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Copyright © 2011 Fluxicon
Process Mining
Tutorial
Loeng 9 Protsessi kaevandamine
Process Mining Tutorial 2
Goals of this tutorial
• Understand phases of process mining analysis
• Be able to get started and play around with your own data
Process Mining Tutorial 3
Outline
1. Example Scenario
2. Roadmap
3. Process Mining Session
4. Take-away Points
Process Mining Tutorial 4
Call center
Example Scenario Customer service process
CRM
Front Line Back Line Customers
1
2
Process Mining Tutorial 5
Example Scenario
Increased costs:
- More activities
- Lower first call resolution
rate
Decreased customer
satisfaction:
- Net promotor score (NPS) Expected
Process
Our problem: Inbound
Call
Inbound
Handle
Outbound
Call
Outbound
Handle
Case
Start
End
Process Mining Tutorial
Net promotor score (NPS)
NPS can be as low as −100 (everybody is a
detractor) or as high as +100 (everybody is
a promoter). An NPS that is positive (i.e.,
higher than zero) is felt to be good, and an
NPS of +50 is excellent.
6
Process Mining Tutorial 7
Example Scenario
Questions:
1) Is the expected process the actual process?
2) Can we find points of improvement to save
cost or increase quality?
Process Mining:
You can’t control what you can’t measure.
Process Mining Tutorial 10
Data Extraction
IT Admin of call center performs
SQL Query on the CRM system
• All cases started last month
• For two problematic product
categories
CRM
CSV
Data
CSV file is starting point for our Session
Process Mining Tutorial 12
Event Log Construction
• Input data needs to be mapped onto event
sequences
• Fluxicon’s tool Nitro makes this easy
CSV
Data
Event
Log
Download from
fluxicon.com/nitro
Process Mining Tutorial 14
Data Analysis
• Event log can be loaded in open source
software ProM (We use Version 5.2)
• Academic toolset that is great to start
experimenting with process mining
Download from
www.promtools.
org/prom5/
Event
Log
Process Mining Tutorial 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..
Process Mining Tutorial 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’
Process Mining Tutorial 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...’
Process Mining Tutorial 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
Process Mining Tutorial 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’ ...
Process Mining Tutorial 27
Step 5 - Discover Process
• Choose ‘Mining ➞ Filtered
ExampleLog.mxml.gz (Advanced filter) ➞ Heuristics miner’ from menu
• Press ‘start mining’
Process Mining Tutorial 29
Step 6 - Compare
Process • Answer question No. 1:
Is the expected process the
actual process?
• Observations:
1. Actual process is much
more complex!
2. Does not always start with
calls or emails (quality problem)
Inbound
Call
Inbound
Handle
Outbound
Call
Outbound
Handle
Case
Start
End
Process Mining Tutorial 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...’
Process Mining Tutorial 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)
Process Mining Tutorial 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
Process Mining Tutorial 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’
Process Mining Tutorial 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
Process Mining Tutorial 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
Process Mining Tutorial 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
Process Mining Tutorial 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 (NPS results and Servicer data)
Process Mining Tutorial 45
Quantitative Analysis
Determine No. of process
variants (354 unique
paths)
Analyze frequency
and timing of activities
Process Mining Tutorial 46
Conformance Initial Model
67% of the cases “fit” Inbound
Call
Inbound
Handle
Outbound
Call
Outbound
Handle
Case
Start
End
Process Mining Tutorial 47
Social Network Analysis
Shows case transfers between agents
Process Mining Tutorial 48
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