When Should I use Simulation?

When should I use
simulation?
Prof. Brian Harrington

Agenda
•
•
•
•
•

Common Manufacturing issues
Intro to different types of simulation
Using maths to analyze a Queuing System
Using Excel/Monte Carlo simulation
Using Discrete Event Simulation to look at
system design
• Six Sigma simulations
• A case study.
SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com

Manufacturing Dilemma

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


Simulation in Manufacturing

• System Design
• Operational Procedures
• Performance Evaluation


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.

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


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!

Why Simulation?

•
•
•
•
•

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


Types of Simulation

• Mathematical Modeling
– e.g. Queuing Theory

• Monte Carlo Simulation
– e.g. Excel based models

• Discrete Event Simulation
– e.g. SIMUL8


Simulation Overview
System Model

Deterministic

Stochastic

Queuing
Theory

Static

Dynamic

Static

Differential
equations

Monte
Carlo

Continuous

Discrete

Dynamic

Continuous

Discrete
DES


A Queuing System
Input Source

Service Process

Queue
Arrival
Process

Service
Mechanism

Jockeying

Queue
Balking
Reneging

Served Customers

Queue Structure

Queuing Concepts
Relationships for M/M/C
1

Po =

C-1

S

n=0

(l/m)
n!

n

+ (l/m)
c!

c

cm
(
)
cm - l

c

Lq =

(l/m) (l m) Po
(c – 1)! (cm – l) 2

l = mean arrival rate
m= mean service rate
C = number of parallel servers

These are messy to calculate by
hand, but are very easy with
appropriate software or a table.


Queuing Concepts
A Comparison of Single Server Models
2

M/G/1 L =
q

M/D/1 L q =

M/M/1 L =
q

l s

2

2

+ (l/m)

2(1 - l/m)

(l/m)

2

2(1 - l/m)
2

(l/m)

(1 - l/m)

Note that
M/D/1 is
½ of M/M/1

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!


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


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()

Monte Carlo Simulation
A Simple Example
Day

RN

Deman
d

Units
Sold

Units
Units Sale
Unsold Short s
Rev

Return
s
Rev

Unit Good Profit
Cost Will
$

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 this numbers come from?

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.

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

SIMUL8 Common Building Blocks

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

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.

…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.


Less is More using 6-Sigma

DMAIC or DMADV steps:
• Define, Measure, Analyze, Improve, Control
• Define, Measure, Analyze, Design, Verify

DES Steps:
• Objective, Assumptions, Data Collection, Build Model,
Verify, Validate, Experimentation, Results

Very similar steps!

When Should I use Simulation?

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