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Process Mining in
Action
Self-service data science for
business teams
Marlon Dumas
Professor of Information Systems @ University of Tartu
Co-founder @ Apromore
Meet Tom
- Customer Excellence Manager @ XYZ
- Tom cares about customers, recurrent
sales revenue, efficient service delivery, …
- Every week, he has different questions:
- Why did our churn rate increased last month?
- Why did the number of customer complaints
keep rising?
- Why are our response times so slow even
though we have added more staff?
- Should I automate parts of my customer
service process? Should I buy this brand new
chatbot? Should I buy the brand new co-
browsing platform I saw last week?
Tom’s company has tons of data
in their information systems!
- Customers’ web site visits
- Customers’ orders
- Customers’ complaints
- The activity of salespeople
- The activity of customer service staff
- Your shipments, product returns, …
- etc.
How to use these data?
4
https://www.youtube.com/watch?v=z9b9ZeU5aac
• Nice option, very useful to address specific challenges
• …but will you keep calling on them every day you
need an insight?
Hire a data science consultancy that delivers
successfully on 85% of its projects
• Possible in larger companies, but how much time it
takes them to gain your domain & business
knowledge?
Hire a data scientist or data science team
• Is it possible?
Set up a self-service-system that allows you to
analyze the data yourself.
Digital Footprint
Every process leaves digital footprints (transactions)
Data Preparation
Data is prepared (e.g. multiple datasets are merged)
Event Collection
Transactional data is collected from enterprise systems
and other sources
Process Analysis
Process mining algorithms are used to extract process
models and other process analytics.
Step 01
Step 02
Step 03
Step 04
Process Mining:
Self-Service Data-Driven Process Analysis
Input: Business Process Event Log
Process Mining
event log
discovered
process model
Automated Process
Discovery
Enter Loan
Application
Retrieve
Applicant
Data
Compute
Installments
Approve
Simple
Application
Approve
Complex
Application
Notify
Rejection
Notify
Eligibility
CID Task Time Stamp …
13219 Enter Loan Application 2007-11-09 T 11:20:10 -
13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 -
13220 Enter Loan Application 2007-11-09 T 11:22:40 -
13219 Compute Installments 2007-11-09 T 11:22:45 -
13219 Notify Eligibility 2007-11-09 T 11:23:00 -
13219 Approve Simple Application 2007-11-09 T 11:24:30 -
13220 Compute Installements 2007-11-09 T 11:24:35 -
… … … …
Process Map
(directly follows graph)
BPMN process model
Automated Process Discovery
8
Process Mining
/
event log
discovered
process model
Automated Process
Discovery
Conformance
Checking
Business rules /
normative model
END-TO-END PROCESS IMPROVEMENT
Copyright 2020, Apromore Pty Ltd
Copyright 2020, Apromore Pty Ltd
Conformance checking
PROCESS MINING 101
Modeled process
(Expected: 8 hours)
Actual process
(In reality: 18 hours)
10
Process Mining
/
event log
discovered
process model
Automated Process
Discovery
Conformance
Checking
Performance
Mining
Enhanced
process model
Business rules /
normative model
Process map with duration overlay
Process performance
dashboards
Performance Mining
Process Mining
/
event log
discovered
process model
Automated Process
Discovery
Conformance
Checking
Variants Analysis
Difference
diagnostics
Performance
Mining
Business rules /
normative model
Enhanced
process model
event log’
Simple
repairs
Complex repairs
Variant Analysis
HSPI, Process Mining: A Database of Applications, 2020
Where is it used?
Uptake by organization size
MarketsandMarkets, Process Analytics Market – Global Forecast to 2023, May 2018
Case 1:
Process mining @ Nordic financial company
• Context: Mid-sized European payment systems provider operating in multiple countries
• Goal: Analysis of customer onboarding and customer support processes (B2B sales)
• Questions: Why are we performing in terms of customer satisfaction and resolution
times better in some countries and for some customer segments and not for others?
• Data sources: SAP CRM and ServiceNow, centralized via a data warehouse solution
• Timeframe: 8 weeks of data extraction & analysis, continued use aftewards
Positive deviance
Practices prevalent in best-performing
countries. For example, we found that
performing some activities earlier in the
process lead to better customer feedback.
Negative deviance
Practices associated with poor customer
feedback. For example, certain rework
loops caused by incorrect data collection
(for a type of customer) lead to delays.
Outcomes (after ca. 6 months)
• Process changes leading to reductions in customer onboarding time
of several days in lower-performing countries
• Changes leading to reductions in rework loops, increase in NPS
• Analysts are able to perform regular review of the process in days,
instead weeks (more than 3 x speed-up)
Case 1:
Process mining @ Nordic financial company
Case 2:
Process mining @ Australian pension fund
Identifying Inefficiencies in the
Claims Process
Identifying Complexity of the Claims
Process
Augmenting the Speed to Analysis
Proactive Conformance/Compliance
Analysis
Variant Analysis
Showcase the Flexibility of the Tool
Model the new Claims Process
Validate the Cost to Serve
Case 2:
Process mining @ Australian pension fund
82% of all pension claims cases were following the
“happy path”, i.e. were compliant with the process model
(straight-through processing).
But the 82% only accounted for 2 out of the total of 474
case variants, suggesting there were various non-
compliant cases.
Case 2:
Process mining @ Australian pension fund
The remaining 18% of cases had various errors, rework
loops, or were withdrawn at different stages of the
pension claims process.
Case 2:
Process mining @ Australian pension fund
Significant expected
ROI
 Estimated annual ROI
of over AUD
 Cost savings of AUD
150K during
project phase
Increased Speed to
Process Improvement
 2.6 times
faster compared
to traditional PI
approaches
Increased Process
Efficiency
 Expected process
efficiency gains
between 5-30%
1 2 3
Case 3:
Process mining @ Cineca
Fast facts
• One of the major EU computational centers
• 600 IT staff
• 2000+ requests per month
Processes analyzed
• Change request process
• Help request process
• Fault handling process
• Variants per geographical region and university
served
• 10 months of data
Case 3:
Process mining @ Cineca
Adoption of positive
deviances
• Replicating behavior of
top performing offices
• Training of IT service operators
Changes in ticket management
system
• Identified reasons for some
types of bounce-backs between
teams
• Alerts for real-time monitoring of
certain state changes
HSPI, Process Mining: A Database of Applications, 2020
Process Mining is Everywhere!

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Process Mining in Action: Self-service data science for business teams

  • 1. Process Mining in Action Self-service data science for business teams Marlon Dumas Professor of Information Systems @ University of Tartu Co-founder @ Apromore
  • 2. Meet Tom - Customer Excellence Manager @ XYZ - Tom cares about customers, recurrent sales revenue, efficient service delivery, … - Every week, he has different questions: - Why did our churn rate increased last month? - Why did the number of customer complaints keep rising? - Why are our response times so slow even though we have added more staff? - Should I automate parts of my customer service process? Should I buy this brand new chatbot? Should I buy the brand new co- browsing platform I saw last week?
  • 3. Tom’s company has tons of data in their information systems! - Customers’ web site visits - Customers’ orders - Customers’ complaints - The activity of salespeople - The activity of customer service staff - Your shipments, product returns, … - etc.
  • 4. How to use these data? 4 https://www.youtube.com/watch?v=z9b9ZeU5aac • Nice option, very useful to address specific challenges • …but will you keep calling on them every day you need an insight? Hire a data science consultancy that delivers successfully on 85% of its projects • Possible in larger companies, but how much time it takes them to gain your domain & business knowledge? Hire a data scientist or data science team • Is it possible? Set up a self-service-system that allows you to analyze the data yourself.
  • 5. Digital Footprint Every process leaves digital footprints (transactions) Data Preparation Data is prepared (e.g. multiple datasets are merged) Event Collection Transactional data is collected from enterprise systems and other sources Process Analysis Process mining algorithms are used to extract process models and other process analytics. Step 01 Step 02 Step 03 Step 04 Process Mining: Self-Service Data-Driven Process Analysis
  • 7. Process Mining event log discovered process model Automated Process Discovery
  • 8. Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility CID Task Time Stamp … 13219 Enter Loan Application 2007-11-09 T 11:20:10 - 13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 - 13220 Enter Loan Application 2007-11-09 T 11:22:40 - 13219 Compute Installments 2007-11-09 T 11:22:45 - 13219 Notify Eligibility 2007-11-09 T 11:23:00 - 13219 Approve Simple Application 2007-11-09 T 11:24:30 - 13220 Compute Installements 2007-11-09 T 11:24:35 - … … … … Process Map (directly follows graph) BPMN process model Automated Process Discovery 8
  • 9. Process Mining / event log discovered process model Automated Process Discovery Conformance Checking Business rules / normative model
  • 10. END-TO-END PROCESS IMPROVEMENT Copyright 2020, Apromore Pty Ltd Copyright 2020, Apromore Pty Ltd Conformance checking PROCESS MINING 101 Modeled process (Expected: 8 hours) Actual process (In reality: 18 hours) 10
  • 11. Process Mining / event log discovered process model Automated Process Discovery Conformance Checking Performance Mining Enhanced process model Business rules / normative model
  • 12. Process map with duration overlay Process performance dashboards Performance Mining
  • 13. Process Mining / event log discovered process model Automated Process Discovery Conformance Checking Variants Analysis Difference diagnostics Performance Mining Business rules / normative model Enhanced process model event log’
  • 15. HSPI, Process Mining: A Database of Applications, 2020 Where is it used?
  • 16. Uptake by organization size MarketsandMarkets, Process Analytics Market – Global Forecast to 2023, May 2018
  • 17. Case 1: Process mining @ Nordic financial company • Context: Mid-sized European payment systems provider operating in multiple countries • Goal: Analysis of customer onboarding and customer support processes (B2B sales) • Questions: Why are we performing in terms of customer satisfaction and resolution times better in some countries and for some customer segments and not for others? • Data sources: SAP CRM and ServiceNow, centralized via a data warehouse solution • Timeframe: 8 weeks of data extraction & analysis, continued use aftewards
  • 18. Positive deviance Practices prevalent in best-performing countries. For example, we found that performing some activities earlier in the process lead to better customer feedback. Negative deviance Practices associated with poor customer feedback. For example, certain rework loops caused by incorrect data collection (for a type of customer) lead to delays. Outcomes (after ca. 6 months) • Process changes leading to reductions in customer onboarding time of several days in lower-performing countries • Changes leading to reductions in rework loops, increase in NPS • Analysts are able to perform regular review of the process in days, instead weeks (more than 3 x speed-up) Case 1: Process mining @ Nordic financial company
  • 19. Case 2: Process mining @ Australian pension fund Identifying Inefficiencies in the Claims Process Identifying Complexity of the Claims Process Augmenting the Speed to Analysis Proactive Conformance/Compliance Analysis Variant Analysis Showcase the Flexibility of the Tool Model the new Claims Process Validate the Cost to Serve
  • 20. Case 2: Process mining @ Australian pension fund 82% of all pension claims cases were following the “happy path”, i.e. were compliant with the process model (straight-through processing). But the 82% only accounted for 2 out of the total of 474 case variants, suggesting there were various non- compliant cases.
  • 21. Case 2: Process mining @ Australian pension fund The remaining 18% of cases had various errors, rework loops, or were withdrawn at different stages of the pension claims process.
  • 22. Case 2: Process mining @ Australian pension fund Significant expected ROI  Estimated annual ROI of over AUD  Cost savings of AUD 150K during project phase Increased Speed to Process Improvement  2.6 times faster compared to traditional PI approaches Increased Process Efficiency  Expected process efficiency gains between 5-30% 1 2 3
  • 23. Case 3: Process mining @ Cineca Fast facts • One of the major EU computational centers • 600 IT staff • 2000+ requests per month Processes analyzed • Change request process • Help request process • Fault handling process • Variants per geographical region and university served • 10 months of data
  • 24. Case 3: Process mining @ Cineca Adoption of positive deviances • Replicating behavior of top performing offices • Training of IT service operators Changes in ticket management system • Identified reasons for some types of bounce-backs between teams • Alerts for real-time monitoring of certain state changes
  • 25. HSPI, Process Mining: A Database of Applications, 2020 Process Mining is Everywhere!