Successfully reported this slideshow.
Your SlideShare is downloading. ×

Automated Process Improvement: Status, Challenges, and Perspectives

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Loading in …3
×

Check these out next

1 of 32 Ad
Advertisement

More Related Content

Slideshows for you (20)

Similar to Automated Process Improvement: Status, Challenges, and Perspectives (20)

Advertisement

More from Marlon Dumas (20)

Advertisement

Automated Process Improvement: Status, Challenges, and Perspectives

  1. 1. Automated Process Improvement Status, Challenges & Perspectives Marlon Dumas Professor of Information Systems @ University of Tartu Co-founder @ Apromore Keynote, BPMDS + EMMSAD’2020, 8 June 2020
  2. 2. Process Mining 1.0 / event log discovered process model Automated Process Discovery Conformance Checking Variant Analysis Differences & diagnostics Performance Mining business rules / process model Enhanced process model event log’ Dumas et al. Fundamentals of Business Process Management, 2nd edition, Springer 2018
  3. 3. Prescriptive Analytics Predictive Analytics Diagnostic Analytics Descriptive Analytics The Evolution of Process Mining 3 Process Mining 1.0 Automated Process Discovery & Analysis Process Mining 2.0 Predictive Process Monitoring Automated Process Improvement
  4. 4. Operational Level Predictive Process Monitoring Predicting future states, outcomes, or properties of a process instance or group of process instances Prescriptive process monitoring Recommending actions on the basis of predictions to maximize a performance indicator Tactical Level Automated Process Improvement Robotic Process Mining Search-Based Process Optimization Process Mining 2.0 4
  5. 5. 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? 5
  6. 6. Predictive Process Monitoring Event stream Predictive models Detailed predictive dashboard Alarm-based prescriptive dashboard Aggregate predictive dashboards Event logDatabase Enterprise System 6
  7. 7. Event log Classifier / Regressor / Outcome predictionAttributes Traces 7 Event log Structured predictor Next activity / Future path prediction Attributes Traces Performance measure prediction Predictive Process Monitoring: General Approach
  8. 8. Predictive Process Monitoring Approaches Teinemaa et al. Outcome-Oriented Predictive Process Monitoring: Review and Benchmark. TKDD 13(2):17:1-17:57, 2019. Verenich et al. Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring. TIST 10(4), 2019. Tax et al. An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction. Software and Systems Modeling, 2020, to appear.
  9. 9. Explaining predictions Helping users understand the causes of predicted outcomes Turning predictions into actions Prescriptive process monitoring Challenges in Predictive Process Monitoring 10
  10. 10. Prescriptive process monitoring Event log (completed traces) Predictive model(s) Running trace Appl y P( ) Prediction Alarm/ no alarmPolicy +/- - - + Cost model Teinemaa et al. “Alarm-Based Prescriptive Process Monitoring”. Proceedings of BPM Forum’2018
  11. 11. Search-Based Process Optimizer Domain Knowledge IoT, Web& social sensing streams Automated Process Improvement EnterpriseSystem
  12. 12. Example: Improvement Opportunities 1 Officer Clerk Clerk Officer 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 This 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
  13. 13. Given • one or more 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
  14. 14. Task • Automate individual tasks or groups of tasks • Recommend best practices for task execution Control-flow • Task elimination/addition • Task merging/splitting • Task re-ordering, parallelization Decision (data) • Add / delete decision points • Refine / enhance decision rules Resource • Re-allocate resources • Refine / enhance resource allocation policies Automated Process Improvement Types of Changes
  15. 15. Automated Process Improvement 16 16 Execution data 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
  16. 16. 31 Automatable Task Example
  17. 17. Starting Point: UI log 18 V. Leno, A. Polyvyanyy, M. La Rosa, M. Dumas and F. Maria Maggi. Action logger: Enabling process mining for robotic process automation. In Proceedings of Demonstration Track at BPM 2019, 124–128, 2019
  18. 18. Robotic Process Mining: Synthesis of RPA Scripts for Task Automation 19
  19. 19. [copy to clipboard] A task is automatable if every step in the task can be deterministically executed based on input data, or data produced by previous actions [select cell C1] [select cell C2] [edit cell C2] 20 Automatable Task
  20. 20. 32 Synthesis of RPA Scripts as a Transformation Problem V. Leno et al. Automated Discovery of Data Transformations for Robotic Process Automation. In AAAI-20 Workshop on Intelligent Process Automation, New York, USA, January 2020
  21. 21. 322 Synthesis of RPA Scripts for Task Automation: Approach V. Leno et al. Automated Discovery of Data Transformations for Robotic Process Automation. In AAAI-20 Workshop on Intelligent Process Automation, New York, USA, January 2020
  22. 22. 323 Preprocessing Filter out redundant actions  Control-flow redundancy (e.g. double copying without pasting)  Data-flow redundancy (e.g. double editing of text field with replacement) Identify candidate routines (repetitive sequences of actions)  Sequential pattern mining  candidate routines  Clustering similar or related routines
  23. 23. 324 Extracting examples from candidate routines For each candidate routine trace:  Collect the values of all read cells/fields (Inputs)  Collect the latest values of all modified cells/fields (Outputs)  Create input-output transformation example (Inputs, Outputs) Inputs = [“Albert”, “Rauf”, “11/04/1986”, “+61 043 512 4834”, “arauf@gmail.com”, “Germany”, “99 Beacon Rd, Port Melbourne, VIC 3207, Australia”] Outputs = [“Albert Rauf”, “11-04- 1986”, “Germany”, “043-512- 4834”, “arauf@gmail.com”, “99 Beacon Rd”, “Port Melbourne”, “VIC”, “3207”, “Australia”]
  24. 24. 325 Transformation discovery FOOFAH – transformation discovery by example  Program synthesis as a search problem in a state space graph  Heuristic search approach based on A* algorithm  Cost function is the amount of manipulations  Deals with string and table manipulations +61 039 689 9324 +61 035 341 2938 +61 079 149 3015 +61 039 689 9324 +61 035 341 2938 +61 079 149 3015 039 689 9324 035 341 2938 079 149 3015 +61 039 689 9324 +61 035 341 2938 +61 079 149 3015 039 689 9324 035 341 2938 079 149 3015 039 689 9324 035 341 2938 079 149 3015 split_first(0, ‘ ‘) split(0, ‘ ‘) drop(0, ‘ ‘) drop(0, ‘ ‘) join(0, ‘ ‘) join(0, ‘ ‘) Input Output
  25. 25. 326 Transformation discovery FOOFAH – transformation discovery by example  Program synthesis as a search problem in state space graph  Heuristic search approach based on A* algorithm  Cost function is an amount of manipulations  Deals with string and table manipulations +61 039 689 9324 +61 035 341 2938 +61 079 149 3015 +61 039 689 9324 +61 035 341 2938 +61 079 149 3015 039 689 9324 035 341 2938 079 149 3015 split_first(0, ‘ ‘) split(0, ‘ ‘) drop(0, ‘ ‘) drop(0, ‘ ‘) join(0, ‘ ‘) join(0, ‘ ‘) Input Output +61 039 689 9324 +61 035 341 2938 +61 079 149 3015 039 689 9324 035 341 2938 079 149 3015 039 689 9324 035 341 2938 079 149 3015
  26. 26. 327 Foofah-Based Transformation Synthesis: Limitations Does not scale up to real examples when applied directly Discovers complex transformations (low readability) Optimizations  Synthesize one transformation per output field and use UI log to discover input-to-output data flows  Discover patterns in the input values and discover one transformation per input pattern
  27. 27. Robotic Process Mining 28  Extracting candidate routines from noisy UI logs  Handling heterogeneous occurrences of candidate routines • Many ways of performing the same routine  Discover transformations where the output fields are not (always) derived from fields that are explicitly accessed (e.g. using screenshot processing and/or eye tracking?)  Discover conditional transformations • The transformation steps to be performed depend on conditions in the inputs  Handling complex data types, e.g. copying a purchase order consisting of multiple line items Open Challenges
  28. 28. Automated Process Improvement 29 29 Execution data 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
  29. 29. Search-Based Process Optimization Discover Process Model Metaheuristics Optimizer (e.g. Genetic, Hill Climbing) Candidate Changeset Evaluator Candidate Changeset Generator New Pareto front Event log Candidate Change- sets Discover Simulation Model Simulation Model As-Is Process Model Current Pareto front Business Process Simulator Allowed Changes
  30. 30. 331 Automated Discovery of Simulation Models from Event Logs Camargo et al. Automated Discovery of Simulation Models for Event Logs, Decision Support Systems, to appear, 2020 https://github.com/AdaptiveBProcess/Simod
  31. 31. • Exploring search spaces of process changes is challenging due to combinatorial explosion and the fact that process changes may have non-additive effects Scalability • Simulation allows us to estimate changes in time and cost metrics, but what about quality metrics (e.g. defect rates)? Estimating the Effect of Changes • How can analysts conveniently capture the allowed space of changes and their associated costs? • How to help analysts to navigate through the discovered change-sets and their trade- offs? • How can we make the analyst trust the change recommendations made by an automated system? Usability Search-Based Process Optimization: Challenges 32
  32. 32. https://sep.cs.ut.ee/Main/PIX The Process Improvement Explorer (PIX)

Editor's Notes

  • Lost

    Make example of risk objectives
  • Lost

    Make example of risk objectives
  • https://www.if4it.com/core-domain-knowledge-critical-foundation-successful-design-thinking/
    https://towardsdatascience.com/minimum-viable-domain-knowledge-in-data-science-5be7bc99eca9
  • Use case inspired by a real-life scenario at the University of Melbourne
  • V. Leno, A. Polyvyanyy, M. La Rosa, M. Dumas and F. Maria Maggi. Action logger: Enabling process mining for robotic process automation. In Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019, 124–128, 2019

    Available recording tools (e.g., WinParrot, JitBit) record low-level action only – clickstreams, keystrokes
    Although RPA tools (e.g., UI Path, Automation Anywhere) provide recording capabilities they are focused on manual programming of scripts. They do not record values of involved fields, do not capture timestamps, etc.

    In UI Path Studio, however, there is a component called UI Explorer, that is similar to our Action Logger, but it works only for Web (supports limited amount of actions), while our tool covers also Excel spreadsheet
  • Baseline approach aims to discover document-to-document transformation, e.g. a program that maps all inputs into all outputs
  • https://www.if4it.com/core-domain-knowledge-critical-foundation-successful-design-thinking/
    https://towardsdatascience.com/minimum-viable-domain-knowledge-in-data-science-5be7bc99eca9

×