six sigma quality engineering l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Six Sigma Quality Engineering PowerPoint Presentation
Download Presentation
Six Sigma Quality Engineering

Loading in 2 Seconds...

play fullscreen
1 / 21

Six Sigma Quality Engineering - PowerPoint PPT Presentation


  • 190 Views
  • Uploaded on

Six Sigma Quality Engineering. Week 11 Improve Phase. Objectives. Overview of Design of Experiments A structured method to learn about a process by changing many factors at the same time. It occurs in Improvement Phase. Fractional factorial experiments are used for initial screening

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Six Sigma Quality Engineering' - addison


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
six sigma quality engineering

Six Sigma Quality Engineering

Week 11

Improve Phase

objectives
Objectives
  • Overview of Design of Experiments
    • A structured method to learn about a process by changing many factors at the same time.
    • It occurs in Improvement Phase.
    • Fractional factorial experiments are used for initial screening
    • Full factorial experiments are smaller and more precise
  • Graphical Analysis
    • Main effects plots
    • Interaction plots
    • Cube plots
  • Statistical Analysis
    • P value for main effects and interactions
improve phase
Improve Phase

Goal:

  • Develop, try out, and implement solutions that address root causes

Output:

  • Planned, tested actions that eliminate or reduce the impact of the identified root causes
  • Key Deliverables
  • Solutions
  • Risk Assessment on Solution
  • Pilot Results
  • Implementation Plans

Improve

Establish

Optimum

Process

Select

Solutions

Prepare

improvement

Plans

Develop, try out & implement solutions that address root causes

  • Improvement Strategies
  • Screen Critical Inputs (DOE Plan)
  • Refine Model
  • Define & Confirm Y = f (x)
  • FMEA for Solution
  • Cost Benefit Analysis
  • Verify Metrics
  • Prioritization Matrix
  • Document ‘To Be’ Process
  • Pilot Solution
  • Implementation & Deployment Plans
  • Process Documentation
improve phase5

Generate solutions including

Perform cost-benefit

Benchmarking and select

analysis for the

best approach based on

preferred solution

screening criteria

1

2

3

4

5

6

7

8

9

10

A

B

C

D

E

G

F

G

H

I

J

Recommend a solution

involving key

stakeholders.

Use FMEA to identify

Pilot the solution on

risks associated with the

a small scale and

Use DOE and response

solution and take

evaluate the results

surface optimization to

preventive actions

quantify relationships.

Improve Phase

Generating Solutions

Cost-Benefit Analysis

Design of Experiments

A

4

B

1

C

3

D

2

Selecting the Solution

Implementation

Piloting

Assessing Risks

Full scale

Test

Original

Develop & Execute a full plan

for implementation and

change management

what is a designed experiment
What is a Designed Experiment?
  • A method to change all the factors at once in a structured pattern to determine their effects on the output(s)
  • The structured pattern is known as an orthogonal array

A B A X B

1 -1 -1 1

2 1 -1 -1

3 -1 1 -1

4 1 1 1

0 0 0

full factorial designs
Full Factorial Designs
  • Full Factorial: Examines factor effects and interaction effects. These become large rather quickly.
    • 22 Full Factorial = 2 factors, 2 levels = 4 runs
    • 23 Full Factorial = 3 factors, 2 levels = 8 runs
    • 24 Full Factorial = 4 factors, 2 levels = 16 runs
    • 25 Full Factorial = 5 factors, 2 levels = 32 runs
  • Used after initial screening experiments or where the process is simple or well known. The experiment is run to optimize the process using a vital few factors.
fractional factorial designs
Fractional Factorial Designs
  • Fractional Factorial: Examines factor effects and a carefully selected portion of interaction effects.
  • Shrinks the number of runs for each fraction by one half.
    • 27 Full Factorial = 7 factors, 2 levels = 128 runs
    • 2(7-1) 1/2 Fractional Factorial = 7 factors, 2 levels = 64 runs
    • 2(7-2) 1/4 Fractional Factorial = 7 factors, 2 levels = 32 runs
    • 2(7-3) 1/8 Fractional Factorial = 7 factors, 2 levels = 16 runs
    • 2(7-4) 1/16 Fractional Factorial = 7 factors, 2 levels = 8 runs
fractional factorial designs11
Fractional Factorial Designs
  • Uses interaction column settings to estimate the effects of main factors.
  • Used for initial screening designs to isolate the important (vital few) factors.
  • One DoE leads to another. Fractional Factorial DoE’s lead to smaller Full Factorial DoE’s.
slide13

The Idea of Confounding

A

B

BC

C

AB

AC

ABC

-1

-1

1

1

-1

1

-1

1

2 (a)

3 (b)

5 (c)

8 (abc)

1

- 1

-1

1

1

1

1

1

1

-1

-1

1

-1

-1

1

1

-1

1

-1

1

Same Signs

Was “Y” affected by A or by the interaction of B and C?

general comments
General Comments
  • In general, industry considers 3rd and 4th order interactions to be negligible.
  • Fractional Factorial experiments “pool” the effects of interactions to estimate residual error.
  • No replicates are run - USE WITH CAUTION!
  • Use Fractional Factorial Experiments for screening, then follow up with Full Factorial Designs.
  • Keep your experiments simple
be proactive
Be Proactive!
  • DOE is a proactive tool.
  • If DoE output is inconclusive:
    • You may be working with the wrong variables
    • Your measurement system may not be capable
    • The range between high and low levels may be insufficient
  • There is no such thing as a failed experiment
    • Something is always learned
    • New data prompts asking new questions and

generates follow-on studies

design of experiments minitab practice
Design of Experiments

Minitab practice

slide20

Design Resolution

The resolution number tells you what factor and interactions will be confounded with one another.