The document outlines the process for conducting business process simulations using a defined scenario and analysis techniques to assess performance measures. It discusses the limitations of traditional simulations and the need for improvements, such as incorporating differentiated resources and better handling of waiting times. Additionally, it highlights methodologies for enhancing prediction accuracy and reliability in simulation outcomes.

1. AUTO-TWIN Webinar – June 19, 2024

3. Model the
process
Define a
simulation
scenario
Run the
simulation
Analyze the
simulation
outputs
Repeat for
alternative
scenarios
3

4. 4

5. 5
Exp(20m)
Normal(20m, 4m)
Normal(10m, 2m)
Normal(10m, 2m)
Normal(10m, 2m)
0m

6. 6
Arrival rate = 2 applications per hour
Inter-arrival time = 0.5 hour
Negative exponential distribution
From Monday-Friday, 9am-5pm
0.3
0.7
0.3

7. Clerk
Officer
System
Clerk
Officer
Officer

8. D

9. The process model is authoritative (always followed to the letter)
• No deviations, no workarounds
The simulation parameters accurately reflect reality
• …whereas in reality, they are often guesstimates
A resource only works on one task instance at a time / a task is performed by one resource
• No multi-tasking / no multi-resource tasks (teamwork)
Resources have robotic behavior (eager resources consume work items in FIFO mode)
• No batching, no tiredness effects, no interruptions, no distractions beyond “stochastic” ones
Undifferentiated resources
• Every resource in a “resource pool” has the same performance as others
No time-sharing outside the simulated process
• Resources fully dedicated to one process
9

10. End Result
Business process simulations based
on incomplete models,
guesstimates, and simplifying
assumptions are not faithful
 adoption of business process
simulation is disappointing
0

11. Event Log

13. Given
• one or more business processes, for which we
have:
• one or more process specifications and/or
• event logs generated by the execution of the
processes on top of one or more information
systems.
• one or more process performance measures of
interest (e.g. cycle time, resource cost)
• One or more changes to the process (interventions)
Predict
• Predict the values of the process performance
measures after the given interventions.

14. Non-Functional Requirements
14
Predictions accurate.
Accuracy may be measured e.g. via an error
between the predicted and the actual
performance measures after intervention.
Predictions should be accompanied by a
reliability estimate. In most cases, the
reliability is high.
Reliability could be captured, e.g. by
confidence intervals

15. Stochastic Process Model Discovery
Event log
Simod
Control-Flow
(BPMN Model)
• Filtering threshold
• Parallelism threshold
Branching
probabilities
Trace alignment +
replay to determine
how many times each
sequence flow is
traversed
Resources & Performance
• Resource pools
• Pool-task assignment
• Resource timetables
• Multi-tasking behavior
• Interarrival distribution
• Activities durations distribution
Simulation
Simulator
Optimizer
Congestion Model Discovery
SplitMiner
Digital
Process
Twin (DPT)

16. event log (testing set)
K simulated event logs
1. Generate K simulated event logs.
2. Compare individually and report the average and confidence
interval.
D. Chapela-Campa, M. Dumas, A. Senderovich, et al. Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models. BPM 2023.

17. Temporal performance: mean percentage error of 15-40%
Huge under-estimation of activity waiting times
Erratic performance with large number of resources!
Erratic performance when multi-tasking present
Poor ability to capture the distribution of activity sequences.
Two directions:
Black-box – bring in deep learning!
White-box – Address limitations one by one, Kaizen style
M. Camargo et al. Automated discovery of business process simulation models from event logs. Decis. Support Syst. 134: 113284 (2020).

18. 4 Clerks
Same Availability
Same Performance
vs Differentiated Availability
vs Differentiated Performance
Senior Clerk
Junior Clerk
Pooled Allocation
vs Unpooled Allocation
5-10%
improvement
O. Lopez-Pintado & M. Dumas Discovery, simulation, and optimization of business processes with differentiated resources. Inf. Syst. 120: 102289 (2024)

19. Branching Probabilities
* Frequency Analysis
Branching Conditions
* Deterministic Expressions
0.3
0.7
0.5
Activity Decision
Make Credit offer denied
Notify Rejection granted
Activity Decision
Make Credit offer granted
Notify Rejection denied
5-10%
improvement

20. Extraneous activity delays: waiting times not explained by available data.
1. Analyze the waiting time previous to each activity (since its enablement).
2.Were the resources available?
10-20%
improvement
D. Chapela-Campa & M. Dumas. Enhancing business process simulation models with extraneous activity delays. Inf. Syst. 122: 102346 (2024)

21. 21
08:00 12:00 13:00 17:00
Monday
08:00 12:00 13:00 17:00
Friday
30% 50% 90% 100% 100% 100% 100% 100%
08:00 12:00 13:00 17:00
Monday
90% 90% 90% 70% 50% 50% 5% 5%
08:00 12:00 13:00 17:00
Friday
20-40%
improvement

22. 5-20%
improvement

23. https://tinyurl.com/autoDPT