The document discusses the concepts and applications of process mining, emphasizing its role in bridging traditional business process management (BPM) with real operational data. It outlines techniques for discovering actual processes, checking compliance, identifying bottlenecks, and predicting outcomes while highlighting the integration of robotic process automation (RPA) with process mining methodologies. Various examples and challenges in technology transfer and data utilization in process mining are also presented.

1. © Wil van der Aalst (use only with permission & acknowledgements)
prof.dr.ir. Wil van der Aalst
RWTH Aachen University
W: vdaalst.com T:@wvdaalst
Process Mining
BPM on Steroids
CPOs@BPM&O 2019
7-3-2019 Flora Köln

2. Miguel Valdes
CEO and co-founder of BonitaSoft at BPM 2017

3. © Wil van der Aalst (use only with permission & acknowledgements)
Traditional BPM: Wallpaper models and problematic implementations
slow
disconnected
expensive

4. “BPM on Steroids”
Process mining provides a bridge between models and reality

5. © Wil van der Aalst (use only with permission & acknowledgements)
process
data
processmining

6. © Wil van der Aalst (use only with permission & acknowledgements)
model
reality
processmining

7. © Wil van der Aalst (use only with permission & acknowledgements)
IT
processmining

8. © Wil van der Aalst (use only with permission & acknowledgements)
machine
learning
data
mining
algorithms
statistics privacy,
security,
law &
ethics
behavioral
/social
science
business
models &
marketing
visualization
& visual
analytics
distributed
systems
databases
predictive
analytics
stochastics
operations
manage-
ment &
research
business
process
management
process
automation
&
optimi-
zation
formal methods
& concurrency
theory
business
process
improve-
ment
workflow
manage-
ment
data
science
process
science
process
mining

9. www.pads.rwth-aachen.de
Data Science, Process Science, Process Mining, Business Process Management, Data Mining, Process
Discovery, Conformance Checking, Simulation, and Responsible Data Science.
Foundations
of Process
Mining
Dealing
with XXXX
Event Data
Automated
Operational
Process
Improvement
Responsible
Process
Mining

10. © Wil van der Aalst (use only with permission & acknowledgements)
A Personal Journey (between process and data)
• Petri nets (1988-now)
• WFM systems (1995-2005)
• BPM (2000-now)
• Process Mining (1999-now)
• Director DSC/e (2013-2018)
• AvH-RWTH-FIT(2018-now)

11. © Wil van der Aalst (use only with permission & acknowledgements)
A Personal Journey (not just paper)
Woflan
over 100.000 participants in the first process mining course

12. © Wil van der Aalst (use only with permission & acknowledgements)© Wil van der Aalst (use only with permission & acknowledgements)
What is Process
Mining?

13. © Wil van der Aalst (RWTH Aachen University)
12
start from raw data
csv / excel file with
2 × 80,609 events (= rows)
about
12,666 cases (= orders)
referring to
8 unique activities

14. © Wil van der Aalst (RWTH Aachen University)
13
Let’s focus on the events of order 9012

15. © Wil van der Aalst (RWTH Aachen University)
14
12,666 cases (orders)
80,609 events (only
using completes)
discovered
using ILP miner

16. © Wil van der Aalst (RWTH Aachen University)
15
“happy” “freq” “time”

17. © Wil van der Aalst (RWTH Aachen University)
16
process model discovered using
the inductive miner (showing
only the most frequent paths)

18. © Wil van der Aalst (RWTH Aachen University)
17
zooming in

19. © Wil van der Aalst (RWTH Aachen University)
18
using conformance checking
to see all deviations
happened in
reality but not
allowed by the
model
required by the
model but did not
happen

20. © Wil van der Aalst (RWTH Aachen University)
19
bottleneck analysis: enriching
the model with performance
information

21. © Wil van der Aalst (RWTH Aachen University)
20
animating the event log
showing real cases

22. seamless abstraction
► one log many possible views

23. © Wil van der Aalst (RWTH Aachen University)
22
create purchase
requisition
create purchase
order
approve purchase
order
receive order
confirmation
receive goods receive invoice
pay invoice
Purchase-to-Pay
• Simple process found in
almost any organization.
• Data available in e.g. SAP.
• Most cases follow the so-
called “happy path”.
• 80/20 rule applies.

24. © Wil van der Aalst (RWTH Aachen University)
23
Real process may look like this:
700,000 cases may exhibit 7,000 unique variants

25. © Wil van der Aalst (RWTH Aachen University)
24
Price changes
• One of the many variations.
• Changing prices result in
lots of extra work and
significant delays.
create purchase
requisition
create purchase
order
approve purchase
order
receive order
confirmation
receive goods receive invoice
pay invoice
price change
8% of cases
adds (on average)
a delay of 4.5 days

26. © Wil van der Aalst (RWTH Aachen University)
25
Pay before receipt
• Goods are paid before they
have been received.
• Goods arrived too late or
not at all.
• May indicate fraud.
create purchase
requisition
create purchase
order
approve purchase
order
receive order
confirmation
receive goods receive invoice
pay invoice
2% of cases
goods are paid but
never received

27. © Wil van der Aalst (RWTH Aachen University)
26
Two additional
variations
• Orders created without
requisition.
• Rejected orders
generating rework.
 7000-4 = 6996 variants to go …
 Can be sorted based on
frequency or impact.
create purchase
requisition
create purchase
order
approve purchase
order
receive order
confirmation
receive goods receive invoice
pay invoice
rejected purchase
order
modify purchase
order
6% of cases
purchase order
created without
requisition
3% of cases
purchase order is
rejected resulting in
rework and a delay of
on average 8.2 days

28. © Wil van der Aalst (RWTH Aachen University)
27
Performance
problems
• Delays inside the
process.
• Excessive flow
times.
• Not meeting Service
Level Agreements
(SLAs).
create purchase
requisition
create purchase
order
approve purchase
order
receive order
confirmation
receive goods receive invoice
pay invoice
!
!
It takes to long to pay
invoices resulting in
complaints and fines
(15% more than 3
weeks).
The approval of
purchase orders takes
too long (28% more
than 10 days).
Drill down to event
data and uncover the
root causes.

29. © Wil van der Aalst (RWTH Aachen University)
28
Compliance problems
Activities may be:
• skipped,
• done too early or too late,
• done by the wrong person,
• should not have happened
at all.
create purchase
requisition
create purchase
order
approve purchase
order
receive order
confirmation
receive goods receive invoice
pay invoice
! Orders are created
without a purchase
requisition.
!Invoices are paid
before the goods
arrive.
Drill down to event
data and uncover the
root causes.

30. © Wil van der Aalst (RWTH Aachen University)
Using process mining, one can …

31. © Wil van der Aalst (RWTH Aachen University)
discover the real processes

32. © Wil van der Aalst (RWTH Aachen University)
check compliance

33. © Wil van der Aalst (RWTH Aachen University)
uncover and quantify deviations

34. © Wil van der Aalst (RWTH Aachen University)
find root causes for process variations

35. © Wil van der Aalst (RWTH Aachen University)
find bottlenecks

36. © Wil van der Aalst (RWTH Aachen University)
find root causes for delays

37. © Wil van der Aalst (RWTH Aachen University)
predict process outcomes

38. © Wil van der Aalst (RWTH Aachen University)
foresee deviations and bottlenecks

39. © Wil van der Aalst (RWTH Aachen University)
PM is great!
How to start?
What will happen next?

40. © Wil van der Aalst (use only with permission & acknowledgements)© Wil van der Aalst (use only with permission & acknowledgements)
Technology transfer

41. © Wil van der Aalst (RWTH Aachen University)
Applications: Always about compliance and performance
• Processes supported by ERP and CRM systems (e.g., SAP).
• Healthcare (range of hospitals).
• Logistics and production (e.g., with Vanderlande).
• E-learning (e.g., based on Coursera).
• E-government (see CoSeLog project).
• Smart homes / quantified self (with Philips).
• High-tech systems.
• Auditing.
• Fraud detection.
• Etc.

42. © Wil van der Aalst (RWTH Aachen University)
Technology transfer
challenges
ideas
new techniques
and approaches
data

43. © Wil van der Aalst (RWTH Aachen University)
Tooling
• ProM is the de facto standard in the scientific world.
• Ideas initially developed in ProM have been adopted by commercial
vendors.
• Currently, more than 25 commercial vendors offering process mining
software (Celonis, Fluxicon, ProcessGold, QPR, etc.).
>1500 plug-ins

44. © Wil van der Aalst (RWTH Aachen University)
1500 plug-ins

45. © Wil van der Aalst (RWTH Aachen University)
Usability

46. © Wil van der Aalst (RWTH Aachen University)
Technology transfer (e.g., Celonis)
[2012] process discovery
inspired by heuristic miner
(2002) and fuzzy miner
(2006)
[2018] process discovery
based on the inductive miner
(2013)
[2013] token
animation and
sliders (2006)
[2017] token-based
conformance checking
(2005)
[2017] process-
based root cause
analysis (2006)
10
5
11
12
7

47. © Wil van der Aalst (RWTH Aachen University)
Celonis was the first to provide “forward looking” process mining
past present future
actionable
insights

48. © Wil van der Aalst (RWTH Aachen University)
Adoption & Practical Relevance
Identified as a new market
segment by Gartner.
(April 2018)
Celonis gets
Unicorn status
Many large SAP customers are
already using process mining

49. © Wil van der Aalst (use only with permission & acknowledgements)© Wil van der Aalst (use only with permission & acknowledgements)
What is next?

50. Robotic Process
Automation
(RPA) & PM

51. © Wil van der Aalst (use only with permission & acknowledgements)
1 of 17 variants
8% of cases covered

52. © Wil van der Aalst (use only with permission & acknowledgements)
5 of 17 variants
88% of cases covered

53. © Wil van der Aalst (use only with permission & acknowledgements)
17 of 17 variants
100% of cases covered

54. approx. 3000 cases
having 700 variants

55. 208 cases having
203 variants

56. © Wil van der Aalst (use only with permission & acknowledgements)
The goal
case
frequency
(number of similar
cases in a given
period)
different
types of cases
(sorted by
frequency)
many cases follow the
same structured process ,
making automation
economically feasible
there is repetitive work ,
but not frequent enough
to justify automation
Infrequent/exceptional
cases that need to be
handled in an ad-hoc
manner
See W. van der Aalst, et al. (2018). Robotic Process Automation. Business
& Information Systems Engineering: Vol. 60, No. 4. Springer. (69-272).

57. © Wil van der Aalst (use only with permission & acknowledgements)
The goal
case
frequency
(number of similar
cases in a given
period)
different
types of cases
(sorted by
frequency)
traditional
process
automation
work that can
only be done by
humans
?
See W. van der Aalst, et al. (2018). Robotic Process Automation. Business
& Information Systems Engineering: Vol. 60, No. 4. Springer. (69-272).

58. © Wil van der Aalst (use only with permission & acknowledgements)
The goal
case
frequency
(number of similar
cases in a given
period)
different
types of cases
(sorted by
frequency)
traditional
process
automation
Robotic Process Automation
(RPA)
candidates
work that can
only be done by
humans
See W. van der Aalst, et al. (2018). Robotic Process Automation. Business
& Information Systems Engineering: Vol. 60, No. 4. Springer. (69-272).

59. © Wil van der Aalst (use only with permission & acknowledgements)
Process Mining for RPA (PM4RPA)
Initial situation

60. © Wil van der Aalst (use only with permission & acknowledgements)
Process Mining for RPA (PM4RPA)
Traditional workflow automation

61. © Wil van der Aalst (use only with permission & acknowledgements)
Process Mining for RPA (PM4RPA)
Traditional process mining
process mining

62. © Wil van der Aalst (use only with permission & acknowledgements)
Process Mining for RPA (PM4RPA)
process mining
case
frequency
(number of similar
cases in a given
period)
different
types of cases
(sorted by
frequency)
traditional
process
automation
work that can
only be done by
humans
?
What to automate in a traditional manner?
What to support using RPA?
What is best done by people?
RPA-I

63. © Wil van der Aalst (use only with permission & acknowledgements)
Process Mining for RPA (PM4RPA)
process mining
Does the robot behave as expected?
How to distribute the work (static)?
RPA-II

64. © Wil van der Aalst (use only with permission & acknowledgements)
Process Mining for RPA (PM4RPA)
process mining
When and how to dynamically transfer work?
How to actively improve compliance/performance?
How to detect drifts and act upon these?
RPA-III

65. Hybrid Process
Models (HPM)

66. © Wil van der Aalst (use only with permission & acknowledgements)
Informal models: providing insights but not very precise.
Can be simplified but this does not improve precision.

67. Complex and overfitting the data.

68. © Wil van der Aalst (use only with permission & acknowledgements)
100% fitting, all activities
100% fitting, high-frequent activities only

69. © Wil van der Aalst (use only with permission & acknowledgements)
Mainstream behavior (subset of activities and most frequent paths).
Formal models are very precise and provide predefined quality guarantees.

70. © Wil van der Aalst (use only with permission & acknowledgements)
Mainstream behavior (subset of activities and most frequent paths).
Formal models allow for conformance checking.

71. © Wil van der Aalst (use only with permission & acknowledgements)
informal when needed
& fast and scalable
precise (having formal
semantics) whenever
possible & useful
Hybrid process models
Wil van der Aalst et al.
Learning Hybrid Process
Models from Events - Process
Discovery Without Faking
Confidence. BPM 2017: 59-76

72. Comparative
Process Mining
(CPM)

73. © Wil van der Aalst (use only with permission & acknowledgements)
Comparative process mining
(variants of the same process in different organizations)

74. © Wil van der Aalst (use only with permission & acknowledgements)
Comparative process mining
(variants of the same process in the same organization)

75. © Wil van der Aalst (use only with permission & acknowledgements)
Comparative process mining
(variants of the same process for different customer groups)

76. © Wil van der Aalst (use only with permission & acknowledgements)
CoSeLoG
http://www.win.tue.nl/coselog/

77. © Wil van der Aalst (use only with permission & acknowledgements)
UWV (Employee Insurance Agency) manages employee
insurances (unemployment, disabilities, health, etc.).
Elham Ramezani Taghiabadi,
Understanding non-compliance,
PhD thesis 2017.

78. © Wil van der Aalst (use only with permission & acknowledgements)
Also the same process/organization over time

79. © Wil van der Aalst (use only with permission & acknowledgements)
Find the seven differences

80. © Wil van der Aalst (use only with permission & acknowledgements)

81. © Wil van der Aalst (use only with permission & acknowledgements)
fast slow

82. © Wil van der Aalst (use only with permission & acknowledgements)
Approaches
• Process cubes (data warehouse for event data).
• Analysis workflow support.
• Delta analysis (model-to-model).
• Conformance checking (log-to-model).
Wil van der Aalst. Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event
Data for Process Mining. AP-BPM 2013: 1-22.
Alfredo Bolt, Massimiliano de Leoni, Wil van der Aalst. Process variant comparison: Using
event logs to detect differences in behavior and business rules. Inf. Syst. 74(Part): 53-66
(2018).
Alfredo Bolt, Massimiliano de Leoni, Wil M. P. van der Aalst, Pierre Gorissen. Business
Process Reporting Using Process Mining, Analytic Workflows and Process Cubes: A Case
Study in Education. SIMPDA (Revised Selected Papers) 2015: 28-53.

83. Interactive
Process Mining
(IPM)

84. © Wil van der Aalst (use only with permission & acknowledgements)
event data
evidence-based
techniques
domain
knowledge
contextual
knowledge
010010011001000110
010010011001000110

85. © Wil van der Aalst (use only with permission & acknowledgements)
Traditional process modeling
conformance
and
performance
diagnostics
manual modeling followed
by conformance checking
improve model or process

86. © Wil van der Aalst (use only with permission & acknowledgements)
Traditional process discovery
conformance
and
performance
diagnostics
process discovery followed
by conformance checking
improve process

87. © Wil van der Aalst (use only with permission & acknowledgements)
Interactive process mining
Alok Dixit, E. Verbeek, J. Buijs, W. van der Aalst. Interactive Data-
Driven Process Model Construction. ER 2018: 251-265
conformance
and
performance
diagnostics
guided process discovery
and improvement
generates modeling
suggestions based
on the data
autocomplete
possibility
immediate
feedback on any
modeling decision
helps to explore the
“evidence” in a very
direct manner

88. Performance-
Driven Process
Modeling (PDPM)

89. © Wil van der Aalst (use only with permission & acknowledgements)
Performance-Driven Process Modeling
Best model or best process ?

90. © Wil van der Aalst (use only with permission & acknowledgements)
Naïve idea
fast cases slow cases
discover process
model based on
only good cases
remove bad
behavior from
discovered
process

91. © Wil van der Aalst (use only with permission & acknowledgements)
Not that simple
good behaviors
observed behaviors
observed good behavior
danger of overfitting or
underfitting
need to identify
process features that
lead to positive
behavior and that can
be influenced

92. Responsible
Process Mining
(RPM)

93. © Wil van der Aalst (RWTH Aachen University)
With great power comes great responsibility!!
Responsible
Process
Mining
(RPM)

94. © Wil van der Aalst (RWTH Aachen University)
Event data are highly sensitive even when
names are removed!

95. www.responsibledatascience.org
Fairness
Accuracy
Confidentiality
Transparency
Value

96. © Wil van der Aalst (RWTH Aachen University)
Examples of ongoing RPM research
Who the blame for delays and
non-compliance?
(avoiding to blame the most experienced/overloaded worker)
How to discover process
models without having events?
(events are like votes in a e-voting system)

97. © Wil van der Aalst (use only with permission & acknowledgements)
To conclude

98. © Wil van der Aalst (RWTH Aachen University)
VisiCalc Killer App
for Apple II (1979)
Spreadsheets can do anything
with numbers
Excel is like toilet paper …
The more
sheets, the
better!

99. © Wil van der Aalst (RWTH Aachen University)
Process mining can do
anything with event logs!
The bigger the
log, the better.
The more logs,
the better.

100. © Wil van der Aalst (RWTH Aachen University)
Process mining is generic
like a spreadsheet and can
do anything with events
(rather than numbers).
Therefore, it is there to stay
(unlike many hyped topics).

101. International Conference
on Process Mining
Aachen, June 23-25, 2019

102. International Conference
on Process Mining
Aachen, June 23-25, 2019

103. © Wil van der Aalst (use only with permission & acknowledgements)
www.pads.rwth-aachen.de
Foundations
of Process
Mining
Dealing
with XXXX
Event Data
Automated
Operational
Process
Improvement
Responsible
Process
Mining
Thanks