Choosing the right process improvement tool for your project. Learn how an experienced engineer decides when simulation is the right tool for his projects, and when it isn't. With the evolution of process improvement software, it can be difficult to decide the right tool for the job. Using something too powerful and complex can be a lengthy and unnecessary process, but underestimating the depth of analysis required and choosing something too simplistic early in a project can result in repeated work later.

1. When should I use
simulation?
Prof. Brian Harrington

2. Introductions
Brittany Hagedorn, MBA,
CSSBB
- SIMUL8’s Healthcare Lead
for North America
- Experienced Six
Sigma Blackbelt and
Healthcare Consultant
- Here to answer your questions at the end

3. Introductions
Brian Harrington, CSSBB
- 20 years in simulation at
Ford Motor Company
- Experienced Six
Sigma Blackbelt and
Simul8 Manufacturing Consultant
- Director of MTN-SIM, a
simulation specialist consulting firm
- Our presenter for today

4. Agenda
•
•
•
•
•
•
•
Manufacturing issues
Different types of simulation
Using Math
Using Excel/Monte Carlo simulation
Using Discrete Event Simulation
Simulation for Six Sigma
Q&A

5. Manufacturing Dilemma
• Any product development process
involves extensive prototyping;
• Yet, costly manufacturing production
systems are typically not prototyped

6. Simulation in Manufacturing
• System Design
• Operational Procedures
• Performance Evaluation

7. System Design
•
•
•
•
•
•
•
Plant Layout
Effects of introducing new equipment
Location and sizing of inventory buffers
Location of inspection stations
Optimal number of carriers, pallets
Resource planning
Protective capacity planning
Biggest Bang for the Dollar!
Contains Operational Procedures &
Performance Metrics.

8. Operational Procedures
• Production Scheduling - Choice of scheduling
and dispatching rules
• Control strategies for material handling
equipment
• Shift patterns and planned downtime
• Impact of product variety and mix
• Inventory Analysis
• Preventative maintenance on equipment
availability
Continuous Improvement

9. Performance Evaluation
• Throughput Analysis (capacity of the
system, identification of bottlenecks); Jobs
per Hour
• Time-in-System Analysis
• Assessment of Work-in-process (WIP)
levels
• Setting performance measure standards;
OEE
If you can measure it, you can manage it!

10. Agenda
•
•
•
•
•
•
•
Manufacturing issues
Different types of simulation
Using Math
Using Excel/Monte Carlo simulation
Using Discrete Event Simulation
Simulation for Six Sigma
Q&A

11. Why Simulation?
•
•
•
•
•
Competition drives the following:
Leaner production environment
Shorter product development cycles
Narrower profit margins
Flexible Manufacturing (1 Facility, 1
Process, Multiple Models)

12. Types of Simulation
• Mathematical Modeling
– e.g. Queuing Theory
• Monte Carlo Simulation
– e.g. Excel based models
• Discrete Event Simulation
– e.g. Using simulation software

13. Simulation Overview
System Model
Deterministic
Stochastic
Queuing
Theory
Static
Dynamic
Static
Differential
equations
Monte
Carlo
Continuous
Discrete
Dynamic
Continuous
Discrete
DES

14. Question Time:
Which of the following Simulation techniques
do you use:
1. Math, Queuing Theory
2. Excel Based, Monte Carlo
3. Discrete Event Simulation
4. None

15. Agenda
•
•
•
•
•
•
•
Manufacturing issues
Different types of simulation
Using Math
Using Excel/Monte Carlo simulation
Using Discrete Event Simulation
Simulation for Six Sigma
Q&A

16. A Queuing System
Input Source
Service Process
Queue
Arrival
Process
Service
Mechanism
Jockeying
Queue
Balking
Reneging
Served Customers
Queue Structure

17. Queuing Concepts
Relationships for M/M/C
1
Po =
C-1
Σ
n=0
(λ/µ)
n!
n
+ (λ/µ)
c!
c
cµ
(
)
cµ - λ
c
Lq =
(λ/µ) (λ µ) Po
(c – 1)! (cµ – λ) 2
λ = mean arrival rate
µ= mean service rate
C = number of parallel servers
ρ = utilization
These are messy to calculate by
hand, but are very easy with
appropriate software or a table.

18. Queuing Concepts
A Comparison of Single Server Models
2
M/G/1 L =
q
M/D/1 L q =
M/M/1 L =
q
λ σ
2
2
+ (λ/µ)
2(1 - λ/µ)
(λ/µ)
2
2(1 - λ/µ)
2
(λ/µ)
(1 - λ/µ)
Note that
M/D/1 is
½ of M/M/1

19. Benefits & Common Uses
Proven mathematical models of queuing behavior;
the underlying framework of more comprehensive
models.
• Computer Networks – data buffering before
loss of data transmission
• Healthcare – optimizing staffing levels
according to patient arrivals
• Traffic & Parking lots – Traffic lights, toll booths
• Service Industry – Number of servers, checkouts, lanes, ATM machines, etc.

20. Limitations on Queuing Models
• What if:
– we don’t have one of these basic models?
– we have a complex system that has segments
of these basic models and has other
segments that do not conform to these basic
models?
• Then – simulate!

21. Excel Based Simulations
• Uses Data Table functions
• Each Row might be one iteration of a simulation
• Each Col is a random variable generated in the
simulation
• RAND(), VLOOKUP(), COUNTIF(), NORMINV()
• Calculation & Iteration
• >>> Using VBA to bring in Probability functions

22. Monte Carlo Simulation
• Named after the gaming tables of Monte Carlo
• Also referred to as a Static Simulation Model in
that it is a representation of a system at a
particular point in time
• In contrast, a Dynamic Simulation is a
representation of a system as it evolves over
time
• Might be accomplished using Excel and the
Random()

23. Monte Carlo Simulation
A Simple Example
Day
RN
Demand Units
Sold
Units
Unsold
Units
Short
Sale
s
Rev
Return
s
Rev
Unit
Cost
Good
Will
Profit
$
1
10
16
16
2
0
4.80
0.16
2.70
0.00
2.26
2
22
16
16
2
0
4.80
0.16
2.70
0.00
2.26
3
24
17
17
1
0
5.10
0.08
2.70
0.00
2.48
4
42
17
17
1
0
5.10
0.08
2.70
0.00
2.48
5
37
17
17
1
0
5.10
0.08
2.70
0.00
2.48
6
77
18
18
0
0
5.40
0.00
2.70
0.00
2.70
7
99
20
18
0
2
5.40
0.00
2.70
0.14
2.56
8
96
20
18
0
2
5.40
0.00
2.70
0.14
2.56
9
89
19
18
0
1
5.40
0.00
2.70
0.07
2.63
10
85
19
18
0
1
5.40
0.00
2.70
0.07
2.63
Avg
2.50
Where do these numbers come from?

24. Benefits & Common Uses
Proven technique that captures random
behavior (at a specific point in time); can go
further than mathematical solutions.
• Business risk assessment
– Demand & Profit
• Sizing of a market place
– Consumption rate
• Project schedules (best case, worst case)

25. Limitations & Disadvantages
• Stochastic, but static! Usually the time
evolution of a manufacturing system is
significant!
• Excel based models, soon start to use
VBA, and become very complicated
• Might require 1000’s of iterations; Data
Tables become slow
• Difficult to communicate results to
management.

26. Agenda
•
•
•
•
•
•
•
Manufacturing issues
Different types of simulation
Using Math
Using Excel/Monte Carlo simulation
Using Discrete Event Simulation
Simulation for Six Sigma
Q&A

27. Benefits of using DES Simulation
• Mathematical & Excel based models only go so
far
• Less difficult than mathematical methods
• Adds lot of “realism” to the model. Easy to
communicate to end users and decision makers
• Time compression
• Easy to “scale” the system and study the effects
• User involvement results in a sense of
“ownership” and facilitates implementation
Sim Tree

28. Manufacturing Models
• The element that the system evolves over time
is important
• Contain several complicated queuing systems
• Internal process steps are significant to achieve
the desired result
• Conditional build signals (Batch, In-Sequence)
• Several sources of stochastic
behavior
• Contain several shared
resources and conditional
decisions

29. Manufacturing Plant Example

30. Plant Example cont…
How do you simulate
an entire plant?

31. DES Building Blocks
The 8 Core Building Blocks: Start Point, Queue, Activity, Conveyor,
Resource, and End Point. Then the Logical aspect Labels & Conditional
Statements.

32. 8 is all you Need
1. Work Item Types: Can represent parts,
carriers, signals, phone calls, just about
anything that requires a “Label Profile”.
2. Activities: Work Centers, machines, tasks,
process steps, anything that requires a “Cycle
Time”.
3. Storage Areas: Buffers, de-couplers, banks,
magazines, anything that requires a finite space
to occupy over time.
4. Conveyors: Moving parts from pt A to pt B;
Number of parts & Speed of conveyor.

33. …8 is all you Need…
5. Resources: Manpower, crews, forklifts, tugs;
anything that require a certain resource to be
present.
6. End Pt: Keep track of statistics and free
memory!
7. Labels: The attributes of a Work Item.
8. Visual Logic: The ability to create conditional
statements; variables, loops, commands &
functions.

34. Question Time…
How do you use 6-Sigma techniques within
your current role?
1. I don’t use 6-Sigma
2. I use 6-Sigma on specific types of
projects
3. I use 6-Sigma on all my projects
4. I use an integrated toolset which includes
6-Sigma

35. Agenda
•
•
•
•
•
•
•
Manufacturing issues
Different types of simulation
Using Math
Using Excel/Monte Carlo simulation
Using Discrete Event Simulation
Simulation for Six Sigma
Q&A

36. Less is More using 6-Sigma
DES Steps:
• Objective, Assumptions, Data Collection, Build Model,
Verify, Validate, Experimentation, Results
DMAIC or DMADV steps:
• Define, Measure, Analyze, Improve, Control
• Define, Measure, Analyze, Design, Verify
Very similar steps!

37. Y=f(x’s) Transfer Function
Six Sigma focuses on Key Input Factors (x’s) to deliver
your Response.
All of the x’s can be measured & controlled to increase
accuracy & precision of hitting your Target (Y).
Trivial Many (N’s)
Inputs (N’s & X’s)
System/Process
Vital Few (X’s)
Output (Y)

38. The P-Diagram
The P-Diagram not only helps engineers to define the Key Parameters for
a robust design, but also acts as an excellent communication tool for
team reviews.

39. Leverage Statistical Distributions!
• Curve fit your data! Instead of using lengthy
spreadsheets.
• Black-box; entire segments of the model can be
collapsed using distributions.
• If using empirical datasets, drop them into a
“Probability Profile Distribution”

40. Graph your Data!
One of the most basic steps in 6-Sigma; Exploit your data!
Stat-Fit for
SIMUL8

41. Use Known Distributions
The data collection phase of modeling can be the
lengthiest and most time consuming.
Downtime (MTBF & MTTR); such as Exponential &
Erlang respectively.
Cycle times often use a Fixed distribution; that is the
“Design Cycle Time”.

42. Steady State
A common data collection error is to capture all
data points, and attempt to force them into one
distribution.
– Filter out the outliers; usually catastrophic points
are outside the scope of the steady state system.
42

43. Concluding Thoughts
• Queuing Theory & Monte Carlo Simulations can meet
your specific objectives in certain applications. Yet, can
become overwhelming when pulling them beyond their
intent.
• Most Manufacturing, Healthcare objectives go much
further beyond these capabilities. Where the dynamic
aspects of time are critical!
• Discrete Event Simulation is a user friendly tool that is
built on the foundations of queuing theory & statistical
sampling.

44. Q&A