Persentation to NoMagic World 2013

1. Business Process Simulation:
How to get value out of it
Denis Gagné,
www.BusinessProcessIncubator.com
Chair BPMN MIWG at OMG
BPMN 2.0 FTF Member at OMG
BPMN 2.1 RTF Member at OMG
CMMN Submission at OMG
Chair BPSWG at WfMC
XPDL Co-Editor at WfMC

2. Abstract
Business Process Simulation can be an effective tool when looking for optimal performance
from a Business Process Model. Although considered quite relevant and applicable in the
context of Business Process Management (BPM), Business Process Simulation is not current
practice for -and even seldom used by- Business Analyst in the course of process analysis.
In this presentation we will explore why this may be the case and will discuss how to use
Business Process Simulation efficiently while identifying some of the pitfalls along the way.
Various Business Process Simulation approaches, their benefits and applicability will be
introduced. The session will conclude with a quick overview of a new Business Process
Simulation standard that is emerging within the industry.

3. Poor Performing Processes
May lead to:
Delays
Back log
Refund Claims
Angry customers
Lost of goodwill (Mission Critical)
Lost of lives (Life Critical)
Gain Insight: Thoroughly analyse business
process in a safe isolated environment prior to
Deploying

4. Simulation for Process Analysis
Provides a priori Insight
Can be Effective Process Analysis tool for:
Alternative Evaluation
Decision Support
Performance Prediction
Optimization

5. Benefits of Simulation
Advantages of simulation over testing on the
real world include:
Lower relative cost of business
transformation explorations
Speed of validation of potential scenarios
No disturbance to current operations

6. Simulation for Business Processes
Visual Depiction (Visualization & Animation)
User is presented with a (sometime interactive) animated
depiction of the business process model
Numeric Simulation (Discreet Events)
User asked to provide values for input and decision
parameters of a simulated business model
Role Play (Serious Gaming)
User asked to take actions and make decisions within a
simulated business environment

7. Types of Process Analysis
using Simulation
Structural Analysis
The structural aspects (configuration) of a process model
Usually Statistical Analysis (using static methods)
Capacity Analysis
The capacity aspects of a process model
Usually Dynamic Analysis (using discreet simulation
methods)

8. When is Numeric Simulation
most Appropriate
Capacity analysis of processes that potentially are
Highly Variable
Variability makes outcomes difficult if not impossible to predict
Interdependent
Changes in one process affect other processes
Complex
Complex structure or complex behavior
Capacity Constraints
Hard resources constraints (as independent variables)

9. When is Numeric Simulation
Less (Not) Appropriate
When an expedited analysis indicates a negligible problem
When there is little or no variability or uncertainty
When the consequences of poor estimates are acceptable
When the cost of intervention is less than the cost of the
analysis experiment

10. Process Simulation Other Uses
Training & Learning
Although very popular in support of operations, limited use in other Business
disciplines
Persuasion & Selling
Simulating results of a proposed solution
Cause and Effect simulation
Verification & Validation
Validation: Are we doing the right “thing”?
Verification: Are we doing “it” right?

11. Process Simulation not yet
Common Practice: Why?
Potential Reasons:
Availability
Limitation of existing BPMS Tooling
Lack of Training or Expertise
Lack of Standards

12. Optimization
Selection of a best scenario (with regard to some criteria)
from some set of available alternatives
Almost impossible without tool support
Sub optimization caveat
Optimizing the outcome for a subsystem will in general not
optimize the outcome for the system as a whole.

13. Process Improvement Project
Best Practices
Defining Success
“Can’t get there if you do not know where you are going”
Why are we conducting this project and what are the
objectives
Stakeholder Analysis
“When it comes to assessing success your own opinion while
interesting is irrelevant”
How do your stakeholders define success
While it is obvious that satisfying the most important
stakeholder is necessary, it is rarely sufficient. Do not ignore
other stakeholders

14. Process Improvement Project
using Simulation
Get the Goal Right
Clearly define the goal or problem to be investigated using
simulation
Clearly state the objectives of the simulation investigation
Match Expertise to Desired Experimentation
Different levels of Investigation Complexity
Get the Model Right
Model Granularity
Model Parameterization

15. Clearly Define the Goal
Intentions Examples
Reduce headcounts or expenses
Improve process predictability or reliability
Increase throughput
Increase output
Ensure SLA
Design the Experiment
Independent vs dependent variables
Same process model under different parameterisations
Different process models under same parameterization
Number of distinct model settings to be run
The experiment should provide insight
The experiment should help inform a decision
The experiment should be in response to clearly defined objectives
that are relevant to a decision

16. Clearly Define the Objectives
Provide SMART Objectives
Specific
Usually answer the five "W" questions
Measurable
Aiming for quantifiable, concrete results
Achievable
While an attainable goal may stretch a team in order to achieve
it, the goal is not extreme
Relevant
To your boss, your organization, your stakeholders
Time Bound
Within a time frame, with a target date
Be mindful of the Optimization Conundrum

17. Optimization Conundrum
Quality
Time Satisfaction
Lean
Cost

18. Expertise vs Experimentation
Expert Verify Process
Structure and logic
Optimization
Process Modeling
Novice Learning via Quantitative
Experimentations Analysis
Novice Expert
Simulation

19. Model Granularity
Pick the right level of process model abstraction
e.g. What is an atomic task
For example a certain level of details may suitable to
compare relative throughput of alternative process designs
while not be detailed enough to provide reliable prediction
of actual throughput

20. Model Input Parameterization
Setting Input parameters for process model elements to
reflect external stimulation
e.g. Arrival Patterns
Opportunity to introduce event variability into the process
model
Select Candidate Probability
Assess Fidelity
Can easily be the cause of misleading results
“Garbage in garbage out”

21. Select Candidate Probability
Based on the external observed behavior
Is it Constant or Random
Select a distribution that best captures
characteristics, observations, or available data
Some distribution are better fits to specific situations
(e.g. Poisson for mutually independent arrivals)
Using available historical or event log data as reference may require data cleansing e.g.
minimum task time =8 mins
Mode task time = 32 mins
Maximum task time = 9.5 hours
May not notice that maximum task time includes an 8 hour off shift

22. Assess Fidelity
Check how well your input parameterization reflect the
observed behavior
Model behave as desired or expected, or
Model behavior reflects “As Is” situation
Carry out Sensitivity Testing
Determine how sensitive your model is to different input
parameters
Check sensitivity in magnitude (e.g. mean) and variability
(e.g. range)

23. Simulation is often a process of discovery
Examine output results
Unexpected result are not necessarily a problem
Primary reason for your simulation experimentation
Need to find an explanation
Will provide enlightenment of actual process behavior
vs assumed process behavior
Unexplainable results are a problem

24. When Examining Results
When randomness is introduced replications should be
used
Replication = same scenario but with different sequences of
random variables
e.g. repeated coin toss
Warm up periods may be required
Reflect the notion of work in progress (WIP)
Time during which results are either not collected, or which
can be separated off from the main results collection period
e.g. A bank (opens empty and idle each day) model does not require warm-up (and
indeed should not have warm-up). Common examples of situations requiring warm-
up are manufacturing in general, hospital emergency rooms, 24-hour telephone
exchanges, etc

25. Demo
Randomness and likelihood

26. Business Process Simulation Working Group
BPSWG
www.BPSim.org

27. Why BPSim
Encourage wider adoption of simulation within BPM
community through a standards led approach
Process simulation is a valuable technique to support process
design, reduce risk of change and improve efficiency in the
organisation
Provide a framework for the specification of simulation
scenario data and results as a firm foundation for
implementation
Open interchange of simulation scenario data between
modeling tool, simulator, results analysis/presentation tool

28. BPSim Scope
Complements existing process modeling standards
“Not Reinvent the Wheel”

29. BPSim Element Parameters
Each element parameter of a scenario references a specific element of
a process within the business process model
Each element of the business process model may be parameterized
with zero or multiple element parameters
Perspectives
 TimeParameters
 ControlParameters
P  ResourceParameters
 CostParameters
 InstanceParameters
 PriorityParameters

30. Demo

31. Business Process Simulation
Best Practices
The Right Model for the Right Goal
Align Modeling Objectives with Simulation Objectives
Abstraction
Fidelity
Validity (soundness and completeness)
The Right Answer to the Right Question
Make sure to instrument your business process model with parameters that
are actual indicators (influencers) of what you wish to explore
The Right Expert for the Right Task
Although conceptually simple to grasp, successfully (meaningfully) using
numerical simulation for business modeling still requires some expertise
(Advanced Mathematical Skills)

32. Business Process Simulation
Caveats
Unrealistic User Expectations
Simple Press-Button Simulation
Deterministic behavior assumptions
A Business Process Model is a Simulation Model (not necessarily)
Their goals (purposes) may be misaligned
Be Mindful of Misleading Results (Garbage in Garbage Out)
A simulation model that is fidel &valid with uncharacteristic data can lead to incorrect
conclusions or predictions, Negative Training, …
Sub-Optimization
Partial or sub-model optimization can lead you astray

33. Discussions & Questions
www.BPSim.org
dgagne@trisotech.com