ideas@work open house seminar 2011
DESCRIPTION
Slides of my presentation at Ideas@Work Open House Seminar, 30 March 2011, Brussel, BTRANSCRIPT
Faculty of Economics and Business Administration Department of Management Information and Operations Management
Presentation for Ideas@Work9 April 2023
Process MiningOpen House Seminar
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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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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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Example Scenario
Call center
Customer service process
CRM
Front Line Back LineCustomers
1
2
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Outline
1. Example Scenario
2. Roadmap
3. Process Mining Session
4. Take-away Points
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Example Scenario
Call center
Customer service process
CRM
Front Line Back LineCustomers
1
2
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Example Scenario
Increased costs:- More activities- Lower first call resolution rate
Decreased customer satisfaction:- Net promotor score (NPS)
Expected Process
Our problem:
Email Outbound
Email OutboundCall OutboundCall Outbound
Handle EmailHandle EmailHandle Case
Handle Case
StartStart
EndEnd
Inbound CallInbound Call Inbound EmailInbound Email
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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.
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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
CSVData
CSV file is starting point for our Session
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Event Log Construction
Input data needs to be mapped onto event sequences
Fluxicon’s tool Nitro makes this easy
CSVData
EventLog
Download from fluxicon.com/nitro
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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
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Roadmap
Focus of today’s session
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Hands-on Session
Let’s get started!
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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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Step 0 - Inspect Data
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Step 1- Construct Log
Start Nitro and load ExampleLog.csvAssign 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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Step 1- Construct Log
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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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Step 2 - Inspect Log
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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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Step 3 - Discover Process
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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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Step 4 - Add Start and End
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Step 5 - Discover Process
Choose ‘Mining ➞ Filtered ExampleLog.mxml.gz (Advanced filter) ➞ Heuristics miner’ from menu
Press ‘start mining’
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Step 5 - Discover Process
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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)
Email Outbound
Email OutboundCall OutboundCall Outbound
Handle EmailHandle EmailHandle Case
Handle Case
StartStart
EndEnd
Inbound CallInbound Call Inbound EmailInbound Email
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Not allowedNot allowed
Step 6 - Compare Process
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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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Step 7 - Construct New Log
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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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Step 8 - Inspect New Log
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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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Step 9 - Drill Down
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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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Step 10 - Discover Process
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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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Step 11 - Tune Level of Detail
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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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Step 12 - Animate Process
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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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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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Conformance Initial Model
67% of the cases “fit”
Email Outbound
Email Outbound
Call Outbound
Call Outbound
Handle Email
Handle Email
Handle Case
Handle Case
StartStart
EndEnd
Inbound CallInbound Call Inbound Email
Inbound Email
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Social Network Analysis
Shows case transfers between agents
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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
Faculty of Economics and Business Administration Department of Management Information and Operations Management
Presentation for Ideas@Work9 April 2023
My researchPhD project for Ghent University
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My research
CSVData
EventLog
CSVData
EventLog
EventLog
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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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Contact information
Jan [email protected]://processmining.ugent.be
FEB08, Tweekerkenstraat 29000 Gent, Belgium