Statisticalqualitycontrol

Learning Objectives
 Describe Categories of SQC
 Explain the use of descriptive statistics
in measuring quality characteristics
 Identify and describe causes of
variation
 Describe the use of control charts
 Identify the differences between x-bar,
R-, p-, and c-charts

Learning Objectives -
continued
 Explain process capability and process
capability index
 Explain the concept six-sigma
 Explain the process of acceptance sampling
and describe the use of OC curves
 Describe the challenges inherent in
measuring quality in service organizations
© Wiley 2007

Three SQC Categories
 Statistical quality control (SQC) is the term used to describe
the set of statistical tools used by quality professionals
 SQC encompasses three broad categories of;
© Wiley 2007
 Descriptive statistics
 e.g. the mean, standard deviation, and range
 Statistical process control (SPC)
 Involves inspecting the output from a process
 Quality characteristics are measured and charted
 Helpful in identifying in-process variations
 Acceptance sampling used to randomly inspect a batch of goods to
determine acceptance/rejection
 Does not help to catch in-process problems

Sources of Variation
 Variation exists in all processes.
 Variation can be categorized as either;
 Common or Random causes of variation, or
 Random causes that we cannot identify
© Wiley 2007
 Unavoidable
 e.g. slight differences in process variables like diameter, weight, service
time, temperature
 Assignable causes of variation
 Causes can be identified and eliminated
 e.g. poor employee training, worn tool, machine needing repair

Traditional Statistical Tools
© Wiley 2007
 Descriptive Statistics
include
 The Mean- measure of central
tendency
 The Range- difference
between largest/smallest
observations in a set of data
 Standard Deviation
measures the amount of data
dispersion around mean
 Distribution of Data shape
 Normal or bell shaped or
 Skewed

x
i 
 n
x
n
i 1
 x 
X

n 1
σ
n
i 1
2
i





Distribution of Data
 Normal distributions  Skewed distribution
© Wiley 2007

SPC Methods-Control Charts
 Control Charts show sample data plotted on a graph with CL,
© Wiley 2007
UCL, and LCL
 Control chart for variables are used to monitor characteristics
that can be measured, e.g. length, weight, diameter, time
 Control charts for attributes are used to monitor characteristics
that have discrete values and can be counted, e.g. % defective,
number of flaws in a shirt, number of broken eggs in a box

Setting Control Limits
© Wiley 2007
 Percentage of values
under normal curve
 Control limits balance
risks like Type I error

Control Charts for Variables
© Wiley 2007
 Use x-bar and R-bar
charts together
 Used to monitor
different variables
 X-bar & R-bar Charts
reveal different
problems
 In statistical control on
one chart, out of control
on the other chart? OK?

Control Charts for Variables
 Use x-bar charts to monitor the
changes in the mean of a process
(central tendencies)
 Use R-bar charts to monitor the
dispersion or variability of the process
 System can show acceptable central
tendencies but unacceptable variability or
 System can show acceptable variability
but unacceptable central tendencies
© Wiley 2007

Constructing a X-bar Chart: A quality control inspector at the Cocoa
Fizz soft drink company has taken three samples with four observations
each of the volume of bottles filled. If the standard deviation of the
bottling operation is .2 ounces, use the below data to develop control
charts with limits of 3 standard deviations for the 16 oz. bottling operation.
 Center line and control
limit formulas
1 2 n
σ
where ( ) is the # of sample means and (n)
is the #of observations w/in each sample
 
UCL x zσ
x x
LCL x zσ
x x
© Wiley 2007
n
, σ
x x ...x
x x
 

 

k
k
Time 1 Time 2 Time 3
Observation 1 15.8 16.1 16.0
Observation 2 16.0 16.0 15.9
Observation 3 15.8 15.8 15.9
Observation 4 15.9 15.9 15.8
Sample
means (X-bar)
15.875 15.975 15.9
Sample
ranges (R)
0.2 0.3 0.2

Solution and Control Chart (x-bar)
 Center line (x-double bar):
15.92
 
15.875 15.975 15.9
x 
 Control limits for±3σ limits:
.2
.2




© Wiley 2007
3

15.62
4
x x
LCL x zσ 15.92 3
16.22
4
UCL x zσ 15.92 3
x x
  

 

   
  

 

   

X-Bar Control Chart
© Wiley 2007

Control Chart for Range (R)
Sample Size
(n)
© Wiley 2007
 Center Line and Control Limit
formulas:
 Factors for three sigma control limits
.233
 
0.2 0.3 0.2
3
R

UCL D 4
R 2.28(.233) .53
R
  
LCL R
D 3
R 0.0(.233) 0.0
  

Factor for x-Chart
Factors for R-Chart
A2 D3 D4
2 1.88 0.00 3.27
3 1.02 0.00 2.57
4 0.73 0.00 2.28
5 0.58 0.00 2.11
6 0.48 0.00 2.00
7 0.42 0.08 1.92
8 0.37 0.14 1.86
9 0.34 0.18 1.82
10 0.31 0.22 1.78
11 0.29 0.26 1.74
12 0.27 0.28 1.72
13 0.25 0.31 1.69
14 0.24 0.33 1.67
15 0.22 0.35 1.65

R-Bar Control Chart
© Wiley 2007

Second Method for the X-bar Chart Using
R-bar and the A2 Factor (table 6-1)
 Use this method when sigma for the process
distribution is not know
 Control limits solution:
 
.233
0.2 0.3 0.2
    
UCL x A R 15.92 0.73 .233 16.09
x 2
    
LCL x A R 15.92 0.73.233 15.75
© Wiley 2007
3
R
x 2

 


Control Charts for Attributes –
P-Charts & C-Charts
 Attributes are discrete events; yes/no,
© Wiley 2007
pass/fail
 Use P-Charts for quality characteristics that are
discrete and involve yes/no or good/bad decisions
 Number of leaking caulking tubes in a box of 48
 Number of broken eggs in a carton
 Use C-Charts for discrete defects when there can be
more than one defect per unit
 Number of flaws or stains in a carpet sample cut from a
production run
 Number of complaints per customer at a hotel

P-Chart Example: A Production manager for a tire company has
inspected the number of defective tires in five random samples
with 20 tires in each sample. The table below shows the number of
defective tires in each sample of 20 tires. Calculate the control
limits.
.09
9
   
100
0.64
#Defectives
Total Inspected
(.09)(.91)
 
20
CL p

p(1 p)
n
σ
    
UCL p
p z σ .09 3(.064) .282
© Wiley 2007
Sample Number
of
Defective
Tires
Number of
Tires in
each
Sample
Proportion
Defective
1 3 20 .15
2 2 20 .10
3 1 20 .05
4 2 20 .10
5 2 20 .05
Total 9 100 .09
 Solution:
 
LCL p
p zσ .09 3(.064) .102 0
p
      


P- Control Chart
© Wiley 2007

C-Chart Example: The number of weekly customer
complaints are monitored in a large hotel using a
c-chart. Develop three sigma control limits using the
data table below.
2.2
22
  
10
#complaints
# of samples
CL
    
UCL c z
c 2.2 3 2.2 6.65
© Wiley 2007
Week Number of
Complaints
1 3
2 2
3 3
4 1
5 3
6 3
7 2
8 1
9 3
10 1
Total 22
 Solution:
c
LCL c
c c 2.2 3 2.2 2.25 0
  z
    

C- Control Chart
© Wiley 2007

Process Capability

USL LSL
specificat ion width
 
  
μ LSL
,
USL μ
© Wiley 2007
 Product Specifications
 Preset product or service dimensions, tolerances
 e.g. bottle fill might be 16 oz. ±.2 oz. (15.8oz.-16.2oz.)
 Based on how product is to be used or what the customer expects
 Process Capability – Cp and Cpk
 Assessing capability involves evaluating process variability relative to
preset product or service specifications
 Cp assumes that the process is centered in the specification range
6σ
process width
Cp
 Cpk helps to address a possible lack of centering of the process




3σ
3σ
Cpk min

Relationship between Process
Variability and Specification Width
 Three possible ranges for Cp
 Cp = 1, as in Fig. (a), process
© Wiley 2007
variability just meets
specifications
 Cp ≤ 1, as in Fig. (b), process not
capable of producing within
specifications
 Cp ≥ 1, as in Fig. (c), process
exceeds minimal specifications
 One shortcoming, Cp assumes
that the process is centered on
the specification range
 Cp=Cpk when process is centered

Computing the Cp Value at Cocoa Fizz: three bottling
machines are being evaluated for possible use at the Fizz plant.
The machines must be capable of meeting the design
specification of 15.8-16.2 oz. with at least a process
capability index of 1.0 (Cp≥1)

Cp  
© Wiley 2007
 The table below shows the information
gathered from production runs on each
machine. Are they all acceptable?
 Solution:
 Machine A
USL LSL
 Machine B
Cp=
 Machine C
Cp=
Machine σ USL-LSL 6σ
A .05 .4 .3
B .1 .4 .6
C .2 .4 1.2
1.33
.4
6(.05)
6σ

Computing the Cpk Value at Cocoa Fizz
 Design specifications call for a
target value of 16.0 ±0.2 OZ.
(USL = 16.2 & LSL = 15.8)
 Observed process output has now
shifted and has a μ of 15.9 and a
σ of 0.1 oz.
.1
  
15.9 15.8
,
16.2 15.9

 Cpk is less than 1, revealing that
the process is not capable
© Wiley 2007
.33
.3
Cpk
3(.1)
3(.1)
Cpk min
 
 

 



±6 Sigma versus ± 3 Sigma
 Motorola coined “six-sigma” to
© Wiley 2007
describe their higher quality
efforts back in 1980’s
 Six-sigma quality standard is
now a benchmark in many
industries
 Before design, marketing ensures
customer product characteristics
 Operations ensures that product
design characteristics can be met
by controlling materials and
processes to 6σ levels
 Other functions like finance and
accounting use 6σ concepts to
control all of their processes
 PPM Defective for ±3σ
versus ±6σ quality

Acceptance Sampling
 Definition: the third branch of SQC refers to the
process of randomly inspecting a certain number of
items from a lot or batch in order to decide whether to
accept or reject the entire batch
 Different from SPC because acceptance sampling is
performed either before or after the process rather
than during
 Sampling before typically is done to supplier material
 Sampling after involves sampling finished items before shipment
or finished components prior to assembly
 Used where inspection is expensive, volume is high, or
inspection is destructive
© Wiley 2007

Acceptance Sampling Plans
 Goal of Acceptance Sampling plans is to determine the criteria
for acceptance or rejection based on:
© Wiley 2007
 Size of the lot (N)
 Size of the sample (n)
 Number of defects above which a lot will be rejected (c)
 Level of confidence we wish to attain
 There are single, double, and multiple sampling plans
 Which one to use is based on cost involved, time consumed, and cost of
passing on a defective item
 Can be used on either variable or attribute measures, but more
commonly used for attributes

Operating Characteristics (OC)
Curves
 OC curves are graphs which show
© Wiley 2007
the probability of accepting a lot
given various proportions of
defects in the lot
 X-axis shows % of items that are
defective in a lot- “lot quality”
 Y-axis shows the probability or
chance of accepting a lot
 As proportion of defects
increases, the chance of
accepting lot decreases
 Example: 90% chance of
accepting a lot with 5%
defectives; 10% chance of
accepting a lot with 24%
defectives

AQL, LTPD, Consumer’s Risk (α)
& Producer’s Risk (β)
© Wiley 2007
 AQL is the small % of defects that
consumers are willing to accept;
order of 1-2%
 LTPD is the upper limit of the
percentage of defective items
consumers are willing to tolerate
 Consumer’s Risk (α) is the chance
of accepting a lot that contains a
greater number of defects than the
LTPD limit; Type II error
 Producer’s risk (β) is the chance a
lot containing an acceptable quality
level will be rejected; Type I error

Developing OC Curves
 OC curves graphically depict the discriminating power of a sampling plan
 Cumulative binomial tables like partial table below are used to obtain
probabilities of accepting a lot given varying levels of lot defectives
 Top of the table shows value of p (proportion of defective items in lot), Left
hand column shows values of n (sample size) and x represents the cumulative
number of defects found
Table 6-2 Partial Cumulative Binomial Probability Table (see Appendix C for complete table)
Proportion of Items Defective (p)
.05 .10 .15 .20 .25 .30 .35 .40 .45 .50
n x
5 0 .7738 .5905 .4437 .3277 .2373 .1681 .1160 .0778 .0503 .0313
Pac 1 .9974 .9185 .8352 .7373 .6328 .5282 .4284 .3370 .2562 .1875
AOQ .0499 .0919 .1253 .1475 .1582 .1585 .1499 .1348 .1153 .0938
© Wiley 2007

Example 6-8 Constructing an OC Curve
© Wiley 2007
 Lets develop an OC curve for a
sampling plan in which a sample
of 5 items is drawn from lots of
N=1000 items
 The accept /reject criteria are set
up in such a way that we accept a
lot if no more that one defect
(c=1) is found
 Using Table 6-2 and the row
corresponding to n=5 and x=1
 Note that we have a 99.74%
chance of accepting a lot with 5%
defects and a 73.73% chance
with 20% defects

Average Outgoing Quality (AOQ)
© Wiley 2007
 With OC curves, the higher the quality of
the lot, the higher is the chance that it will
be accepted
 Conversely, the lower the quality of the lot,
the greater is the chance that it will be
rejected
 The average outgoing quality level of the
product (AOQ) can be computed as follows:
AOQ=(Pac)p
 Returning to the bottom line in Table 6-2,
AOQ can be calculated for each proportion
of defects in a lot by using the above
equation
 This graph is for n=5 and x=1 (same
as c=1)
 AOQ is highest for lots close to 30%
defects

Implications for Managers
 How much and how often to inspect?
 Consider product cost and product volume
 Consider process stability
© Wiley 2007
 Consider lot size
 Where to inspect?
 Inbound materials
 Finished products
 Prior to costly processing
 Which tools to use?
 Control charts are best used for in-process production
 Acceptance sampling is best used for inbound/outbound

SQC in Services
 Service Organizations have lagged behind
manufacturers in the use of statistical quality control
 Statistical measurements are required and it is more
difficult to measure the quality of a service
 Services produce more intangible products
 Perceptions of quality are highly subjective
 A way to deal with service quality is to devise
quantifiable measurements of the service element
© Wiley 2007
 Check-in time at a hotel
 Number of complaints received per month at a restaurant
 Number of telephone rings before a call is answered
 Acceptable control limits can be developed and charted

Service at a bank: The Dollars Bank competes on customer service and
is concerned about service time at their drive-by windows. They recently
installed new system software which they hope will meet service
specification limits of 5±2 minutes and have a Capability Index (Cpk) of
at least 1.2. They want to also design a control chart for bank teller use.
 They have done some sampling recently (sample size of 4
customers) and determined that the process mean has
shifted to 5.2 with a Sigma of 1.0 minutes.
USL LSL
Cp 
1.8
1.0


  
7.0 5.2
,
5.2 3.0

 Control Chart limits for ±3 sigma limits


3 5.0 zσ X UCL x x     


3 5.0 zσ X LCL x x     
© Wiley 2007
1.2
1.5
Cpk
3(1/2)
3(1/2)
Cpk min
 
 

 


1.33
4
6
7 - 3
6σ
 

 



5.0 1.5 6.5 minutes
1
4

 

   
5.0 1.5 3.5 minutes
1
4

 

   

SQC Across the Organization
 SQC requires input from other organizational
functions, influences their success, and are actually
used in designing and evaluating their tasks
 Marketing – provides information on current and future
© Wiley 2007
quality standards
 Finance – responsible for placing financial values on SQC
efforts
 Human resources – the role of workers change with SQC
implementation. Requires workers with right skills
 Information systems – makes SQC information accessible for
all.

Chapter 6 Highlights
 SQC refers to statistical tools t hat can be sued by quality
professionals. SQC an be divided into three categories:
traditional statistical tools, acceptance sampling, and
statistical process control (SPC).
 Descriptive statistics are sued to describe quality
characteristics, such as the mean, range, and variance.
Acceptance sampling is the process of randomly inspecting
a sample of goods and deciding whether to accept or
reject the entire lot. Statistical process control involves
inspecting a random sample of output from a process and
deciding whether the process in producing products with
characteristics that fall within preset specifications.
© Wiley 2007

Chapter 6 Highlights -
continued
 Two causes of variation in the quality of a product or process:
common causes and assignable causes. Common causes of variation
are random causes that we cannot identify. Assignable causes of
variation are those that can be identified and eliminated.
 A control chart is a graph used in SPC that shows whether a sample of
data falls within the normal range of variation. A control chart has
upper and lower control limits that separate common from assignable
causes of variation. Control charts for variables monitor
characteristics that can be measured and have a continuum of values,
such as height, weight, or volume. Control charts fro attributes are
used to monitor characteristics that have discrete values and can be
counted.
© Wiley 2007

continued
 Control charts for variables include x-bar and R-charts. X-bar
charts monitor the mean or average value of a product
characteristic. R-charts monitor the range or dispersion of
the values of a product characteristic. Control charts for
attributes include p-charts and c-charts. P-charts are used
to monitor the proportion of defects in a sample, C-charts
are used to monitor the actual number of defects in a
sample.
 Process capability is the ability of the production process
to meet or exceed preset specifications. It is measured by
the process capability index Cp which is computed as the
ratio of the specification width to the width of the process
variable.
© Wiley 2007

continued
 The term Six Sigma indicates a level of quality in
which the number of defects is no more than 2.3
parts per million.
 The goal of acceptance sampling is to determine
criteria for the desired level of confidence.
Operating characteristic curves are graphs that
show the discriminating power of a sampling plan.
 It is more difficult to measure quality in services
than in manufacturing. The key is to devise
quantifiable measurements for important service
dimensions.
© Wiley 2007

The End
 Copyright © 2007 John Wiley & Sons, Inc. All rights reserved.
Reproduction or translation of this work beyond that permitted
in Section 117 of the 1976 United State Copyright Act without
the express written permission of the copyright owner is
unlawful. Request for further information should be addressed
to the Permissions Department, John Wiley & Sons, Inc. The
purchaser may make back-up copies for his/her own use only
and not for distribution or resale. The Publisher assumes no
responsibility for errors, omissions, or damages, caused by the
use of these programs or from the use of the information
contained herein.
© Wiley 2007

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