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Efficiency Optimization through Process Mining at BMW Group

Explore the utilization of process mining at BMW Group to analyze operational processes and derive recommendations for optimized vehicle distribution. Learn about the bupaR framework, use cases, preprocessing steps, control flow metrics, performance analysis, and predictive process analytics.

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Efficiency Optimization through Process Mining at BMW Group

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  1. Process Analysis with r BMW Group Julia Fumbarev | 11 September 2019

  2. Agenda • Aboutme • ProcessminingandbupaR • Usecase • Next steps

  3. Aboutme • Business Administration andElectrical Engineering at TUM in Munich • Data scientist at BMW • Expertise: Machine Learning, Data Mining

  4. Processmining • Companies collectdataabouttheirprocesses, howeveroftennotransparency • Process Mining is analyzing operational process executions based on event logs • Process mining aims at: • Discovering process model from event data • Checking the conformance of a advised model and real-life execution • Real-time process analysis • Predictive process monitoring

  5. Processdiscovery Processmining Eventlog Processmodel * Most frequentactivities covering 60% of the event log • Algorithms: • Alpha miner • Heuristicminer • Inductiveminer • Others event = case + activity + timestamp

  6. Tools • Commercial and open-source • Majorityare stand aloneprograms, without an interfaceto general-purposedatamining, visualizationorstatisticaltools • bupaRframeworkforeventdata (author Gert Janssenwillen, www.bupar.net) • Containspackages like edeadR (Calculatingdescriptiveprocessmetrics), processmapR (drawprocessmapandotherprocessspecificvisualization) • Easily extensible andcombinablewithothertools • Reusableanalysisscript

  7. Usecase: Vehicledistribution Background • BMW Group distributes 2.7 Mio cars in a year; average logistic costs 5-10 €/day/car • Complexity through big number of nodes and links in the logistics network • Complexity through many external factors and manual processes Task: Identify useful patterns for deriving recommendations for optimized outplacement

  8. bupaR - Create eventlog

  9. Bupar - preprocessing Event filters • Activities labels • Activity frequency • Resource labels • Resource frequency • Trim cases • Trim to time window Case filters • Throughput time • Processing time • Trace length • Activity presence • End points • Precedence • Trace Frequency • Time period

  10. Bupar - preprocessing Event filters • Activities labels • Activity frequency • Resource labels • Resource frequency • Trim cases • Trim to time window Case filters • Throughput time • Processing time • Trace length • Activity presence • End points • Precedence • Trace Frequency • Time period How do I getcarsthatstartwith a certainactivity? How do I gettraceswith 60% mostfrequentactivities?

  11. Bupar - preprocessing Event filters • Activities labels • Activity frequency • Resource labels • Resource frequency • Trim cases • Trim to time window Case filters • Throughput time • Processing time • Trace length • Activity presence • End points • Precedence • Trace Frequency • Time period How do I getcasesfor a certainperiod? How do I getcasesforwithcertainactivities?

  12. Usecase – controlflow • Metrics • Entry and exit points • Length of cases • Presence of activities • Rework • Visuals • Processmap • Trace explorer • Precedencematrix • Bar plot

  13. Usecase – controlflow • Metrics • Entry and exit points • Length of cases • Presence of activities • Rework • Visuals • Processmap • Trace explorer • Precedencematrix • Bar plot Rework points to in inefficiencies (time, recourses) and shouldbeavoided  Don‘tmove cars that need extra work

  14. Usecase – Performance • Metrics • Throughput time • Processing time • Idle time • Visuals • Performance processmap • Dottedchart • Box plot Cars that were taken out of storage should have long standing time  Reduce short term parking in MUCGA

  15. Usecase – Performance • Metrics • Throughput time • Processing time • Idle time • Visuals • Performance processmap • Dottedchart • Box plot Analysis of standing and transport time

  16. Whatelsecanbedonewiththeprocessgraph? • Clustering • Trace clustering • Sub processesclustering • Predictiveprocessanalytics • Compliance monitoring

  17. Clustering on processgraph Trace clustering • A lot of diversity leads to complex models that are difficult to interpret • event log is split into homogeneous subsets and for each subset a process model is created • One-hot encoding of the activities and enhancing with performance metrics • PCA for reducing the number of components • K-means for splitting the traces into groups

  18. Clustering on processgraph

  19. Predictiveprocessanalytics • predicting the next activity (anditstimestamp) • predicting the future path • predicting the remaining cycle time • predicting deadline violations  Applied LSTM, first results are promising. “Predictive Business Process Monitoring with LSTM Neural Networks”, NiekTax , Ilya Verenich , Marcello La Rosa , Marlon Dumas

  20. Thankyouforyourattention!Questions?

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