Downloaded 115 times
Uploaded byMarlon Dumas
AI for Business Process Management
The document discusses the integration of artificial intelligence in business process management, focusing on techniques like process mining and automated process improvement. It outlines various methodologies for analyzing and enhancing business processes, including event logs, predictive monitoring, and robotic process mining. Additionally, challenges in implementing these techniques and the potential for automation are explored.
More Related Content
Similar to AI for Business Process Management
More from Marlon Dumas
![Polymer [ बहुलक ] Chemistry Notes PDF - Irfanullah Mehar - JJ Sir Chemistry.pdf](https://cdn.slidesharecdn.com/ss_thumbnails/polymerchemistrynotespdf-irfanullahmehar-jjsirchemistry-260210172118-3f9b37f7-thumbnail.jpg?width=640&height=640&fit=bounds)
AI for Business Process Management
- 1. Marlon Dumas University ofTartu Institute of Computer Science AI for Business Process Management From Process Mining to Automated Process Improvement Bolzano Rules and Artificial INtelligence Summit (BRAIN 2019) 17 September 2019
- 2. a set ofinter-related events, activities and decisions ...involving a number of actors (resources) and (data) objects, ….triggered by a need and leading to an outcome that is of value to a customer. Examples: • Order-to-Cash • Procure-to-Pay (aka Purchase-to-Pay) • Application-to-Approval • Fault-to-Resolution A business process is… 2
- 3. Business Process Management(BPM) Process identification Conformance and performance insights Conformance and performance insights Process monitoring and controlling Executable process model Executable process model Process implementation To-be process model To-be process model Process analysis As-is process model As-is process model Process discovery Process architectureProcess architecture Process redesign Insights on weaknesses and their impact Insights on weaknesses and their impact 3
- 4. Performance Dashboards Process Mining Database Enterprise System BusinessProcess Monitoring Event log Event stream 4
- 5. Structure of aBusiness Process Event Log
- 6. Tactical Process Mining 6 6 / eventlog discovered process model Automated Process Discovery Conformance Checking Variants Analysis Difference diagnostics Performance Mining input process model Enhanced process model event log’
- 7. Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility CID TaskTime 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 - … … … … Process Map (directly follows graph) BPMN process model Automated Process Discovery 7
- 8. Alpha miner (α-miner) •Simple, limited, not robust Heuristics miner (and derivatives, including Fodina) • Robust to noise, fast, but can produce incorrect models Inductive miner • Ensures that models are block-structured & correct Split miner • Produces deadlock-free but not necessarily structured models Discovering BPMN Process Models 8 Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility event log
- 9. 9 Process Model Log Unfitting behavior (lack offitness) Additional behavior (lack of precision) Lack of generalization Accuracy of Automated Process Discovery
- 10. Lack of fitness Event log: ABCDEH ACBDEH ABCDFH ACBDFH ABDEH ABDFH ConformanceChecking 10 García-Bañuelos et al. “Complete and Interpretable Conformance Checking of Business Processes” IEEE Transactions on Software Engineering 44(3): 262-290, 2018 Lack of precision
- 11. • 24 real-lifeevent logs (most from IEEE Task force on Process Mining) • Quality criteria: • Accuracy measures: Fitness, precision, F-Score, generalization • Model complexity measures: size, structural complexity, structuredness • Model soundness • Execution time • Main conclusions: • Inductive Miner, Evolutionary Tree Miner, Split Miner have highest F-scores • Closely followed by Fodina • Inductive Miner achieves highest fitness generally, but lower precision (than Split Miner) • Evolutionary tree miner produces simpler models, but high execution times Automated Process Discovery Benchmark 11 Adriano Augusto et al. “Automated Discovery of Process Models from Event Logs: Review and Benchmark”. IEEE Transactions on Knowledge and Data Engineering (2018), DOI: 10.1109/TKDE.2018.2841877
- 12. Automated Process DiscoveryMethods Heuristics Miner good F-score complex models semantic errors 12 Inductive Miner high fitness no semantic errors simpler models low precision A. Augusto et al. Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs. In ICDM’2017. Split Miner high fitness no semantic errors simpler models Moderate precision
- 13.
- 14. 14 ≠ Conformance Checking Given aprocess model and an event log, find, describe, and/or measure the differences between them
- 15. Unfitting behaviour: • TaskC is optional (i.e. may be skipped) in the log Additional behavior: • The cycle including IGDF is not observed in the log Event log: ABCDEH ACBDEH ABCDFH ACBDFH ABDEH ABDFH Conformance Checking – Output 15 García-Bañuelos et al. “Complete and Interpretable Conformance Checking of Business Processes” IEEE Transactions on Software Engineering 44(3): 262-290, 2018
- 16. Behavior Alignment Petri Net compress DAFSA Reachability Graph PSP EventLog Optimal Alignments Difference Statements expand compare (1) (2) (3) 16 Daniel Reißner, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, Abel Armas-Cervantes: Scalable Conformance Checking of Business Processes. OTM Conferences (1) 2017: 607-627
- 17. Process Model Repair A.Armas Cervantes et al. “Interactive and Incremental Business Process Model Repair”, Proceedings of CoopIS’2017 17 Research Challenge • Minimal repaired model with syntactic and semantic guarantees? Constraint Solving, Automated Planning, Synthesis_
- 18. Statistics-Based Techniques Performance Dashboards Model-BasedTechniques Process Mining Database Enterprise System Business Process Analytics Event log Event stream 18 18
- 19. Predictive Process Monitoring Event stream Predictive models Detailedpredictive dashboard Alarm-based prescriptive dashboard Aggregate predictive dashboards Event logDatabase Enterprise System 19
- 20. Predictive Process Monitoring •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? 20
- 21. Event log Classifier / Regressor / OutcomepredictionAttributes Traces 21 Event log Structured predictor Next activity / Future path prediction Attributes Traces Performance measure prediction Predictive Process Monitoring: General Approach
- 22. Predictive process monitoringworkflow Prefix extraction Bucketing Sequence encoding Model training
- 23.
- 24.
- 25. Sequence encoding Prefix extractionBucketing Sequence encoding Model training (Amsterdam, (Paris, 2 adults, 1 adult, 3 days, 4 days, €100) €150) not cancelled cancelled (Amsterdam, (Paris, 2 adults, 1 adult, €100) €150) not cancelled cancelled (Amsterdam, (Paris, 2 adults, 1 adult, €100) €150) not cancelled cancelled (Amsterdam, 2 adults, 3 days, €100) not cancelledJuly, (Amsterdam, (Paris, 2 adults, 1 adult, 3 days, 4 days, €100) €150) not cancelled cancelled April, July,
- 26. Model training Prefix extraction Bucketing Sequence encoding Model training (Amsterdam, (Paris , 2 adults, 1adult, 3 days, 4 days, €100 ) €150 ) not cancelled cancelled (Amsterdam, (Paris , 2 adults, 1 adult, €100 ) €150 ) not cancelled cancelled (Amsterdam, (Paris , 2 adults, 1 adult, €100 ) €150 ) not cancelled cancelled (Amsterdam, 2 adults, 3 days, €100 ) not cancelledJuly, (Amsterdam, (Paris , 2 adults, 1 adult, 3 days, 4 days, €100 ) €150 ) not cancelled cancelled April, July,
- 27. Predictive process monitoringworkflow Encoding Bucketing Learning Training set Last state Aggregation Index-based … Zero Cluster Prefix-length … Decision tree Random forest SVM … Buckets Models 27
- 28. Taxonomy of predictiveprocess montioring approaches What is the relative performance of these methods? Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi: Outcome-Oriented Predictive Process Monitoring: Review and Benchmark. TKDD 13(2): 17:1-17:57 (2019)
- 29. Evaluation datasets Dataset Domain# traces Median # events in trace Class ratio 1 bpic2011_1 Hospital treatment 1140 25 0.4 2 bpic2011_2 Hospital treatment 1140 54.5 0.78 3 bpic2011_3 Hospital treatment 1121 21 0.23 4 bpic2011_4 Hospital treatment 1140 44 0.28 5 bpic2012_1 Loan application 4685 35 0.48 6 bpic2012_2 Loan application 4685 35 0.17 7 bpic2012_3 Loan application 4685 35 0.35 8 bpic2015_1 Building permit 696 42 0.23 9 bpic2015_2 Building permit 753 55 0.19 10 bpic2015_3 Building permit 1328 42 0.2 11 bpic2015_4 Building permit 577 42 0.16 12 bpic2015_5 Building permit 1051 50 0.31 13 bpic2017_1 Loan application 31413 35 0.41 14 bpic2017_2 Loan application 31413 35 0.12 15 bpic2017_3 Loan application 31413 35 0.47 16 Production Manufacturing 220 9 0.53 17 sepsis_1 Hospital treatment 754 14 0.14 18 sepsis_2 Hospital treatment 782 13 0.14 19 sepsis_3 Hospital treatment 782 13 0.14 20 Traffic Traffic fines 129615 4 0.46 21 hospital_1 Hospital finances 77525 6 0.1 22 hospital_2 Hospital finances 77525 6 0.05 23 insurance_1 Insurance 1065 12 0.16
- 30. XGBoost Random forest Logistic regression Supportvector machine Average rank over the 24 datasets in terms of AUC Results: Learning Algorithms (Nemenyi test)
- 31. Average rank over the24 datasets in terms of AUC Results: Bucketing and Sequence Encoding
- 32. • Predict processoutcome (e.g. “Is this loan offer going to be rejected?”) • Predict process performance (e.g. “Will this claim take longer than 5 days to be handled?”) • Predict future events (e.g. “What activity is likely to be executed next? And after that?”) Event log Training module Training Validation Predictor Dashboard Runtime module Information system Predictions Stream (Kafka) Predictive model(s) Event stream Event stream Batched Predictions (CSV) Apromore Predictive process monitoring (Apromore) 32 32 http://apromore.org
- 33. • Explainable &Actionable Predictive Process Monitoring • Extracting interpretable predictions • Helping users understand the root causes of predicted outcomes • Turning predictions into actions • Prescriptive process monitoring Frontier challenges in process mining 33
- 34. Prescriptive process monitoring Eventlog (completed traces) Predictive model(s) Running trace Appl y P( ) Predictio n Alarm/ no alarm Alarm policy +/- - - + Cost model Irene Teinemaa et al. Alarm-Based Prescriptive Process Monitoring. In Proc. of BPM Forum’2018
- 35. Cost model Undesired outcomeDesired outcome Alarm raised cost of intervention + (1 - mitigation effectiveness) * cost of undesired outcome cost of intervention + cost of compensation Alarm not raised cost of undesired outcome no costs Time Mitigation effectiveness Cost of intervention Undesired outcome
- 36. • Raise analarm if P(undesired outcome) > 𝜏 • Optimal 𝜏 is found via empirical thresholding Alarm policy P(cancel) = 0.2 0.6 0.8 Alarm Search View View Example: 𝜏 = 0.65 • What if the alarm may be ignored by the users (no intervention)? • E.g. resources are limited for interventions • What if the effect of the alarm depends on the state of the case? • What if there are multiple possible alarms/ interventions,
- 37. • Actionable PredictiveProcess Monitoring • Extracting interpretable predictions • Helping users understand the root causes of predicted outcomes • Turning predictions into actions • Prescriptive Process Monitoring • Automated Process Improvement • Automated control-flow, resource and decision optimization • Robotic Process Mining: Discovering executable routine specifications from UI logs (related to Robotic Process Automation) Frontier challenges in process mining 37
- 38. Automated Process Improvement 3 Officer Clerk ClerkOfficer Officer Clerk Skip credit history check when customer has previous loans with bank Allocate an additional clerk on Monday-Tuesdays, one less officer on Fridays Task can be automated with an RPA script For consumer loans, check credit history before income If loan-to-annual- income ratio > 1.5, allocate a senior officer If credit rating is C or D, do not wait for appeal
- 39. Given • one ormore event logs recording the execution of one or more processes • one or more performance measures that we seek to maximize/minimize • a process model, decision rules and resource allocation rules • a set of allowed changes to the process model and associated rules Find • One or all set(s) of Pareto-optimal changes to the process model and rules. Automated Process Improvement
- 40. Control-flow • Task elimination/addition •Merging/separation • Re-ordering, parallelization Decision (data) • Adding/deleting decision points • Refining/enhancing decision rules Resource • Re-allocating resources • Refining, enhancing allocation policies Task • (Partially) automating individual tasks or groups of tasks Automated Process Improvement: Types of Changes
- 41. Automated Process Improvement 41 41 eventlog Executable routine specifications Robotic Process Mining (Task Automation) Decision Rule Optimization Flow Optimization Optimized process model Resource Optimization Decision rules Optimized resource allocation policies Optimized decision rules
- 42. Robotic Process Mining Information System EventLog Process Mining Discovery Conformance Enhancement Process Model Interaction Information systems Users (employees) UI log Recording
- 43.
- 44. Robotic Process Mining 44 Information System EventLog Process Mining Discovery Conformance Enhancement Process Model Interaction Information systems Users (employees) UI log Routine Routine specification RPA script Identification Discovery Compilation Research challenge Recording
- 45. 45 Detect automatable routines: 1.Detect automatable actions 2. Return only those frequent subsequences made of automatable actions An action is automatable if all its arguments are constant or functions of arguments of previously-executed actions Robotic Process Mining: Initial Approach Detect automatable routines UI log Extract frequent subsequences Antonio Bosco et al. Discovering Automatable Routines From User Interface Logs. In Proc. of BPM Forum’2019
- 46. 46 Robotic Process Mining:Initial Approach Foofah – Discovering Data Transformations by Example Antonio Bosco et al. Discovering Automatable Routines From User Interface Logs. In Proc. of BPM Forum’2019
- 47. 47 Discover activation conditions: Foreach automatable routine, discover its activation condition, containing: 1. Triggering action, which must be successfully executed before the routine 2. Boolean condition, which must be valid at the completion of the triggering action Robotic Process Mining: Initial Approach Detect automatable routines UI log Extract flat polygons Discover activation conditions Routines specs Antonio Bosco et al. Discovering Automatable Routines From User Interface Logs. In Proc. of BPM Forum’2019
- 48. Challenges Robotic Process Mining •Identifying patterns in unstructured and noisy UI logs • Identifying complex data transformations in the presence of noise • Identifying conditional data transformations (only apply in some cases) Automated Process Improvement • The process model, decision rules, and resource allocation policies may not be known, may be incomplete or imprecise • The set of possible process changes may be too large for exhaustive exploration • Process changes may have non-linear effects (e.g. on waiting time) • The effects of process changes are not additive 48
- 49. https://sep.cs.ut.ee/Main/PIX The Process ImprovementExplorer (PIX)