1) The document discusses business process management and how conceptual modeling and process mining can help understand and improve digital enterprises. 2) Process mining techniques like process discovery from event logs, decision mining, and social network mining can provide insights into how processes are executed in reality. 3) Replay techniques can enhance process models with timing information and detect deviations to help align actual behaviors with expected behaviors.

1. Processes and Organizations
A look behind the paper wall
Marco Montali :: Free University of Bozen-Bolzano
Fit for Digital, 23/10/2019

2. Business Process Management

3. Business Process Management

4. digital
enterprise

5. model-driven
digital
enterprise

6. Conceptual modeling

7. Conceptual modeling

8. Understand, share, explain
Semantic DMN 17
Cer. Exp.
(date)
Length
(m)
Draft
(m)
Capacity
(TEU)
Cargo
(mg/cm2)
0 0 0 0 0
Enter
y, n
Ship Clearance
 today
> today < 260 < 10 < 1000
> today < 260 < 10 1000
> today < 260 [10,12] < 4000  0.75
> today < 260 [10,12] < 4000 > 0.75
> today [260,320) (10,13] < 6000  0.5
> today [260,320) (10,13] < 6000 > 0.5
> today [320,400) 13 > 4000  0.25
> today [320,400) 13 > 4000 > 0.25
n
y
n
y
n
y
n
y
n
Table 1: DMN representation of the ship clearance decision of Figure 1b
Enter Length
(m)
Cargo
(mg/cm2)
y,n 0 0
Refuel Area
none, indoor, outdoor
U
Refuel area determination
n
y  350
y > 350  0.3
y > 350 > 0.3
none
indoor
indoor
outdoor
1
2
3
4
Ship
id-code
name
Certificate
exp-date
Harbor
location
Attempt
when
outcome
tried entering into
owns
1
0..1
* *
receive
entrance request
record
ship info
inspect ship
ship id
acquire
certificate
record
cargo
residuals
record
exp. date
cargo residuals
certificate exp. date
decice
clearance
enter
refuel area
enter?
send
refusal
send
fuel area info
open
dock
N
Y
ship type (short name)

9. Understand, share, explain
receive
entrance request
record
ship info
inspect ship
ship id
acquire
certificate
record
cargo
residuals
record
exp. date
cargo residuals
certificate exp. date
decice
clearance
enter
refuel area
enter?
send
refusal
send
fuel area info
open
dock
N
Y
ship type (short name)
Semantic DMN 17
Cer. Exp.
(date)
Length
(m)
Draft
(m)
Capacity
(TEU)
Cargo
(mg/cm2)
0 0 0 0 0
Enter
y, n
Ship Clearance
 today
> today < 260 < 10 < 1000
> today < 260 < 10 1000
> today < 260 [10,12] < 4000  0.75
> today < 260 [10,12] < 4000 > 0.75
> today [260,320) (10,13] < 6000  0.5
> today [260,320) (10,13] < 6000 > 0.5
> today [320,400) 13 > 4000  0.25
> today [320,400) 13 > 4000 > 0.25
n
y
n
y
n
y
n
y
n
Table 1: DMN representation of the ship clearance decision of Figure 1b
Enter Length
(m)
Cargo
(mg/cm2)
y,n 0 0
Refuel Area
none, indoor, outdoor
U
Refuel area determination
n
y  350
y > 350  0.3
y > 350 > 0.3
none
indoor
indoor
outdoor
1
2
3
4
Ship
id-code
name
Certificate
exp-date
Harbor
location
Attempt
when
outcome
tried entering into
owns
1
0..1
* *
internal members
(management, IT, domain experts)
external stakeholders
(customers, citizens, auditors)
digital agents
(applications, robots, …)

10. Machines can use models…
receive
entrance request
record
ship info
inspect ship
ship id
acquire
certificate
record
cargo
residuals
record
exp. date
cargo residuals
certificate exp. date
decice
clearance
enter
refuel area
enter?
send
refusal
send
fuel area info
open
dock
N
Y
ship type (short name)
for automated
analysis
for execution
support
…

11. Do models reflect reality?
(Does management understand what is going on?)

12. Do models reflect reality?
(Does management understand what is going on?)

13. Do models reflect reality?
(Does management understand what is going on?)

14. data-driven
digital
enterprise

15. Going digital

16. event log
Going digital

17. Going digital in reality
event log
completeness
quality
integration

18. A sea of event data tracing the
evolution of process instances…

19. … to compute indicators

20. … to compute indicators
Why is reality so?

21. model-driven
data-driven
digital
enterprise

22. Process Mininging events to model
ntial for process mining
process model
Replay:
elemen
event log
Play-In
Play-Out
insights

23. Replay: Connecting events to
elements is essential for proc
event log process model
Play-In
event logprocess model
Play-Out
Replay
• extended model
showing times,
elements is essential for pro
event log process model
Play-In
event logprocess model
Play-Out
Replay
• extended model
showing times,
frequencies, etc.
• diagnostics
• predictions
event log process model
Play-In
event logprocess model
Play-Out
event log process model
Replay
• extended model
showing times,
frequencies, etc.
• diagnostics
• predictions
• recommendations
Play in
Play out
Replay
Process Mining

24. Play in: process discovery
Executed process

25. Dutch Municipalities28 Ube van der Ham
[van der Ham, BPI Challenge 2015]

26. Dutch Municipalities28 Ube van der Ham
Although the processes are centrally designed, several differences are visible that
make the difference in speed and objections very clear. We will focus on MuniC
versus MuniB and MuniD, because the difference in performance is most obvious.
Fig. 5 shows the control flow for the three municipalities in Disco.
[van der Ham, BPI Challenge 2015]

27. Play in: not just control-flow…
Decision mining: how do people route process instances?

28. Play in: not just control-flow…
Social network mining: “real” organizational structure (handover of work)

29. Play out: simulation

30. Replay: enhancement
Although the processes are centrally designed, several differences are visible that
make the difference in speed and objections very clear. We will focus on MuniC
versus MuniB and MuniD, because the difference in performance is most obvious.
Fig. 5 shows the control flow for the three municipalities in Disco.

31. Replay: enhancement
Although the processes are centrally designed, several differences are visible that
make the difference in speed and objections very clear. We will focus on MuniC
versus MuniB and MuniD, because the difference in performance is most obvious.
Fig. 5 shows the control flow for the three municipalities in Disco.
MuniC is somewhat slower.
The still from the animation (Fig. 6) shows not only the bottleneck before BB_5 in
MuniB and MuniD, but also the more regular distributed cases over the rest of the
steps in MuniC.

32. Replay: conformance checking
Detect deviations and align the actual with the expected behaviors

33. Replay: conformance checking

34. Replay: conformance checking

35. Runtime operational support
Predictive monitoring: what will likely happen to my process instance?

36. This is for us to take, and
manage wisely
Not to control… …but to continuously improve!

37. !Thanks!