11 1 name work/educational experience background/classes taken in math, quality, continuous...
TRANSCRIPT
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Name Work/educational experience Background/classes taken in
math, quality, continuous improvement, statistics, SPC, designed experiments
Expectations
Introduction
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Chapter 1
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What is quality?
Quality = Performance
Expectation
Fitness for use
Conformance to specifications
Producing the best results
Total customer satisfaction
Exceeding customer
expectations
Excellent products or services
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Kano Model
The Kano model relates three factors to their degree of implementation or level of implementation, as shown in the diagram. 1) Basic ("must be") factors2) Performance ("more is better") factors3) Delighter ("excitement") factors.
The degree of customer satisfaction ranges from disgust, through neutrality, to delight.
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Quality
Cost
Delivery
Responsiveness
Safety
… what about other factors?
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1. Each team will be handed 20 cards.
2. Each team will have three operators, each of whom will drop one card at a time onto a target area.
3. The method of drop will be to hold the card at arms length over the target area or not. Only those cards that fall completely within the target area may move on.
4. The goal is to deliver 20 completed products or units to the customer.
5. Metrics-1. # of good units per station (A)2. # of cards used per station (B)3. Total time of exercise (C)4. Total # of defects (D)
Exercise: Why control a process?
Station 1 Station 2 Station 3 CustomerStart
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Exercise cont…
A = # of good units B = # of cards
A1= B1=
A2= B2=
A3= B3=
FPY: Y1 = A1/B1 =
Y2 = A2/B2 =
Y3 = A3/B3 =
RTY = Y1 * Y2 * Y3 =
Total Cost = ($10 * D) + ($2 *[B1+B2+B3])=
Average cost per unit = Total cost / 20 =
Average cycle time = C / 20 =
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RTY chart
99-100%90-99%0-90%
# of Steps +/- 3 Sigma +/- 4 Sigma +/- 5 Sigma +/- 6 Sigma1 93.32% 99.379% 99.9767% 99.99966%7 61.63% 95.733% 99.837% 99.998%
10 50.09% 93.961% 99.767% 99.997%20 25.09% 88.286% 99.535% 99.993%40 6.29% 77.944% 99.072% 99.986%60 1.58% 68.814% 98.612% 99.980%80 0.40% 60.753% 98.153% 99.973%
100 0.10% 53.637% 97.697% 99.966%150 0.00% 39.282% 96.565% 99.949%200 0.00% 28.769% 95.446% 99.932%300 0.00% 15.431% 93.248% 99.898%400 0.00% 8.277% 91.100% 99.864%500 0.00% 4.439% 89.002% 99.830%600 0.00% 2.381% 86.952% 99.796%700 0.00% 1.277% 84.949% 99.762%800 0.00% 0.685% 82.992% 99.728%900 0.00% 0.367% 81.081% 99.694%
1,000 0.00% 0.197% 79.213% 99.661%1,200 0.00% 0.057% 75.606% 99.593%3,000 0.00% 0.000% 49.704% 98.985%
17,000 0.00% 0.000% 1.904% 94.384%38,000 0.00% 0.000% 0.014% 87.880%70,000 0.00% 0.000% 0.000% 78.820%
150,000 0.00% 0.000% 0.000% 60.050%
(Distribution shifted +/- 1.5 Sigma)Overall Yield vs. Sigma
a.k.a. LeanSigma
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Interesting Quote
“Quality control by statistical methods is now so extensively applied in all lines of industry, and in all sections of the United States, that everyone who is interested in
manufacturing should also have a definite interest in the methods.”
-Control Charts, E.S. Smith - 1947
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First, it is nothing new. Developed in the 1920’s.
Description: Involves the use of statistical signals to identify sources of variation, to maintain or improve performance to a higher quality level
What is SPC?
Process Control Statistical
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Quality is a must Detection mentality is out the
door Must build quality in Quality is a part of all job
functions
Why SPC?
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Training Quality
Planning Design
Review Quality
system audits
Continuous improvement
Technical data review
Process validation
Marketing research
Customer surveys
Field trials Supplier
quality planning
SPC Process
Control
The Cost of QualityPreventative Costs:
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The use of statistical signals to maintain or improve the process
The Prevention Model
Process Shipment
Figure 1.3
Output
Inspect with SPCAnalyze
Continueor Improve
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Let’s build a prevention model for taking a college course.
Exercise: Prevention Model
__________________
__________________
Output
Inspect with SPCAnalyzeImprove
______________
______________
______________
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Purchasing Appraisal Costs
Receiving/Incoming Inspection and Test
Measurement Equipment
Qualification of Supplier Product
Source Inspection and Control Programs
Manufacturing Appraisal Costs
Planned Inspections, Tests, Audits
Checking Labor Product or Service
Quality Audits Review of Test and
Inspection data Other Quality
Evaluations Laboratory Support
Inspection and Test Materials
Set-up Inspections and Tests
Depreciation Allowances
Measurement Equipment Expense
Maintenance and Calibration Labor
Outside Endorsements and Certifications
External Appraisal Costs
Field Performance Evaluations
Special Product Evaluations
Evaluations of Field Stock and Spare Parts
Process Control Measurement
The Cost of Poor QualityAppraisal Costs:
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An attempt to achieve quality by inspecting the quality into the product through 100% inspection
The Detection Model
Process Inspection Shipment
Repair/ Rework
Scrap or wasteFigure 1.2
What happens when your process improves?
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Exercise: Detection Model
______________
______________
______________
______________
______________
Let’s build a detection model for taking a college course.
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Appraisal exercise
The defects in this story appear as the letter “S.”How many defects or S’s (capital or lower case) can
you detect in the story below?
Bubba-Gump Shrimp Company
It was a simple test to sample the number of shrimp secured by each of the fishing vessels after passing the second level of serious FDA inspection techniques. Susan was the first of several student inspectors to have the occasion to assay the new sampling system. Susan first separated seventy random sized shrimp from the sample received from the FDA. Those seventy were then weighed on a special scale. Susan then posted this weight on a job log. The sample of seventy shrimp is then returned to the same FDA sample. The weight of the seventy shrimp is then sent to the large sample scale were the first sample of shrimp has been assembled. The sample from the fishing vessel is then weighed and a series of automatic calculations determines the best number of shrimp in the sample from the comparison of Susan’s seventy shrimp sample.
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Reliability on visual inspection methods?
1. How many S's did you find in the story after reading it only once? ___________________________________________________ 2. How many S's did you find in the story after reading it through the second time? ___________________________________________________ 3. What was your range from your first to second reading? ___________________________________________________ 4. How is this similar to “real life” inspection systems?______________________________________________________________________________________________________ 5. What is the most cost-effective way to not have non-conformances pass through the system?_____________________________________________________________________________________________________
See example 1.1, page 6, in the text book
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This is so interesting!.......can you read this note?
I cdnuolt blveiee taht I cluod aulaclty
uesdnatnrd waht I was rdgnieg.
THE PAOMNNEHAL PWEOR OF THE HMUAN MNID
Aoccdrnig to rscheearch at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a word are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The rset can be a taotl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Amzanig huh?
Something weird I thought you might like....
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The Cost of Poor QualityFailure Costs (internal or external):
• Scrap• Rework• Customer Complaint Investigation• Returned Goods• Retrofit Costs• Recall Costs• Warranty Claims• Liability Costs• Penalties• Customer/User Goodwill • Other Failure Costs
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Minimize cost Attain a consistent process Allow everyone to contribute to
process improvement Help to make economical
decisions
Goals of SPC
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Use the seven basic tools◦ Flowchart◦ Pareto chart◦ Checksheet◦ Cause-and-effect diagram◦ Histogram◦ Control Chart◦ Scatterplot
How?
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1. Analyze where SPC should be done
2. Work on decreasing any obvious variability
3. Gage R&R4. Make sampling plan5. Create control chart – allow
only common cause variability6. Run the process7. Calculate process capability8. Improve if necessary or control
process9. Pre-control10. Continue to improve
How to apply SPC to a process
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DOE – Design of Experiments or referred as Designed Experiments
A systematic change to process variables to find the best combination to produce a quality product
What is DOE?
Process
Input Output
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Will help to gain knowledge in:
Improving process performance Reducing costs Understanding relationships
between variables Understanding how to optimize
processes
Why use DOE ?
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Let’s start with an example:
Data
18 16 30 29 28 21 17 41 8 1732 26 16 24 27 17 17 33 19 1831 27 23 38 33 14 13 26 11 2821 19 25 22 17 12 21 21 25 2623 20 22 19 21 14 45 15 24 34
Fuel Economy of 50 automobiles (in mpg)
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Histogram of MPG:Fuel Economy
0
2
4
6
8
10
12
14
16
0 to <6 6 to <12 12 to <18 18 to <24 24 to <30 30 to <36 36 to <42 42 to <48 48 to <54 54 to <=60
mgp
Nu
mb
er
of
Ca
rs
What causes variation in fuel economy?
DOE is about discovering and quantifying the magnitude of cause and effect relationships.
We need DOE because intuition can be misleading.
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What about other factors - and noise?
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To explain how we can model data experimentally, let’s take another look at the mileage data and see if there’s a factor that might explain some of the variation.
Draw a scatter diagram for the following data:
Let’s talk about regression
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Mileage data with vehicle weight:
The variable called ‘weight’ is known as a ‘factor’ and is plotted on the x-axis.
The variable called ‘mileage’ is known as the response, and is plotted on the y-axis. It’s sometimes called ‘Y’.
Weight (lbs) Mileage(mpg)3000 182800 212100 322900 172400 313300 142700 213500 122500 233200 14
Observation12345
10
6789
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Scatter Diagram Form:
Mile
age
Vehicle Weight (lbs)
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Yours might look something like:
Scatter Chart (Weight vs mpg)
05
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1900 2400 2900 3400 3900
Weight
mpg
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If you draw a best fit line and figure out an equation for that line, you would have a ‘model’ that represents the data.
Regression analysis
Scatter Chart (Weight vs mpg)
y = -0.0152x + 63.507
R2 = 0.9191
05
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1900 2400 2900 3400 3900
Weight
mp
g
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Looking at correlation from a Scatter diagram:
‘Correlation’ is a fancy word for how well the model predicts the response from the factors.
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Is there really an effect?
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There are basically two ways to understand a process you are working on.
One factor at a time (OFAT) DOE
Each have their advantages and disadvantages. We’ll talk about each.
Understanding a system
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To illustrate the need for experimental design, let’s consider how two known (based on years of experience) factors affect gas mileage, tire size (T) and fuel type (F).
Why DOE an OFAT example
Fuel Type Tire size
F1 T1
F2 T2
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Step 1:Select two levels of tire size and two kinds of fuels.
Step 2: Holding fuel type constant, test the car at both tire sizes.
One–at–a-time design
Fuel Type
Tire size Mpg
F1 T1 20
F1 T2 30
404040
Since we want to maximize mpg the more desirable response happened with T2.
Step 3: Holding tire size at T2, test the car at both fuel types.
One–at–a-time design
Fuel Type
Tire size Mpg
F1 T2 30
F2 T2 40
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At first glance the ideal setting looks like F2 and T2 at 40mpg.
However this experimental method did not test the interaction effect of tire size and fuel type.
One–at–a-time design
Fuel Type
Tire size Mpg
F1 T2 30
F2 T2 40
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Suppose that the untested combination F2T1 would produce the results below.
There is a different slope so there appears to be an interaction. A more appropriate design would be to test all four combinations.
One–at–a-time design
0
10
20
30
40
50
60
70
T1 T2
Tire Size
mpg
F2
F1
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What about the other factors - and noise?
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We need a way to investigate the relationship(s) between variables
We need to distinguish the effects of variables from each other (and maybe tell if they interact with each other)
We need to be able to quantify the effects...
...so we can predict, control, and optimize processes.
What we need to answer
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Objectives of an Experimental Design Obtain the
maximum amount of information using a minimum amount of resources
Determine which factors shift the average response, which shift the variability and which have no effect
Build empirical models relating the response of interest to the input factors
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So how do we do it?
PLANNING
DESIGN
ANALYSIS
CONFIRMATION
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DOE uses purposeful changes of the inputs (factors) in order to observe corresponding changes in the output (response).
Use an IPO – they are real important here.
DOE to the rescue!!
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Here’s what one looks like:
Run X1 X2 X3 X4 Y1 Y2 Y3 Y-bar SY
1 - - - -2 - - + +3 - + - +4 - + + -5 + - - +6 + - + -7 + + - -8 + + + +
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To ‘design’ an experiment, means to pick the points that you’ll use for a scatter diagram.
The basics
Run A B
1 - -
2 - +
3 + -
4 + +
In tabular form, it would look like:
High (+)
Low (-)
Fa
cto
r B
Se
ttin
gs
Factor A Settings High (+)Low (-)
(-,+)
(+,-)
(+,+)
(-,-)
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The difference in the average Y when A was ‘high’ from the average Y when A was ‘low’ is the ‘factor effect’
Res
pons
e -
Y
Factor ALow High
Average Y when A was set ‘high’
Average Y when A was set ‘low’
The differences are calculated for every factor in the experiment.
Okay - a little math must be done. But a computer helps to keep it simple.
Measuring an “Effect”
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When the effect of one factor changes due to the effect of another factor, the two factors are said to ‘interact’.
more than two factors can interact at the same time, but it is rare.
Res
pons
e - Y
Factor ALow High
B = High
B = Low
No interaction:
Resp
onse
- Y
Factor ALow High
B = High
B = Low
Slight
Re
spo
nse
- Y
Factor ALow High
B = High
B = Low
Strong
Looking for interactions
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Poor experimental discipline Measurement error Other errors Too much variation in the
response Aliases (confounded) effects Inadequate model Something changed
Reasons why a model might not confirm:
- And: - There may not be a true
cause-and-effect relationship.
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Is there really an effect?