Más contenido relacionado Similar a Ideas@Work Open House Seminar 2011 (20) Ideas@Work Open House Seminar 20111. Process Mining
Open House Seminar
Faculty of Economics and Business Administration Presentation for Ideas@Work
Department of Management Information and Operations Management 30 August, 2011
2. Presentation
This presentation is from Fluxicon as part of
their Academic Initiative
It is used under the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0
Unported License
More information: www.fluxicon.com
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3. Goals of this tutorial
Understand phases of process mining analysis
Be able to get started and play around with
your own data
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7. Example Scenario
Start
Our problem:
Inbound Call Inbound Email
Increased costs:
- More activities
- Lower first call resolution Handle
Handle Email
rate Case
Decreased customer Call Outbound
Email
Outbound
satisfaction:
- Net promotor score (NPS)
Expected
End Process
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8. Example Scenario
Process Mining:
You can’t control what you can’t measure.
Questions:
1) Is the expected process the actual process?
2) Can we find points of improvement to save cost
or increase quality?
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10. Data Extraction
IT Admin of call center performs CRM
SQL Query on the CRM system
All cases started last month
For two problematic product
CSV
categories Data
CSV file is starting point for our Session
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12. Event Log Construction
Input data needs to be mapped onto event
sequences
Fluxicon’s tool Nitro makes this easy
Download from
fluxicon.com/nitro
CSV Event
Data Log
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14. Data Analysis
Event log can be loaded in open source
software ProM (We use Version 5.2)
Download from
www.promtools.
Event org/prom5/
Log
Academic toolset that is great to start
experimenting with process mining
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15. Roadmap
Focus of today’s session
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17. Step 0 - Inspect Data
Open ExampleLog.csv file in Excel and
inspect its contents
You can see information about
• Service instances
• Service operations
• Start and end times
• Additional data..
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18. Step 0 - Inspect Data
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19. Step 1- Construct Log
Start Nitro and load ExampleLog.csv
Assign columns as follows:
Service ID ➞ Case ID
Operation ➞ Activity
Start Date ➞ Set ‘column ignored’
End Date ➞ Timestamp
... ➞ Other
Agent ➞ Resource
Press ‘Start conversion’
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20. Step 1- Construct Log
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21. Step 2 - Inspect Log
Look at ‘Statistics’ tab to see overview
information about event log
Select ‘Explorer’ tab to inspect individual
service instances
Press ‘Export MXML file...’
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22. Step 2 - Inspect Log
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23. Step 3 - Discover Process
Start ProM and open ExampleLog.mxml.gz
Choose ‘Mining ➞ Raw
ExampleLog.mxml.gz (unfiltered) ➞
Heuristics miner’ from menu
Press ‘start mining’
Look at the resulting process model
- Numbers in rectangles are activity frequencies
- Lower number at arcs is frequency of connection
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24. Step 3 - Discover Process
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25. Step 4 - Add Start and End
Go back to log window and select ‘Filter’ tab
Select ‘Advanced’ filter tab
Select ‘Add Artificial Start Task Log Filter’
from list ➞ press ‘add selected filter’
➞ press ‘add new filter’
Select ‘Add Artificial End Task Log Filter’ ...
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26. Step 4 - Add Start and End
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27. Step 5 - Discover Process
Choose ‘Mining ➞ Filtered
ExampleLog.mxml.gz (Advanced filter)
➞ Heuristics miner’ from menu
Press ‘start mining’
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28. Step 5 - Discover Process
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29. Step 6 - Compare Process
Start
Answer question No. 1:
Is the expected process the Inbound Call Inbound Email
actual process?
Observations: Handle
Case
Handle Email
1. Actual process is much
more complex! Email
Call Outbound
Outbound
2. Does not always start with
calls or emails (quality problem)
End
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30. Step 6 - Compare Process
Not allowed
Not allowed
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31. Step 7 - Construct New Log
Goal: We want to see whether quality
problem is in front line (FL) or back line (BL)
Go back to Nitro and change
‘Agent Position’ field from ‘Other’ to
‘Activity’
Press ‘Start conversion’ and ‘Export MXML
file...’
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32. Step 7 - Construct New Log
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33. Step 8 - Inspect New Log
Open new log in ProM
Select ‘Filter’ tab and see how activities are
distinguished between BL and FL
Observation:
In ‘Start Events’ we can see that new cases are started in the
back line (should not happen)
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34. Step 8 - Inspect New Log
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35. Step 9 - Drill Down
Select ‘Inbound Call-BL’ in ‘Start events’
filter to focus on cases that start with this
activity
Go to ‘Summary’ tab in log window and scroll
to bottom to look at ‘Originators’
Actionable result for question No. 2:
Give targeted training: Agents can be asked to re-use existing
service instances
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36. Step 9 - Drill Down
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37. Step 10 - Discover Process
Go to ‘Filter’ tab in log window again, choose
‘Advanced’ filter tab
• Select + add ‘Add Artificial Start Task Log Filter’
• Select + add ‘Add Artificial End Task Log Filter’
Choose ‘Mining ➞ Filtered
ExampleLog.mxml.gz (Advanced filter)
➞ Fuzzy miner’ from menu
Press ‘start mining’
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38. Step 10 - Discover Process
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39. Step 11 - Tune Level of Detail
Move the slider in the ‘Node filter’ tab on
the right (“significance cutoff”) up and
down
Observe how the process can be simplified
and detailed dynamically
Pull the slider down to the bottom
Last step: We will now visualize how individual
cases flow through process
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40. Step 11 - Tune Level of Detail
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41. Step 12 - Animate Process
Go to ‘Animation’ tab and pull ‘Lookahead’
slider to the far left ➞ Press ‘view
animation’
Press ▷ button to start animation
Observe how one service instance after
another moves through the process
Drag needle to end of time line and observe
how most used paths get thicker and thicker
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42. Step 12 - Animate Process
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43. That’s it!
We learned how to discover a process model
and found opportunities to improve service
quality by targeted training
Close the loop: Take action and verify results
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44. Further Steps
Process Mining allows for much more:
• Perform quantitative analysis
• Explicitly check conformance of initial model
• Perform social network analysis
• ...
We could also include additional data sources
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45. Conformance Initial Model
Start
67% of the cases “fit”
Inbound
Inbound Call
Email
Handle Handle
Case Email
Call Email
Outbound Outbound
End
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47. Take-away Points
Real processes are often more complex than
you would expect
There is no one “right” model
You can take multiple views on the same data
Process mining is an explorative, interactive
activity
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48. My research
PhD project for Ghent University
Faculty of Economics and Business Administration Presentation for Ideas@Work
Department of Management Information and Operations Management 30 August, 2011
49. My research
CSV Event
Data Log
CSV Event
Data Log
Event
Log
Presentation for Ideas@Work - Research of Jan Claes
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50. Free data analysis
You are looking for an easy way to jump in?
I am looking for some real case examples.
Let’s work together!
Free process mining analysis
Minimal time investment
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51. Contact information
Jan Claes
jan.claes@ugent.be
http://processmining.ugent.be
FEB08, Tweekerkenstraat 2
9000 Gent, Belgium
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