Process Mining and Predictive Process Monitoring
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This document discusses process mining and predictive process monitoring, detailing types of dashboards used to monitor business processes and improve performance. It outlines methodologies for data collection and analysis, and presents case studies illustrating the application of process mining in various industries such as insurance and healthcare. Additionally, it introduces predictive monitoring techniques to forecast process outcomes and improve decision-making.
Process Mining and Predictive Process Monitoring
- 1. Process Mining and Predictive Process Monitoring Marlon Dumas University of Tartu, Estonia marlon.dumas@ut.ee 1https://www.slideshare.net/MarlonDumas
- 2. Business Process Monitoring Dashboards & reports Process miningEvent stream DB logs Event log
- 4. • Aimed at process workers & op managers • Emphasis on runtime monitoring: detect & respond • Typical performance indicators: • Work-in-progress • Resource load • Problematic cases – e.g. overdue/at-risk cases Operational dashboards
- 5. Operational process dashboards • Aimed at process workers & operational managers • Emphasis on monitoring (detect-and-respond), e.g.: - Work-in-progress - Problematic cases – e.g. overdue/at-risk cases - Resource load
- 6. • Aimed at process owners / managers • Emphasis on analysis and management • E.g. detecting bottlenecks • Typical process performance indicators • Cycle times • Error rates • Resource utilization Tactical dashboards
- 8. Tactical Performance Dashboard @ Australian Insurer
- 9. Tactical Performance Dashboard @ Qld Government - Recruitment Process
- 10. • Aimed at executives & managers • Emphasis on management • Link process performance to strategic objectives • Often based on scorecards, e.g. balanced scorecard Strategic dashboards
- 11. Manage Unplanned Outages Manage Emergencies & Disasters Manage Work Programming & Resourcing Manage Procurement Customer Satisfaction 0.5 0.55 - 0.2 Customer Complaint 0.6 - - 0.5 Customer Feedback 0.4 - - 0.8 Connection Less Than Agreed Time 0.3 0.6 0.7 - Key Performance Process Strategic Performance Dashboard @ Australian Utilities Provider
- 12. Process: Manage Emergencies & Disasters Process: Manage Procurement Process: Manage Unplanned Outages Overall Process Performance Financial People Customer Excellence Operational Excellence Risk Management Health & Safety Customer Satisfaction Customer Complaint Customer Rating (%) Customer Loyalty Index Average Time Spent on Plan 1st Layer Key Result Area 2nd Layer Key Performance Satisfied Customer Index Market Share (%) 3rd & 4th Layer Process Performance Measures 0.65 0.6 0.7 0.7 0.6 0.8 0.4 0.8 0.5 0.4 0.5 0.8 0.4 0.54 0.58 0.67
- 14. Process Mining 14 / event log discovered model Discovery Conformance Deviance Difference diagnostics Performance input model Enhanced model event log’
- 15. Automated Process Discovery 15 Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility CID Task Time Stamp … 13219 Enter Loan Application 2007-11-09 T 11:20:10 - 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 11:24:35 - … … … …
- 16. Automated Process Discovery in Action Apromore.org
- 18. Conformance Checking with Behavioral Alignment Desired conformance output: • task C is optional in the log • the cycle including IGDF is not observed in the log Log traces: ABCDEH ACBDEH ABCDFH ACBDFH ABDEH ABDFH
- 19. Conformance Checking in Action 19 Full demo at: https://www.youtube.com/watch?v=3d00pORc9X8
- 20. Given two logs, find the differences and root causes for variation or deviance between the two logs Simple claims and quick Simple claims and slow Deviance Mining MODEL S. Suriadi et al.: Understanding Process Behaviours in a Large Insurance Company in Australia: A Case Study. CAiSE 2013
- 21. Sequence classification vs. log delta analysis L1 - Short stay 448 cases 7329 events L2 - Long stay 363 cases 7496 events Log delta analysis 48 statements In L1, “Nursing Primary Assessment” is repeated after “Medical Assign” and “Triage Request”, while in L2 it is not … N.R. van Beest, L. Garcia-Banuelos, M. Dumas, M. La Rosa, Log Delta Analysis: Interpretable Differencing of Business Process Event Logs. BPM 2015: 386-405
- 22. Tools • Apromore (open-source) • ProM (open-source) • Disco (Fluxicon) • Celonis Process Mining (Celonis) • Minit (MinitLabs) • myInvenio (Cognitive Technlogy) • ARIS Process Performance Manager (Software AG) • Signavio Process Intelligence • Discovery Analyst (StereoLOGIC) • Interstage Process Discovery (Fujitsu) • Perceptive Process Mining (Lexmark) • ProcessAnalyzer (QPR) 22
- 23. Apromore Process Analytics Platform http://apromore.org Open-source, highly scalable, SaaS BPM analytics platform
- 24. • Insurance - Suncorp, Australia • Government - Qld Treasury & Trade, Australia • Health - AMC Hospital, The Netherlands - São Sebastião Hospital, Portugal - Chania Hospital, Greece - EHR Workflow Inc., USA • Transport - ANA Airports, Portugal - Busan Port, South Korea - Kuehne + Nagel, Switzerland-Germany • Electronics - Phillips, The Netherlands • Banking, construction… etc. Process Mining: Where is it used?
- 25. Case Study: Suncorp Group • Top 20 Australian market • General & life insurance, banking, superannuation and investments management • 9M customers • 16K employees • $85 billion in assets
- 26. Each process is varied by product & brand… End to end insurance process Source: Guidewire reference models Total process variants: 3,000+ 30 variations 500 tasks Home Motor Commercial Liability CTP / WC Suncorp Insurance
- 27. Processing Problem OK OK Good Bad Expected Performance Line
- 28. Discover and analyse actual organisational processes from data Main result Key patterns that explain lower performance identified Simple Claim and Quick Simple Claim and Slow Discriminative model discovery MODEL
- 30. 1. Frame & Plan the Problem 2. Collect the Data 3. Analyze: Look for Patterns 4. Interpret & Create Insights 5. Create Business Impact Wil van der Aalst, 2012 Process Mining Methodology
- 31. 1. Plan & Frame the Problem • Frame a top-level question or phenomenon: - How and why does customer experiences 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
- 32. 1. Plan and Frame the Problem – Suncorp • Often “simple” claims take an unexpectedly long time to complete: - 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? • Define what a “simple” claim is • Create awareness of the extent of the problem Resources: • 2 part-time Business Analysts, 1 DB Administrator, 1 Executive Manager (sponsor) • 1 full-time QUT Researcher Timeframe: 4 months
- 33. • Find relevant data sources - Information systems, SAP, Oracle, BPM Systems… - Identify process-related entities and their identifiers and map entities to relevant processes in the process architecture • Extract traces - Collect records associated with process entities - Group records by process identifier to produce “traces” - Export traces into standard format (XES or MXML) • Clean - Filter irrelevant events - Combine equivalent events - Filter out traces of infrequent variants if not relevant 2. Collect the data
- 34. 2. Collect the data: minimum requirements
- 35. • Discover the real process from the logs • Calculate process metrics - Cycle times, waiting times, error rates… • Explore frequent paths • Discover types of cases (good vs bad) • Identify process deviances and early predictors • … 3. Analyze – look for patterns
- 36. How likely is it that a running process will become “deviant”? Will it end up in a negative outcome? Will it fail to meet its SLAs in the next 24 hours? Will it generate abnormal effort, costs or rework? Beyond Deviance Mining: Predictive Process Monitoring
- 37. Predictive Process Monitoring – Detailed View PAGE 38 Current situation • What is the next activity for this case? • When is this next activity going to take place? • How long is this case still going to take until it is finished? • What is the outcome of this case? Is the compensation going to be paid? Or rejected?
- 38. Predictive Monitoring Example: Debt Recovery Process 39 Debt repayment due Call the debtor Send a reminder Payment received
- 39. Debt repayment due Call the debtor Send a reminder Send a warning Call the debtor Call the debtor Send to external debt collection agency Call the debtor Send a reminder Send a warning Call the debtor Call the debtorCall the debtor Call the debtor Call the debtor Call the debtor Call the debtor Call the debtor Predictive Monitoring Example: Debt Recovery Process 40
- 40. Event log Classifier / Outcome Predictions Attributes Traces Predictive Process Monitoring: General Approach 41 Event log Regressor / structured predictor Future “paths” prediction Attributes Traces
- 41. Predictive Process Monitoring for Debt Collection 42Irene Teinemaa, Marlon Dumas, Fabrizio Maria Maggi, Chiara Di Francescomarino: Predictive Business Process Monitoring with Structured and Unstructured Data. Proc. of BPM 2016, pp. 401-417.
- 42. Text mining 43
- 44. Open-Source Predictive Process Monitoring Nirdizati.com • Predict process outcome – “Is this loan offer going to be rejected?”) • Predict process performance – “Will this claim take longer than 5 days to be handled?” • Predict future events – “What activity is likely to be executed next? And after that?”
- 45. Predictive Process Monitoring: Summary & Links 47 Nirdizati https://github.com/nirdizati/ Outcome Prediction Clustering-based method: http://goo.gl/ykozBf Index-based methd: https://goo.gl/BQFk7k Index-based with text: https://goo.gl/a2DoWT Next task & remaining path LTSM-based: https://goo.gl/mkQDyy Remaining time LSTM-based: https://goo.gl/mkQDyy
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