QUALITY CONTROL
Ferdous Sarwar, PhD
Learning Objectives
2
1.
2.
3.
4.
5.
List and briefly explain the elements in the control
process
Explain how control charts are used to monitor a
process, and the concepts that underlie their use
Use and interpret control charts
Perform run tests to check for nonrandomness in
process output
Assess process capability
10-2
Quality Management
3
⚫
Quality
⚫
The ability of a product or service to consistently meet or exceed
customer expectations
⚫
⚫
For a decade or so, quality was an important focal point in business.
After a while, this emphasis began to fade as other concerns took
precedence
There has been a recent resurgence in attention to quality given
recent experiences with the costs and adverse attention associated
with highly visible quality failures:
■
■
■
■
■
Auto recalls
Toys
Produce
Dog food
Pharmaceuticals
Quality Control
4
◻
Quality control consists of developing,
designing, producing, marketing, and servicing
products and services with optimum cost
effectiveness and usefulness, which
customers will purchase with satisfaction.
-Kauro Ishikawa
Determinants of Quality
5
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Quality of design
⚫
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Quality of conformance
⚫
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The degree to which goods or services conform to the intent of
the designers
Ease-of-Use and user instructions
⚫
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Intention of designers to include or exclude features in a product
or service
Increase the likelihood that a product will be used for its intended
purpose and in such a way that it will continue to function
properly and safely
After-the-sale service
⚫
Taking care of issues and problems that arise after the sale
Cost of Quality
6
◻
Appraisal Costs
Costs of activities designed to ensure quality or
uncover defects
◻
Prevention Costs
All TQ training, TQ planning, customer
assessment, process control, and quality
improvement costs to prevent defects from
occurring
Cost of Quality
7
⚫
Failure Costs - costs incurred by defective
parts/products or faulty services.
⚫
Internal Failure Costs
⚫
⚫
Costs incurred to fix problems that are detected
before the product/service is delivered to the
customer.
External Failure Costs
⚫
All costs incurred to fix problems that are detected
after the product/service is delivered to the customer
Quality Contributors
8
Contributor
Key Contributions
Shewart
Control charts; variance reduction
Deming
14 points; special vs. common causes of variation
Juran
Quality is fitness-for-use; quality trilogy
Feigenbaum
Quality is a total field; the customer defines quality
Crosby
Quality is free; zero defects
Ishikawa
Cause-and-effect diagrams; quality circles
Taguchi
Taguchi loss function
Ohno and
Shingo
Continuous improvement
TQM: Definition
9
◻
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Total Quality Management (TQM) and Six
Sigma (6σ) are sweeping “culture change”
efforts to position a company for greater
customer satisfaction, profitability and
competitiveness.
TQM may be defined as managing the entire
organization so that it excels on all dimensions
of products and services that are important to
the customer.
Total Quality Management
10
Main concerns of Manufacturers and Customers
Manufacturer
Customer
Quality
Quality
Cost
Price
Productivity
Availability
Quality is the only common concern
Total implies Complete - 100%
All areas and functions
All activities
All employees - everyone
All time - always
(a) The traditional cost of quality model, and (b) the traditional cost of
quality model with adjustments to reflect TQM criticisms
11
Total Quality Management
12
◻
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Quality target is 100%, not even 99.9% because
even 99.9% might mean many dissatisfied
customers every year, defective components
entering assembly, accidents etc.
Quality definition
Old view : Quality relates to products manufactured
exactly to specifications.
New view : Total Quality relates to products that
totally satisfy our customer needs and expectations in
every respect on a continuous basis. Quality then is to
satisfy customer needs.
Who is our customer?
13
◻
◻
The next person(individual or functional group)
in the workplace; the receiver of output and the
next to act on it.
A customer may be either external or internal.
Sector
Next in process customer
Marketing
Design
Design
Manufacturing
Manufacturing
Sales
Machine Shop
Assembly
Assembly
Testing
Sales
Product User
TQM Approach
14
4.
Find out what the customer wants
Design a product or service that meets or exceeds
customer wants
Design processes that facilitate doing the job right the
first time
Keep track of results
5.
Extend these concepts throughout the supply chain
1.
2.
3.
TQM Elements
15
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Continuous improvement
Competitive benchmarking
Employee empowerment
Team approach
Decision based on fact, not opinion
Knowledge of tools
Supplier quality
Champion
Quality at the source
Suppliers are partners in the process
PDSA Cycle
16
◻
Plan-Do-Study-Act (PDSA) Cycle
Plan
■
■
■
■
Begin by studying and documenting the current
process.
Collect data on the process or problem
Analyze the data and develop a plan for improvement
Specify measures for evaluating the plan
Do
■
Implement the plan, document any changes made,
collect data for analysis
PDSA Cycle
17
⚫
Plan-Do-Study-Act (PDSA) Cycle
⚫
Study
⚫
⚫
⚫
Evaluate the data collection during the do phase
Check results against goals formulated during the
plan phase
Act
⚫
⚫
⚫
If the results are successful, standardize the new
method and communicate it to the relevant personnel
Implement training for the new method
If unsuccessful, revise the plan and repeat the
process
TQM : Basic Tools
18
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Check Sheet
Stratification Analysis
Histogram
Pareto Analysis
Process Flow Chart
Cause-Effect Diagram
Scatter Diagram
Control Chart
Histogram
19
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Show frequency distribution and its pattern or
shape
Use when the data are numerical
Organize data into groups by counting how much
data is in each group
Groups->Bins
Frequency ->number of observations
Max = maximum value of the given data
Min= minimum value of the given data
Range=Max-Min
Histogram
20
◻
◻
Bin width=(max-min)/ No. of bins
Adjusted bin width=round up the bin width to the
higher value
Example: if bin width is 5.4 , the adjusted bin
width 6 if bin width is 0.028 , the adjusted bin
width 0.03
Construction of Histogram
21
◻
◻
Collect at least 30 data points from a process
Types
Normal
Skewed
Double peaked
Plateau
Edge peak
Truncated
Pareto Chart
22
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Pareto Analysis often reveals that a small
number of failures are responsible for the bulk of
quality costs, a phenomenon called the ‘Pareto
Principle.’
Use 1: To determine the most common type
of defect
Use 2: To focus your efforts on projects that
will bring the greatest returns
Pareto Chart-contd.
23
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This pattern is also called the ‘80/20 rule’ and
shows itself in many ways. For example:
• 80% of sales are generated by 20% of
customers.
• 80% of Quality costs are caused by 20% of the
problems.
The lengths of the bars represent frequency or
cost
Longest bar on the left and the shortest to the
right
Pareto Chart-contd.
24
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◻
A Pareto diagram allows data to be displayed as a
bar chart and enables the main contributors to a
problem to be highlighted.
As a basic Quality Improvement tool, Pareto
Analysis can:
Define categories of defects which cause a particular
output (product, service, unit) to be defective;
Count the frequency of occurrence of each defect;
Display graphically as a bar chart, sorted in
descending order, by frequency of defect;
Use a second y axis to show the cumulative % of
defects.
Pareto Chart-Example
25
Pareto Chart-Example
26
Pareto Chart-Example
27
Process Flow Chart
28
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An organized combination of shapes, lines,
and text that graphically illustrates a process
or structure
A graphical tool that shows the major steps in
a process
Run chart, route sheet or process map
Standard symbols proposed by American
Production and Inventory Control Society
(APICS)
29
Cause-Effect Diagram
30
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A tool for analyzing and illustrating a process
by showing the main causes and sub-causes
leading to an effect
Developed by Kauro Ishikawa-Ishikawa
diagram or fishbone diagram
Cause Enumeration
Process Analysis
31
Construction of CE Diagram
32
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Step 1 Write down the effect to be investigated
and draw the 'backbone' arrow to it.
Step 2 Identify all the broad areas of enquiry in
which the causes of the effect being investigated
may lie.
Step 3 This step requires the greatest amount of
work and imagination because it requires you to
write in all the detailed possible causes in each of
the broad areas of enquiry. Each cause identified
should be fully explored for further more specific
causes which, in turn, contribute to them.
Scatter Diagram
33
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A scatter graph is a graph using paired data that can
be used to find out whether there is a relationship
between two variables.
Paired data is two separate pieces of data referring to
the same thing e.g. the age and value of a car, the
height and shoe size of a person, the marks that a
person gained in two separate tests
A variable is a piece of information that can change.
e.g. test results - these can be any value, but will be a
specific value for a particular person's test.
The more scattered the points are, the weaker the
relationship is.
Scatter Diagram
34
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Correlation is a measure of the relationship
between two variables;
Correlation is assessed by being strong or
weak
Strong means there is a very strong
relationship such as ‘the hotter the weather the
more ice creams are sold’
Weak means there is no relationship between
things such as ‘the colder the weather the
better my exam results will be’
What is Quality Control?
35
◻
Quality Control
A process that evaluates output relative to a
standard and takes corrective action when output
doesn’t meet standards
■
■
If results are acceptable no further action is required
Unacceptable results call for correction action
10-35
Quality assurance
36
Operations Strategy
37
◻
Quality is a primary consideration for nearly all
customers
Achieving and maintaining quality standards is of
strategic importance to all business organizations
■
■
Product and service design
Increase capability in order to move from extensive use
of control charts and inspection to achieve desired
quality outcomes
10-37
Inspection
38
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Inspection
An appraisal activity that compares goods or
services to a standard
Inspection issues:
1.
2.
3.
4.
How much to inspect and how often
At what points in the process to inspect
Whether to inspect in a centralized or on-site
location
Whether to inspect attributes or variables
10-38
Where to Inspect in the Process
39
1.
2.
3.
4.
5.
Raw materials and purchased parts. Supplier certification
programs can reduce or eliminate the need for inspection.
Finished products. Well-designed processes, products and
services, quality at the source, and process monitoring can reduce
or eliminate the need for inspection.
Before a costly operation. The point is to not waste costly labor
or machine time on items that are already defective.
Before an irreversible process. In many cases, items can be
reworked up to a certain point; beyond that point they cannot. For
example, pottery can be reworked prior to firing. After that,
defective pottery must be discarded or sold as seconds at a lower
price.
Before a covering process. Painting, plating, and assemblies
often mask defects.
40
41
42
43
44
45
Quality cycle
46
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Define. The first step is to define in sufficient detail
what is to be controlled. The paint can have a
number of important characteristics such as its
thickness, hardness, and resistance to fading or
chipping.
Measure. Only those characteristics that can be
counted or measured are candidates for control.
Compare. There must be a standard of
comparison that can be used to evaluate the
measurements. This will relate to the level of
quality being sought.
Quality cycle-contd.
47
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Evaluate. The main task of quality control is to
distinguish random from nonrandom variability,
because nonrandom variability means that a process is
out of control.
Correct. When a process is judged out of control,
corrective action must be taken. This involves
uncovering the cause of nonrandom variability (e.g.,
worn equipment, incorrect methods, failure to follow
specified procedures) and correcting it.
Monitor results. To ensure that corrective action is
effective, the output of a process must be monitored for
a sufficient period of time to verify that the problem has
been eliminated.
Statistical Process Control (SPC)
48
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Quality control seeks
⚫
Quality of Conformance
⚫
⚫
A product or service conforms to specifications
A tool used to help in this process:
⚫
SPC
⚫
Statistical evaluation of the output of a process
⚫
Helps us to decide if a process is “in control” or if
corrective action is needed
10-48
Control Charts: The Voice of the
Process
49
⚫
Control Chart
⚫
⚫
A time ordered plot of representative sample statistics
obtained from an ongoing process (e.g. sample
means), used to distinguish between random and
nonrandom variability
Control limits
⚫ The dividing lines between random and
nonrandom deviations from the mean of the
distribution
⚫ Upper and lower control limits define the range
of acceptable variation
10-49
50
51
52
Control Charts for Variables
53
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Variables generate data that are measured
Mean control charts
■
Used to monitor the central tendency of a process.
■
“x- bar” charts
Range control charts
■
Used to monitor the process dispersion
■
R charts
10-53
Establishing Control Limits
54
10-54
X-Bar Chart: Control Limits
55
◻
Used to monitor the central tendency of a
process
10-55
Range Chart: Control Limits
56
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Used to monitor process dispersion
10-56
Application of X-R Chart
57
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To monitor the stability of your process
To determine whether your process is stable
and ready for improvement
To demonstrate improved process
performance
Mean and Range Charts
58
59
60
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62
63
64
65
66
Control Charts for Attributes
67
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Attributes generate data that are counted.
p-Chart
■
Control chart used to monitor the proportion of
defectives in a process
c-Chart
■
Control chart used to monitor the number of defects
per unit
10-67
P-Chart
68
◻
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To evaluate process stability when counting the
fraction defective.
It is used when the sample size varies: the total
number of circuit boards, meals, or bills
delivered varies from one sampling period to the
next.
Example
69
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Repeated samples of 150 coffee cans are inspected to determine
whether a can is out of round or whether it contains leaks due to
improper construction. Such a can is said to be nonconforming.
Following is the data.
Sample
1
2
3
4
5
6
7
8
9
10
Nonconforming#
19
10
4
6
8
9
3
1
0
4
C-Chart
70
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Determining stability of "counted" data (e.g., errors per
widget, inquiries per month, etc.)
The c chart will help evaluate process stability when
there can be more than one defect per unit. Examples
might include: the number of defective elements on a
circuit board, the number of defects in bank statement,
invoice, or bill.
The c chart is useful when it's easy to count the number
of defects and the sample size is always the same.
Example
71
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An automobile assembly worker is interested in
monitoring and controlling the number of minor paint
blemishes appearing on the outside door panel on the
driver’s side of a certain make of automobile. The
following data were obtained, using a sample of 25 door
panel.
Sample
1
2
3
4
5
6
7
-----
-----
25
# of Paint Blemishes
19
10
4
6
8
9
3
------
-----
4
When to use a particular chart?
72
Use a p-chart
1. When observations can be placed
into one or two categories. Examples
include items that can be classified as
a. Good or bad
b. Pass or fail
c. Operate or don’t operate
2. When the data consists of multiple
samples of n observations each. (such
as 15 samples of n=20 observations
each)
Use a c-chart
1. When only the number of
occurrences per unit of measure can be
counted; Non-occurrences cannot be
counted. Examples:
a. Scratches, chips, dents, errors per
items
b. Cracks or faults per unit of distance
c. Breaks or tears per unit of area
d. Pollutants per unit of volume
e. Calls, complaints, failures,
equipment breakdowns, crimes per
unit of time
Example
73
74
75
76
77
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U-Chart
79
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to monitor the number of defects per unit, where each
item can have multiple defects.
to monitor process stability over time
For example, an LCD manufacturer wants to monitor the
number of dead pixels on 17-inch LCD screens.
Technicians record the number of dead pixels for each
screen. Each subgroup has a different number of
screens. The manufacturer uses a U chart to monitor the
average number of dead pixels per screen.
Data considerations for U Chart
80
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You must be able to count the number of defects on
each item or unit
The data should be in time order
The data should be collected at appropriate time
intervals
Collect data in subgroups
The subgroups must be large enough
The data must include enough subgroups to obtain
precise control limits
Example
81
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A manager for a transcription company wants
to assess the quality of the transcription
service. The manager randomly selects 25
sets of pages from consecutive orders and
counts the number of typographical errors
(defects). Each set has a different number of
pages.
Example
82
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Because the sample sizes are unequal, the control limits vary. The
average number of defects per set of pages is 0.238. Subgroups 6
and 18 failed Test 1 because they are outside of the control limits.
Thus, the process is out of control. The manager should identify and
correct any factors that contribute to the special-cause variation.
Process Capability
83
⚫
Once a process has been determined to be stable, it is
necessary to determine if the process is capable of
producing output that is within an acceptable range
⚫
Tolerances or specifications
⚫
⚫
Process variability
⚫
⚫
Range of acceptable values established by engineering
design or customer requirements
Natural or inherent variability in a process
Process capability
⚫
The inherent variability of process output (process width)
relative to the variation allowed by the design specification
(specification width)
10-83
Process capability
84
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A capable process is able to produce products or services
that conform to specifications.
Capability is determined by comparing the process spread to
the specification spread. In other words, the width of the
process variation is compared to the width of the
specification interval. What you want to see is that the
process spread is smaller than and contained within the
specification spread.
Capability indices are ratios of the process spread and
specification spread.
Some capability indices consider the process mean or
target. Many practitioners consider 1.33 to be a minimum
acceptable value for capability indices; and most
practitioners believe a value less than 1 is not acceptable.
Process Capability: Cp
85
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Cp is a capability index defined as the ratio of
the specification spread (USL - LSL) to the
potential process spread (6 times the
within-subgroup standard deviation).
Cp does not consider the location of the
process mean in relation to the specification
interval, so it is a measure of the capability
your process could achieve if centered
between the specification limits.
Cpk
86
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Cpk is a capability index that equals the
minimum of CPU and CPL.
Cpk considers the location of the process
mean relative to the specification interval, so it
is a measure of how the process is actually
performing.
Process Performance: Pp
87
◻
◻
Pp is a capability index defined as the ratio of
the specification spread (USL-LSL) to the
actual process spread (6 times the overall
standard deviation).
Pp does not consider the location of the
process mean in relation to the specification
interval, so it is a measure of the capability
your process could achieve if centered
between the specification limits.
Ppk
88
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Ppk is a capability index that equals the
minimum of PPU and PPL..
Ppk considers the location of the process
mean relative to the specification interval, so it
is a measure of how the process is actually
performing.
Application
89
◻
Use 1: To determine whether your process meets
specifications
An automotive manufacturer requires headlight lenses to be within
1 mm of a target 10 cm diameter. While its supplier produces lenses
that are consistent in size (10.05-10.10 cm), they are outside of the
range of the auto maker’s specifications and have to be scrapped.
◻
Use 2: To determine the potential for process improvement
A ball bearing manufacturer examines its two production lines and
finds that, while both produce bearings of a consistent size, those
from one line are somewhat larger than the other. Eliminating this
difference would reduce the overall variation in ball bearing size.
Example
90
◻
An engine manufacturer uses a forging
process to produce piston rings. The forging
process is in control, and now the quality
engineers want to assess the process
capability. Twenty-five samples of five
measurements of the inner piston ring
diameter were collected. The specification
limits for piston ring diameter are 74.0 + 0.05.
74.03
74.002
74.019
73.992
74.008
73.995
73.992
74.001
74.011
74.004
73.988
74.024
74.021
74.005
74.002
74.002
73.996
73.993
74.015
74.009
73.992
74.007
74.015
73.989
74.014
91
74.009
73.994
73.997
73.985
73.993
73.995
74.006
73.994
74
74.005
73.985
74.003
73.993
74.015
73.988
74.008
73.995
74.009
74.005
74.004
73.998
74
73.99
74.007
73.995
73.994
73.998
73.994
73.995
73.99
74.004
74
74.007
74
73.996
73.983
74.002
73.998
73.997
74.012
74.006
73.967
73.994
74
73.984
74.012
74.014
73.998
73.999
74.007
74
73.984
74.005
73.998
73.996
73.994
74.012
73.986
74.005
74.007
74.006
74.01
74.018
74.003
74
73.984
74.002
74.003
74.005
73.997
74
74.01
74.013
74.02
74.003
73.982
74.001
74.015
74.005
73.996
74.004
73.999
73.99
74.006
74.009
74.01
73.989
73.99
74.009
74.014
74.015
74.008
73.993
74
74.01
73.982
73.984
73.995
74.017
74.013
92
Result interpretation
93
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For the piston data, Cp is 1.66, which indicates that the
specification spread is 1.66 times greater than the 6- σ
spread in the process.
Cp (1.66) and Cpk (1.62) are very close to one another,
indicating that the process is centered on target. The
capability indices are greater than 1.33, indicating that the
process is centered on target and capable of producing
pistons that conform to specifications.
For the piston data, Pp is 1.63, which indicates that the
specification spread is 1.63 times greater than the 6-σ
spread in the process.
Pp (1.63), Ppk (1.60), and Cpm (1.62) are very close to one
another, indicating that the process is centered on target. All
three capability indices are greater than 1.33, which
traditionally is the value used for determining capability.
Thus, the process is centered on target and is capable of
producing pistons that conform to specifications.
Result interpretation
94
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PPM < LSL - number of parts per million (PPM) that have
measurements less than the lower specification limit.
PPM > USL - number of parts per million (PPM) that have
measurements greater than the upper specification limit.
PPM Total - number of parts per million (PPM) that have
measurements beyond the specification limits. PPM Total is the
sum of PPM < LSL and PPM > USL.
For the piston data, all measurements are located inside the
specification interval, so all three PPMs are zero.
For the piston data, 0.18 parts per million are expected to have
measurements less than the LSL and 0.59 parts per million are
expected to have measurements greater than the USL.
For the piston data, 0.26 parts per million are expected to have
measurements less than the LSL and 0.85 parts per million are
expected to have measurements greater than the USL.
Some Common Indices of Process Capability
Cp Formula
Specification Range
Variation of Distribution
of Individual Product
reject
reject
USL ~ LSL
Cp(1) < Cp(2)
(2)
(1)
T
-3σX(1)
95
-3σX(2)
+3σX(1)
N(μX, σX)
+3σX(2)
X
Process Capability Index, Cpk
96
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Purpose: To promote adherence of process
mean to target (nominal) value of spec.
Formulas:
Example
LSL
USL
(μX-T) = bias
100
97
N(130, 10) T = 145
190 x
98