May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Its two most important goals can be summarized as: Get it right the first ti
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Its two most important goals can be summarized as:
Get it right the first time—It is much better to catch mistakes early, when they are less costly to fix, than to wait for final inspection.
Reduce variation—Variability is the main culprit that hurts quality, so companies need to be able to measure it and give workers a way to eliminate it.
Trang 3Deming’s 14 Points
(slide 1 of 3)
W Edwards Deming is probably more responsible for today’s emphasis
on quality than any other single individual.
Deming taught Japanese industries after World War II the principles of
quality management, for which they are now well known
In the early 1980s, Deming and a few other quality gurus began teaching U.S companies the statistical principles they needed to compete
successfully
Deming is perhaps best remembered for his famous 14 points, a list of precepts he taught in all of his seminars
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Deming’s 14 Points
(slide 2 of 3)
1. Constancy of Purpose—Create constancy of purpose toward improvement of
product and service, allocating resources to provide for long-range needs rather than only short-term profitability, with a plan to become competitive, stay in business, and provide jobs
2. The New Philosophy—Adopt the new philosophy We are in a new economic
age, created in Japan We can no longer live with commonly accepted levels
of delays, mistakes, defective materials, and defective workmanship
Transformation of Western management style is necessary to halt the continued decline of industry
3. Cease Dependence on Mass Inspection—Eliminate the need for mass
inspection as a way to achieve quality by building quality into the product in the first place Require statistical evidence of built-in quality in both
manufacturing and purchasing functions
4. End Lowest-Tender Contracts—End the practice of awarding business solely
on the basis of price tag
5. Improve Every Process—Improve constantly and forever the system of
production and service, to improve quality and productivity, and thus constantly decrease costs
6. Institute Training—Institute modern methods of training for everybody’s job,
including management, to make better use of every employee
Trang 5Deming’s 14 Points
(slide 3 of 3)
7. Institute Leadership of People—Adopt and institute leadership aimed at
helping people to do a better job
8. Drive Out Fear—Encourage effective two-way communication and other
means to drive out fear throughout the organization so that everybody can work effectively and more productively for the company
9. Break Down Barriers—Break down barriers between departments and staff
areas
10. Eliminate Exhortations—Eliminate the use of slogans, posters, and
exhortations for the workforce, demanding zero defects and new levels of productivity without providing methods
11. Eliminate Arbitrary Numerical Targets—Eliminate work standards that
prescribe quotas for the workforce and numerical goals for people in management
12. Permit Pride of Workmanship—Remove the barriers that rob hourly workers,
and people in management, of their right to pride of workmanship
13. Encourage Education—Institute a vigorous program of education, and
encourage self-improvement for everyone
14. Top Management Commitment and Action—Clearly define top
management’s permanent commitment to ever-improving quality and
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Introduction to Control Charts
There are two types of variability in a process:
If the current variability in the output of a process is due entirely to the inherent nature of the process, we say that its variability is due to common causes and that the process is in statistical control, or simply, is an in-control process.
Common cause variability is the inherent variation in an in-control process.
If some of the current variability of the process is due to specific assignable
causes, such as bad materials or an improperly adjusted machine, we say that the process is an out-of-control process.
Assignable cause variability is the extra variation observed when a process goes out
of control—which could be for any number of reasons.
Trang 7Introduction to Control Charts
(slide 2 of 3)
One of the main purposes of control charts is to monitor a process so that a company can see when a process goes from an in-control
condition to an out-of-control condition.
A process in control is not necessarily a good process, but it is at least
predictable, regardless of whether it is any good.
An out-of-control process, on the other hand, is unpredictable.
The assignable causes that produce out-of-control behavior can often
be corrected by the workers on the shop floor, without management intervention.
There is little workers can do to improve an in-control process that has unacceptable variability
Control charts allow workers to measure the amount of variability, but there
is generally no way they can reduce the amount of variability without
guidance from management
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Introduction to Control Charts
(slide 3 of 3)
The primary reasons that control charts have become so popular
include:
They improve productivity and lower costs
Productivity is defined as the number of good items produced per hour.
Control charts allow mistakes to be found early in the process—before they result
in poor finished products.
They prevent unnecessary process adjustments
Control charts allow the operator to see when a process is really in need of an
adjustment.
This prevents unnecessary “tampering.”
They provide diagnostic information about the process
Control charts not only signal when something is wrong, but they provide clues as
to the cause of the problem.
They provide information about process capability
Process capability is defined as the ability to produce outputs that meet
specifications.
Control charts provide this information, at least when the process is in control.
Trang 9Control Charts for Variables
(slide 1 of 2)
There are two basic types of control charts:
Charts for variables are relevant when there is a measurable quantity, such
as a diameter or a weight, that can be monitored
The purpose of the chart is to see how this quantity varies through time.
Charts for attributes are appropriate when a item is judged to conform to specifications or not
This type of chart tracks the proportion of conforming (or nonconforming) parts through time.
It is also appropriate for tracking the number of defects through time.
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Control Charts for Variables
(slide 2 of 2)
Two of the most common types of variables control charts are the X
chart and the R chart.
To produce X and R charts, we randomly sample a small number of items
and measure the characteristic
The resulting sample of measurements is called a subsample.
An X chart plots the averages of small subsamples through time.
Its purpose is to see how the mean of the process is changing through time.
An R chart plots the ranges (maximum minus minimum) of small
subsamples through time
Its purpose is to see how the variability of the process is changing through time.
The resulting time series plots are more informative when centerlines and control limits are added to the charts
A centerline indicates the average value that the X’s (or R’s) vary around.
Control limits place upper and lower bounds on where the X’s (or R’s) should be
for a process in control.
Trang 11Example 20.1:
Soda Cans.xlsx (slide 1 of 2)
Objective: To use X and R charts to check whether the process of
filling soda cans is performing as it should.
Solution: The data file contains data on the number of ounces of soda
in cans labeled “12-ounce” cans
Every half hour, five cans of soda from a production process were measured for fill volume
This was done for 70 consecutive half-hour periods
To create the charts in StatTools, designate the data range as a
StatTools data set and then select X/R Charts from the Quality Control group on the StatTools ribbon.
Fill in the resulting dialog box, select the variables Obs1 and Obs5,
limit the graph to observations 1 to 30, and base the control limits
only on these observations.
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Example 20.1:
Soda Cans.xlsx (slide 2 of 2)
StatTools creates a new sheet called X-R Charts, which contains the data
the control charts are based on, along with the X and R charts.
These charts are shown below
No points are outside of the control limits, and there is no obvious
“nonrandom” behavior, such as an upward trend through time
Therefore, this process appears to be in control
Trang 13More on X and R Charts
The X chart is a plot of the subsample averages, that is, the individual X’s.
The centerline for this plot is the average of all X’s, denoted X.
The lower and upper control limits, denoted LCL and UCL, are
approximately three standard deviations (of X) on either side of the centerline, where the standard deviation of X is σ√n and n is the subsample size.
The R chart measures within-subsample variation over time.
Each R measures the variability in the process at a given point in time.
For the R chart, we use the average R as the centerline and again go out three standard deviations (of R) on either side to form the control limits.
We typically look at the R chart first, because the control limits for the X chart make little sense unless the R’s are in control.
Assuming that the R chart indicates in-control behavior, we then shift our attention to the X chart.
Any point beyond the control limits suggests a shift, either up or down, in the mean of the process
If we see such a point, we can begin searching for an assignable cause
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Example 20.1 (Continued):
Soda Cans.xlsx (slide 1 of 2)
Objective: To continue the X and R charts to learn whether the soda can
process stays in control beyond the subsamples on which the original charts were based.
Solution: Plot all of the subsamples but base the control limits and
centerlines only on subsamples 1-30.
The resulting X and R charts are shown below.
Trang 15Example 20.1 (Continued):
Soda Cans.xlsx (slide 2 of 2)
The R chart shows that the process stayed in control for at least 10
more half-hour periods beyond subsample 30.
However, beginning shortly after subsample 40, the process variability
appears to have increased and two points jumped above the upper control limit
Presumably, the operator discovered the problem and fixed it around the time of subsample 55
At about the same time, the X chart suggests a downward shift in the
process mean.
Many points are below the centerline, and one finally crosses the lower
control limit on subsample 63
It appears that this machine needs to be readjusted to bring its mean back
up to the previous level
After this is done, both control charts should indicate an in-control process—
at least until some other assignable cause forces it out of control again
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Control Charts and Hypothesis Testing
We call this a false alarm.
We want to make the probability of a type I error fairly small; if it is too large, we react to too many false alarms.
Therefore, we set the control limits fairly far apart so that the chance of observing a point beyond them is very small.
The mean number of subsamples until an in-control process produces a point beyond the control limits is called the average run length, or ARL.
If we set the control limits three standard deviations from the centerline, ARL = 1/0.0027 ≅ 370.
Trang 17Control Charts and Hypothesis Testing
(slide 2 of 2)
Type II error—when the process has gone out of control, but the control charts do not indicate it
It is difficult to calculate the probability of a type II error because there are many
types of out-of-control conditions that could occur.
To keep both type I and type II errors to a minimum, there are two
strategies:
Sample more frequently.
Increase the subsample size.
Both strategies are intended to decrease the ARL when the process goes out of control.
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Other Out-of-Control Indications
(slide 1 of 2)
In addition to points beyond the control limits, other possible
indications of an out-of-control process include:
1. At least 8 upward (or downward) consecutive changes
2. At least 8 consecutive points above (or below) the centerline
3. At least 2 of 3 consecutive points beyond two standard deviations from
the centerline (where both are on the same side of the centerline); usually applied only to X charts.
4. At least 4 of 5 consecutive points beyond one standard deviation from
the centerline (where all 4 are on the same side of the centerline); usually applied only to X charts.
Trang 19Other Out-of-Control Indications
(slide 2 of 2)
For the last two conditions, it is common to divide the region between the centerline and either control limit into three “zones” of width one standard deviation each, as shown below.
Then condition 3 is called the Zone A rule, and condition 4 is called the
Zone B rule
The idea is that although points within zone A and zone B are within the control limits, it is unlikely that an in-control process would have this many nearby points
in zone A or B.
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Trang 21Example 20.2:
Gaskets.xlsx (slide 1 of 2)
Objective: To see how nonrational samples can produce misleading
information in X and R charts.
Solution: Two parallel production machines produce identical types of gaskets
Every 15 minutes, four gaskets were sampled, two from each machine, to
determine their thickness
The data file contains data from this process
The charts for these data appear below
Trang 22© 2015 Cengage Learning All Rights Reserved May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Example 20.2:
Gaskets.xlsx (slide 2 of 2)
The X chart looks too good.
Each X is an average of two typical machine 1 observations and two typical
machine 2 observations
With such averages, the highs tend to cancel the lows
A rational subsample philosophy would suggest control charts for each
machine, as shown below for machine 2.
There is one out-of-control point in the X chart and nearly another.
Machine 2 should be checked for assignable causes
Trang 23Deming’s Funnel Experiment
and Tampering (slide 1 of 3)
If a system is already in control, frequent small adjustments can
actually make a system worse.
Deming called this “tampering” and often demonstrated it in his seminars with the following funnel experiment
Deming placed a funnel above a target on the floor and dropped small balls through the funnel in an attempt to hit the target.
Deming proposed four rules for adjusting the position of the funnel:
1 Never move the funnel.
2 After each ball is dropped, move the funnel—relative to its previous position—to
compensate for any error.
3 Move the funnel—relative to its original position at (0,0)—to compensate for any
error.
4 Always reposition the funnel directly over the last drop.