close to the real world as possible. Since the 1990s such process tools have been a disappointment; they haven’t covered the true lifecycle. Process mining is a new step which involves seeing how processes are really being executed and using this as an input to allow the design and improvement of processes.

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Keynote Gartner Business Process Management Summit, February 2009, London

  1. 1. Process Mining: BeyondBusiness IntelligenceGartner Business Process Management Summit, February 2009, Londonprof.dr.ir. Wil van der Aalstwww.processmining.org
  2. 2. PAGE 1
  3. 3. Process Mining • Process discovery: "What is really happening?" • Conformance checking: "Do we do what was agreed upon?" • Performance analysis: "Where are the bottlenecks?" • Process prediction: "Will this case be late?" • Process improvement: "How to redesign this process?" • Etc. PAGE 2
  4. 4. • Process discovery: "What is the real curriculum?"• Conformance checking: "Do students meet the prerequisites?"• Performance analysis: "Where are the bottlenecks?"• Process prediction: "Will a student complete his studies (in time)?"• Process improvement: "How to redesign the curriculum?" PAGE 3
  5. 5. Outline• Trends in BPM• Process Mining: The Basics • Input data • Discovery • Conformance • Software support• Process Mining: Applications• Process Mining: TomTom for Business Processes• Conclusion PAGE 4
  6. 6. Trends in BPM
  7. 7. History• The first workflow management systems (called "office automation systems") were implemented in seventies, cf. Petri- net-based systems such as Officetalk (Xerox Parc, Skip Ellis) and SCOOP (Wharton, Michael Zisman).• Mid nineties: "explosion" of workflow products.• Shift from workflow automation to business process management. process diagnosis control process process enactment design implementation/ configuration PAGE 6
  8. 8. MS Workflow Foundation Global 360 BPM Suite YAWL FileNet InConcert Fujitsu Interstage Axxerion BWise Software AG/webMethods casewise COSA XPDL IBM WebSphere BPEL UML Savvion BusinessManager ADs BPM|one TIBCO iProcess SuitejBPM BPMN EPCs FlowConnect SAP Workflow Pegasystems SmartBPM Suite Ensemble Bizagi TeamWARE Oracle BPEL Promatis BiZZdesignerUltimus BPM Suite PAGE 7
  9. 9. Workflow Patterns Initiative• Initiative started in late 90-ties.• Collections: • 43 control-flow patterns (process/routing) • 40 data patterns • 43 resource patterns (work distr. and organization) • exception, flexibility, service interaction, ... patterns• Frequently used as a tool in selection processes.• Influenced standards (BPMN, BPEL, etc.) and systems.• See www.workflowpatterns.com (+/- 500 unique visitors per day) PAGE 8
  10. 10. Problem is NOT the automation of structuredprocesses! Alignment (Avoiding PowerPoint reality) Ensuring Supporting compliance flexibility PAGE 9
  11. 11. Where to start? process diagnosis control process mining process process enactment design implementation/ configuration PAGE 10
  12. 12. Process Mining:The Basics
  13. 13. Role of models "rea l wo "pow rld" erpo i nt re ality " PAGE 12
  14. 14. Event logs are a reflection of reality PAGE 13
  15. 15. Examples: PAGE 14
  16. 16. Process mining: Linking events to models PAGE 15
  17. 17. Starting point: event logsevent logs, audit unified event logtrails, databases, (MXML)message logs, etc. PAGE 16
  18. 18. Discovery PAGE 17
  19. 19. What to discover?• process models (Petri nets, EPCs, BPMN, etc.),• organizational models,• social networks,• sequence diagrams,• business rules,• bottlenecks,• simulation models,• etc.i.e., beyond "slice and dice" and showing KPIs on a dashboard ... PAGE 18
  20. 20. MXML Log - instances: 3512 - audit trail entries: 46138ProM supports +40 types of model discovery! PAGE 19
  21. 21. PAGE 20
  22. 22. PAGE 21
  23. 23. bottle- necks throughputflow time timefrom A to B PAGE 22
  24. 24. short time cases (relative) 46138 events long casescases PAGE 23
  25. 25. A bit of theory: Process discovery techniques • Algorithmic techniques • Alpha miner • Alpha+, Alpha++, Alpha# • Heuristic miner • Multi phase miner • ... • Genetic process mining • Region-based process mining • State-based regions • Language based regionscf. www.processmining.org for an overview PAGE 24
  26. 26. Example: Genetic Mining 1. initial population 6. mutation 7. new population 2. fitness test 5. children 4. crossover 3. select best parentsused in e.g. ProM, Futura Reflex, BPM|one PAGE 25
  27. 27. Conformance Checking PAGE 26
  28. 28. Conformance Checking• Compare process model and event log: highlight deviations and measure conformance.• Compare constraints/business rules and event logs: check e.g. the 4-eyes principle. PAGE 27
  29. 29. Tool support PAGE 28
  30. 30. • Open source initiative started in 2003 after several early prototypes.• Common Public License (CPL).• Current version: 5.0.• ProMimport: to extract MXML from all kinds of applications• Plug-in architecture.• About 250 plug-ins available: • mining plug-ins: 38 (all mining algorithms presented and many more) • analysis plug-ins: 71 (e.g., verification, SNA, LTL, conformance checking, etc.) • import: 21 (for loading EPCs, Petri nets, YAWL, BPMN, etc.) • export: 44 (for storing EPCs, Petri nets, YAWL, BPMN, BPEL, etc.) • conversion: 45 (e.g., translating EPCs or BPMN into Petri nets) • filter: 24 (e.g., removing infrequent activities) PAGE 29
  31. 31. Screenshot of ProM 5.0 PAGE 30
  32. 32. Business Intelligence Tools?• Business Objects (SAP)• Cognos Business Intelligence (IBM)• Oracle Business Intelligence• Hyperion (Oracle)• SAS Business Intelligence• Microsoft Business Intelligence• SAP Business Intelligence (SAP BI)• Jaspersoft (Open Source Business Intelligence)• Pentaho BI Suite (Open Source)• .... • Dashboards, reports, scorecards, ... • Slicing and dicing, data mining, ... PAGE 31
  33. 33. Process Mining Software Futura Reflect BPM|one Comprehend ARIS Process Performance Manager Interstage Automated Business Process Discovery & Visualization Process Discovery Focus Enterprise Visualization Suite PAGE 32
  34. 34. Process Mining:Applications
  35. 35. Where did we apply process mining?• Municipalities (e.g., Alkmaar, Heusden, Harderwijk, etc.)• Government agencies (e.g., Rijkswaterstaat, Centraal Justitieel Incasso Bureau, Justice department)• Insurance related agencies (e.g., UWV)• Banks (e.g., ING Bank)• Hospitals (e.g., AMC hospital, Catharina hospital)• Multinationals (e.g., DSM, Deloitte)• High-tech system manufacturers and their customers (e.g., Philips Healthcare, ASML, Thales)• Media companies (e.g. Winkwaves)• ... PAGE 34
  36. 36. Example: A Dutch Municipality 144 cases 1326 events PAGE 35
  37. 37. Conformance check of discovered model both performed while not allowed activity is good fit sometimes not 97.9% performed drill down PAGE 36
  38. 38. Performance analysis time bottle from neck A to B flow time PAGE 37
  39. 39. Events sorted by start time of case PAGE 38
  40. 40. Events sorted by duration PAGE 39
  41. 41. Idle time versus working time PAGE 40
  42. 42. "Real" animation PAGE 41
  43. 43. And of course ... PAGE 42
  44. 44. Reality ≠ PowerPoint (or Visio) PAGE 43
  45. 45. Process spectrumstructured unstructured(Lasagna) (Spaghetti) PAGE 44
  46. 46. 375 houses 18640 events82 different activities PAGE 45
  47. 47. 2712 patients 29258 events264 different activities PAGE 46
  48. 48. 874 patients 10478 events181 different activities PAGE 47
  49. 49. 24 machines 154966 events360 different activities PAGE 48
  50. 50. 37.5% OK 62.5% NOKdesign reality PAGE 49
  51. 51. Process Mining: TomTom forBusiness Processes
  52. 52. Business Process Navigation?• Often a good process map is missing (incorrect, outdated, no color, ...)• Process maps inherit the limitations of paper maps (no zoom or views)• Process maps tend to aim at "controlling the driver"• Current location unknown• No traffic information is given• No recalculation of the route• No estimated arrival time• ... PAGE 51
  53. 53. What we can learn from maps ... PAGE 52
  54. 54. Why imitate paper maps? • Zoom in - zoom out • Various views (e.g. show hotels and fuel stations at will) • Dynamic content! • Traffic information • Show current location PAGE 53
  55. 55. ProMs Fuzzy Miner: Seamless zoom PAGE 54
  56. 56. ProMs "real animation" PAGE 55
  57. 57. ProMs "real simulation" PAGE 56
  58. 58. Prediction and recommendation • Prediction: When are we home? • Recommendation: What should I do next? • Suggestions without force and the willingness to continuously recalculate the route. PAGE 57
  59. 59. ProMs Case prediction capabilities 144 cases 1326 events PAGE 58
  60. 60. Conclusion
  61. 61. Conclusion• The abundance of event data enables a wide variety of process mining techniques ranging from process discovery to conformance checking.• This is already possible today!• Check out ProM with its 250+ plug-ins.• A reality check for people that are involved in process modeling.• Demand TomTom functionality! PAGE 60
  62. 62. Thanks! cf. www.processmining.org• Wil van der Aalst • Mercy Amiyo • Jan Martijn van der Werf• Peter van den Brand • Carmen Bratosin • Martin van Wingerden• Boudewijn van Dongen • Toon Calders • Jianhong Ye• Christian Günther • Jorge Cardoso • Huub de Beer• Eric Verbeek • Ronald Crooy • Elena Casares• Ana Karla Alves de Medeiros • Florian Gottschalk • Alina Chipaila• Anne Rozinat • Monique Jansen-Vullers • Walid Gaaloul• Minseok Song • Peter Khisa Wakholi • Martijn van Giessel• Ton Weijters • Nicolas Knaak • Shaifali Gupta• Remco Dijkman • Sven Lambrechts • Thomas Hoffmann• Gianluigi Greco • Joyce Nakatumba • Peter Hornix• Antonella Guzzo • Mariska Netjes • René Kerstjens• Kristian Bisgaard Lassen • Mykola Pechenizkiy • Ralf Kramer• Ronny Mans • Maja Pesic • Wouter Kunst• Jan Mendling • Hajo Reijers • Laura Maruster• Vladimir Rubin • Stefanie Rinderle • Andriy Nikolov• Kenny van Uden • Domenico Saccà • Adarsh Ramesh• Irene Vanderfeesten • Helen Schonenberg • Jo Theunissen• Barbara Weber • Marc Voorhoeve • ...• Lijie Wen • Jianmin Wang PAGE 61
  63. 63. Relevant WWW sites• http://www.processmining.org• http:// promimport.sourceforge.net• http://prom.sourceforge.net• http://www.workflowpatterns.com• http://www.workflowcourse.com• http://www.vdaalst.com PAGE 62

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