TomTom for Business Process Managment (TomTom4BPM)

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Invited Talk at 21st International Conference on Advanced Information Systems Engineering (CAiSE´09), 8-12 June 2009, Amsterdam, The Netherlands.

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TomTom for Business Process Managment (TomTom4BPM)

  1. 1. TomTom for BusinessProcess Management(TomTom4BPM)prof.dr.ir. Wil van der Aalstwww.processmining.org
  2. 2. Todays information systems are really crappycompared to a TomTom system! • Good maps? • Navigation by PowerPoints? • Traffic information? • Where is the next fuel station? • Who is in charge? • Seamless zoom? • Customizable views? • When will the destination be reached? PAGE 1
  3. 3. PAGE 2
  4. 4. PAGE 3
  5. 5. 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 4
  6. 6. • 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 5
  7. 7. Process MiningA step towards TomTom functionalityfor business processes
  8. 8. Where to start? process diagnosis control process mining process process enactment design implementation/ configuration PAGE 7
  9. 9. Process mining: Linking events to models PAGE 8
  10. 10. Process mining as a mirror ... PAGE 9
  11. 11. 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 10
  12. 12. Example: WMO process of a Dutch Municipality 144 cases (i.e., requests for adaptation of house)WMO = Wet Maatschappelijke Ondersteuning 1326 recorded events PAGE 11
  13. 13. Conformance check of discovered model both performed while not allowed activity is good fit sometimes not 97.9% performed drill down PAGE 12
  14. 14. Performance analysis time bottle from neck A to B flow time PAGE 13
  15. 15. Events sorted by duration PAGE 14
  16. 16. "Real" animation PAGE 15
  17. 17. And of course ... PAGE 16
  18. 18. Reality ≠ PowerPoint (or Visio) PAGE 17
  19. 19. Process spectrumstructured unstructured(Lasagna) (Spaghetti) PAGE 18
  20. 20. 375 houses 18640 events82 different activities PAGE 19
  21. 21. 2712 patients 29258 events264 different activities PAGE 20
  22. 22. 874 patients 10478 events181 different activities PAGE 21
  23. 23. 24 machines 154966 events360 different activities PAGE 22
  24. 24. 37.5% OK 62.5% NOKdesign reality PAGE 23
  25. 25. PAGE 24
  26. 26. Process Mining: TomTom forBusiness Processes
  27. 27. How can process mining help? • Good maps? • Navigation by PowerPoints? • Traffic information? • Where is the next fuel station? • Who is in charge? • Seamless zoom? • Customizable views? • When will the destination be reached? PAGE 26
  28. 28. city highway PAGE 27
  29. 29. ProMs "real animation" PAGE 28
  30. 30. When will I be home?
  31. 31. PAGE 30
  32. 32. ApproachWhen? 12-6-2009! PAGE 31
  33. 33. Input: partial trace and historic information (A B C D C D C D E)? (14-6-2009)! (12-6-2009)! PAGE 32
  34. 34. Input PAGE 33
  35. 35. Building transition systems C D {A,B} {A,B,C} {A,B,C,D} B B A C {} {A} {A,C} many E abstractionsABCD D are possible {A,E} {A,D,E}ACBD and supportedAEDABCD (a) transition system based on sets by ProMsABCD FSM minerAED C DACBD <A,B> <A,B,C> <A,B,C,D>... B A E D <> <A> <A,E> <A,E,D> C B D <A,C> <A,C,B> <A,C,B,D> (b) transition system based on sequences PAGE 34
  36. 36. Annotated transition system based onremaining time PAGE 35
  37. 37. Predictive information average: 7.2 average: 0 average: 10.33 st. dev.: 1.79 st. dev.: 0 st. dev.: 1.53 min: 6 min: 0 min: 9 max: 10 max: 0 max: 12 predict: 10.33 predict: 0 average: 25.75 [12,9,10] [6,10,6,6,8] [0,0,0,0,0] st. dev.: 12.25 C D {A,B} {A,B,C} {A,B,C,D} min: 13 max: 44 [18,26,44,13, 14,40,24] B predict: 7.2 B C {} {A} {A,C} Aaverage: 25.75 [22,19]st. dev.: 12.25 [18,26,44,13, predict: 25.75 Emin: 13 14,40,24] D {A,E}max: 44 {A,D,E} [34,31] [0,0]average: 32.5st. dev.: 2.12 A B C Dmin: 31 average: 20.5 average: 0max: 34 st. dev.: 2.12 st. dev.: 0 min: 19 min: 0 PAGE 36 max: 22 max: 0
  38. 38. Example: WOZ process in Dutch Municipality 1882 objections triggering 11985 activities PAGE 37
  39. 39. All 11985 events at a glance Average flow time is 107 days (with a huge variation) PAGE 38
  40. 40. For partial tracescorresponding to this state the estimated timeuntil completion is 8.5 days PAGE 39
  41. 41. Cross validation: Mean rootedAverage MSE MAPE training set and test set Error (MAE) PAGE 40
  42. 42. Some results PAGE 41
  43. 43. PAGE 42
  44. 44. Conclusion
  45. 45. Conclusion• The abundance of event data enables a wide variety of process mining techniques ranging from process discovery to conformance checking.• A reality check for people that are involved in process modeling.• TomTom functionality is already possible today!• Check out ProM with its 250+ plug-ins.• Contribute: case studies, plug-ins, etc. PAGE 44
  46. 46. 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• Nikola Trcka • Domenico Saccà • Adarsh Ramesh• Irene Vanderfeesten • Helen Schonenberg • Jo Theunissen• Barbara Weber • Marc Voorhoeve • Kenny van Uden• Lijie Wen • Jianmin Wang • ... PAGE 45
  47. 47. Relevant WWW sites http://www.senternovem.nl/innovatievouchers MKB 2.500 – 7.500 euro• 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 46

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