QUALITY CONTROL AND SPC

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OM
CHAPTER 16
QUALITY CONTROL AND SPC
DAVID A. COLLIER AND JAMES R. EVANS
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Chapter 16 Learning Outcomes

l e a r n i n g o u t c o m e s

LO1 Describe quality control system and key
issues in manufacturing and service. LO2
Explain types of variation and the role of
statistical process control. LO3 Describe how
to construct and interpret simple control charts
for both continuous and discrete data. LO4
Describe practical issues in implementing
SPC. LO5 Explain process capability and
calculate process capability indexes.
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Chapter 16 Quality Control and SPC
arriott has become
infamous for its obsessively detailed
standard operating procedures
(SOPs), which result in
hotels that travelers either love for their
consistent good quality or hate for their bland
uniformity. This is a company that has more
controls, more systems, and more procedural
manuals than anyoneexcept the government, says
one industry veteran. And they actually comply
with them. Housekeepers work with a 114-point
checklist. One SOP Server knocks three times.
After knocking, the associate should immediately
identify themselves in a clear voice, saying,
Room Service! The guests name is never
mentioned outside the door. Although people love
to make fun of such procedures, they are a
serious part of Marriotts business, and SOPs are
designed to protect the brand. Recently,
Marriott has removed some of the rigid guidelines
for owners of hotels it manages, empowering them
to make some of their own decisions on details.

What do you think? What opportunities for
improved quality control or use of SOPs can you
think of at your college or university (e.g.,
bookstore, cafeteria)?
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Chapter 16 Quality Control and SPC
Quality Control Systems The task of quality
control is to ensure that a good or service
conforms to specifications and meets customer
requirements by monitoring and measuring
processes and making any necessary adjustments to
maintain a specified level of performance.

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Chapter 16 Quality Control and SPC

• Quality Control Systems
• Quality Control Systems have three components
• A performance standard or goal,
• A means of measuring actual performance, and
• Comparison of actual performance with the
  standard to form the basis for corrective action.

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Chapter 16 Quality Control and SPC
110100 Rule If a defect or service error is
identified and corrected in the design stage, it
might cost 1 to fix. If it is first detected
during the production process, it might cost 10
to fix. However, if the defect is not discovered
until it reaches the customer, it might cost 100
to correct.
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Chapter 16 Quality Control and SPC
Quality at the source means the people
responsible for the work control the quality of
their processes by identifying and correcting any
defects or errors when they first are recognized
or occur.
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Chapter 16 Quality Control and SPC

• Quality Control Practices in Manufacturing
• Supplier Certification and Management ensures
  conformance to requirements before value-adding
  operations begin.
• In-process control ensures that defective
  outputs do not leave the process and prevents
  defects in the first place.
• Finished goods control verifies that product
  meets customer requirements.

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Chapter 16 Quality Control and SPC

• Quality Control Practices in Services
• Prevent sources of errors and mistakes in the
  first place by using poka-yoke approaches.
• Customer satisfaction measurement with actionable
  results (responses that are tied directly to key
  business processes).
• Many quality control tools and practices apply to
  both goods and services.

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Chapter 16 Quality Control and SPC

• Statistical Process Control and Variation
• Statistical process control (SPC) is a
  methodology for monitoring quality of
  manufacturing and service delivery processes to
  help identify and eliminate unwanted causes of
  variation.

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Chapter 16 Quality Control and SPC

• Statistical Process Control and Variation
• Common cause variation is the result of complex
  interactions of variations in materials, tools,
  machines, information, workers, and the
  environment.
• Common cause variation accounts for 80 to 95
  percent of the observed variation in a process.
• Only management has the power to change systems
  and infrastructure that cause common cause
  variation.

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Chapter 16 Quality Control and SPC

• Statistical Process Control and Variation
• Special (assignable) cause variation arises from
  external sources that are not inherent in the
  process, appear sporadically, and disrupt the
  random pattern of common causes.
• Special cause variation accounts for 15 to 20
  percent of observed variation.
• Front-line employees and supervisors have the
  power to identify and solve special causes of
  variation.

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Chapter 16 Quality Control and SPC

• Foundations of Statistical Process Control
• Stable system a system governed only by common
  causes.
• In control if no special causes affect the
  output of the process.
• Out of control when special causes are present
  in the process.

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Chapter 16 Quality Control and SPC

• Constructing Control Charts
• Steps 1 through 4 focus on setting up an initial
  chart in step 5, the charts are used for ongoing
  monitoring and finally, in step 6, the data are
  used for process capability analysis.
• Preparation
• Choose the metric to be monitored.
• Determine the basis, size, and frequency of
  sampling.
• Set up the control chart.

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Chapter 16 Quality Control and SPC

• Constructing Control Charts
• Data collection
• Record the data.
• Calculate relevant statistics averages, ranges,
  proportions, and so on.
• Plot the statistics on the chart.
• Determination of trial control limits
• Draw the center line (process average) on the
  chart.
• Compute the upper and lower control limits.

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Chapter 16 Quality Control and SPC

• Constructing Control Charts
• Analysis and interpretation
• Investigate the chart for lack of control.
• Eliminate out-of-control points.
• Recompute control limits if necessary.
• Use as a problem-solving tool
• Continue data collection and plotting.
• Identify out-of-control situations and take
  corrective action.
• 6. Determination of process capability using
  the control chart data

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Chapter 16 Quality Control and SPC

• Foundations of Statistical Process Control
• A continuous metric is one that is calculated
  from data that are measured as the degree of
  conformance to a specification on a continuous
  scale of measurement.
• A discrete metric is one that is calculated from
  data that are counted.

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Chapter 16 Quality Control and SPC

• Foundations of Statistical Process Control
• SPC uses control charts, run charts to which two
  horizontal lines, called control limits, are
  added the upper control limit (UCL) and lower
  control limit (LCL).
• Control limits are chosen statistically to
  provide a high probability (generally greater
  than 0.99) that points will fall between these
  limits if the process is in control.

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Chapter 16 Quality Control and SPC

• Foundations of Statistical Process Control
• As a problem-solving tool, control charts allow
  employees to identify quality problems as they
  occur. Of course, control charts alone cannot
  determine the source of the problem.

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Chapter 16 Constructing x-bar and R-Charts
16.1
16.2
16.3
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Chapter 16 Quality Control and SPC

• Solved Problem Goodman Tire and Rubber Company
• Goodman Tire periodically tests its tires for
  tread wear under simulated road conditions using
  x- and R-charts.
• Company collects twenty samples, each containing
  three radial tires from different shifts over
  several days of operations.
• x-bar Control Limits
• UCL 31.88 1.02(10.8) 42.9
• LCL 31.88 1.02(10.8) 20.8

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Excel Template for Goodman Tire x-bar and R-Charts
Exhibit 16.1
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Exhibit 16.2
R-Chart for Goodman Tire Example
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Exhibit 16.3
x-Chart for Goodman Tire Example
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Chapter 16 Quality Control and SPC

• Interpreting Patterns in Control Charts
• A process is said to be in control when the
  control chart has the following characteristics
• No points are outside the control limits (the
  traditional and most popular SPC chart
  guideline).
• The number of points above and below the center
  line is about the same.
• The points seem to fall randomly above and below
  the center line.
• Most points, but not all, are near the center
  line, and only a few are close to the control
  limits.

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Chapter 16 Quality Control and SPC

• Interpreting Patterns in Control Charts
• A more in-depth understanding of SPC charts
  includes evaluating the patterns in the sample
  data using guidelines, such as
• 8 points in a row above or below the center line
• 10 of 11 consecutive points above or below the
  center line
• 12 of 14 consecutive points above or below the
  center line
• 2 of 3 consecutive points in the outer one-third
  region between the center line and one of the
  control limits
• 4 of 5 consecutive points in the outer two-thirds
  region between the center line and one of the
  control limits

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Exhibit Extra
Illustration of Some Rules for Identifying
Out-of-Control Conditions
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Chapter 16 Constructing p-charts
16.4
16.5
16.6
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Exhibit 16.4
Data and Calculations for p-Chart Solved Problem
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p-Chart for ZIP Code Reader Solved Problem
withConstant Sample Size
Exhibit 16.5
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Chapter 16 Constructing c-charts

• Constructing c-charts
• Where p-chart monitors the proportion of
  nonconforming items, a c-chart monitors the
  number of nonconformances per unit (i.e., a
  count of the number of defects, errors, failures,
  etc.).
• Example one customers purchase order may have
  several errors, such as wrong items, order
  quantity, or wrong price.

16.7
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Chapter 16 Constructing c-charts

• Constructing c-charts
• These charts are used extensively in service
  organizations.
• To use a c-chart, the size of the sampling unit
  or the number of opportunities for errors remains
  constant.
• Examples of c-chart applications a fender or
  windshield on a certain automobile model, ceramic
  coffee cups all of same size and shape, etc.

16.7
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Exhibit 16.6
Machine Failure Data for c-Chart Solved Problem
The number of machine failures over a 25-day
period.
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Exhibit 16.7
c-Chart for Machine Failures
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Chapter 16 Control Chart Design

• Control Chart Design
• Sample size small sample size keeps costs lower
  however, large sample sizes provide greater
  degrees of statistical accuracy in estimating the
  true state of control.
• Sampling frequency samples should be close
  enough to provide an opportunity to detect
  changes in process characteristics as soon as
  possible and reduce the chances of producing a
  large amount of nonconforming output.

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Chapter 16 Quality Control and SPC

• Other Practical Issues in SPC Implementation
• SPC is a useful methodology for processes that
  operate at a low sigma level (less than or equal
  to 3-sigma).
• However, when the rate of defects is extremely
  low, standard control limits are not so
  effective.
• For processes with a high sigma level (greater
  than 3-sigma), few defects will be discovered
  even with large sample sizes.

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Chapter 16 Quality Control and SPC

• Process Capability
• Process capability is the natural variation in a
  process that results from common causes.
• Cp (UTL LTL) 16.9
• 6s
• Where
• UTL  upper tolerance limit
• LTL lower tolerance limit
• s standard deviation of the process (or an
  estimate based on the sample standard
  deviation, s)

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Chapter 16 Quality Control and SPC

• Process Capability
• Process capability is the natural variation in a
  process that results from common causes.
• When Cp 1, the natural variation is the same as
  the design specification width, as in Exhibit
  16.8(b).
• When Cp lt 1, a significant percentage of output
  will not conform to the specifications as in
  Exhibit 16.8(a).

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Exhibit 16.8
Process Capability versus Design Specifications
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Chapter 16 Quality Control and SPC

• Process Capability
• Cp gt 1, indicates good capability as in Exhibit
  16.8(c) in fact, many firms require Cp values of
  1.66 or greater from their suppliers, which
  equates to a tolerance range of about 10 standard
  deviations.
• The value of Cp does not depend on the mean of
  the process thus, a process may be off-center,
  such as in Exhibit 16.8(d), and still show an
  acceptable value of Cp.

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Exhibit 16.8
Process Capability Versus Design Specifications
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Chapter 16 Quality Control and SPC
One-sided capability indices that consider off-
centered processes Cpu (UTL
µ)/3s 16.10 Cpl (µ LTL)/3s 16.11
Cpk Min (Cpl, Cpu) 16.12 where UTL
 upper tolerance limit LTL lower tolerance
limit µ the mean performance of the
process s standard deviation of the process
(or an estimate based on the sample
standard deviation, s)
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Chapter 16 Solved Problem
A controlled process shows an overall mean of
2.50 and an average range of 0.42. Samples of
size 4 were used to construct the control charts.
Part A What is the process capability? From
Appendix B, d2 2.059, s R/d2 0.42/2.059
0.20. Thus, the process capability is 2.50 ?
3(.020), or 1.90 to 3.10. Part B If
specifications are 2.60 0.25, how well can this
process meet them? Because the specification
range is 2.35 to 2.85 with a target of 2.60, we
may conclude that the observed natural variation
exceeds the specifications by a large amount. In
addition, the process is off-center (see Exhibit
16.9).
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Comparison of Observed Variation and Design
Specifications for Solved Problem
Exhibit 16.9