when should i use simulation?

When should I use

simulation?

Prof. Brian Harrington

SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com

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.

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;

If you can measure it, you can manage it!

Agenda

system design

• A case study.

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

Static Dynamic Static Dynamic

Continuous Continuous Discrete Discrete

Monte Carlo

Differential equations

Queuing Theory

Agenda

system design

• A case study.

A Queuing System

Jockeying

Reneging

Service

Mechanism

Queue Structure Service Process

Arrival

Process

Balking

Input Source

Queuing Concepts Relationships for M/M/C

C-1 (l/m) n

c + (l/m)

c! ( )

cm - l

c (l m) o P

(c – 1)! (cm – l)

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(1 - l/m)

2 l s + (l/m)

2(1 - l/m)

2 (l/m)

(1 - l/m)

(l/m) 2

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

• Might be accomplished using Excel and the

Random()

Monte Carlo Simulation A Simple Example

Day RN Deman

Unsold

Return

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 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.

Agenda

system design

• A case study.

Benefits of using DES Simulation

• Mathematical & Excel based models only go so

• 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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