quality management “ it costs a lot to produce a bad product. ” norman augustine
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Quality Management “ It costs a lot to produce a bad product. ” Norman Augustine. Cost of quality. Prevention costs Appraisal costs Internal failure costs External failure costs Opportunity costs. What is quality management all about?. - PowerPoint PPT PresentationTRANSCRIPT
Quality Management
“It costs a lot to produce a bad product.”Norman Augustine
Cost of quality
1. Prevention costs
2. Appraisal costs
3. Internal failure costs
4. External failure costs
5. Opportunity costs
What is quality management all about?
Try to manage all aspects of the organization in order to excel in all dimensions that are
important to “customers”
Two aspects of quality: features: more features that meet customer needs
= higher qualityfreedom from trouble: fewer defects = higher
quality
The Quality Gurus – Edward Deming
1900-1993
1986
Quality is “uniformity and dependability”
Focus on SPC and statistical tools
“14 Points” for management
PDCA method
The Quality Gurus – Joseph Juran
1904 - 2008
1951
Quality is “fitness for use”
Pareto PrincipleCost of QualityGeneral
management approach as well as statistics
Defining Quality
The totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs
American Society for Quality
MNBQALeadership: How upper management leads the organization, and how the organization leads within the community. Strategic planning: How the organization establishes and plans to implement strategic directions.Customer and market focus: How the organization builds and maintains strong, lasting relationships with customers. Measurement, analysis, and knowledge management: How the organization uses data to support key processes and manage performance.Human resource focus: How the organization empowers and involves its workforce. Process management: How the organization designs, manages and improves key processes.Business/organizational performance results: How the organization performs in terms of customer satisfaction, finances, human resources, supplier and partner performance, operations, governance and social responsibility, and how the organization compares to its competitors.
What does Total Quality Management encompass?
TQM is a management philosophy:• continuous improvement• leadership development• partnership development
CulturalAlignment
Technical Tools
(Process Analysis, SPC,
QFD)
Customer
Developing quality specifications
Input Process Output
Design Design quality
Dimensions of quality
Conformance quality
Quality Improvement
Traditional
Continuous Improvement
Time
Qua
lity
Continuous improvement philosophy
1. Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes.
2. PDCA: Plan-do-check-act as defined by Deming.
Plan Do
Act Check
3. Benchmarking : what do top performers do?
Tools used for continuous improvement
1. Process flowchart
Tools used for continuous improvement
2. Run Chart
Performance
Time
Tools used for continuous improvement
3. Control Charts
Performance Metric
Time
Tools used for continuous improvement
4. Cause and effect diagram (fishbone)
Environment
Machine Man
Method Material
Tools used for continuous improvement
5. Check sheet
Item A B C D E F G
---------------------
√ √ √√ √
√ √
√
√
√ √√ √ √
√√√
√√ √
Tools used for continuous improvement
6. Histogram
Frequency
Tools used for continuous improvement
7. Pareto Analysis
A B C D E F
Freq
uenc
y
Perc
enta
ge
50%
100%
0%
75%
25%102030405060
Six Sigma Quality• A philosophy and set of methods companies use to eliminate defects in their products and processes• Seeks to reduce variation in the processes that lead to product defects• The name “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs 6
Six Sigma Quality
Makes customer
wait
Absent receiving party
Working system of operators
Customer Operator
Fishbone diagram analysis
Absent
Out of office
Not at desk
Lunchtime
Too many phone calls
Absent
Not giving receiving party’s coordinates
Complaining
Leaving a message
Lengthy talk
Does not know organization well
Takes too much time to explain
Does not understand customer
Daily average
Total number
A One operator (partner out of office) 14.3 172
B Receiving party not present 6.1 73
C No one present in the section receiving call 5.1 61
D Section and name of the party not given 1.6 19
E Inquiry about branch office locations 1.3 16
F Other reasons 0.8 10
29.2 351
Reasons why customers have to wait(12-day analysis with check sheet)
Pareto Analysis: reasons why customers have to wait
A B C D E F
Frequency Percentage
0%
49%
71.2%
100
200
300 87.1%
150
250
In general, how can we monitor quality…?
1. Assignable variation: we can assess the cause
2. Common variation: variation that may not be possible to correct (random variation, random noise)
By observingvariation in
output measures!
SPC – suppl ch. 6 Statistical Process Control (SPC)
Control Charts for VariablesThe Central Limit TheoremSetting Mean Chart Limits (x-Charts)Setting Range Chart Limits (R-Charts)Using Mean and Range ChartsControl Charts for AttributesManagerial Issues and Control Charts
Variability is inherent in every processNatural or common causesSpecial or assignable causes
Provides a statistical signal when assignable causes are present
Detect and eliminate assignable causes of variation
Statistical Process Control (SPC)
Samples
To measure the process, we take samples and analyze the sample statistics following these steps
(a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weightFr
eque
ncy
Weight
#
## #
##
##
#
# # ## # ##
# # ## # ## # ##
Each of these represents one
sample of five boxes of cereal
Figure S6.1
Samples
To measure the process, we take samples and analyze the sample statistics following these steps
(b) After enough samples are taken from a stable process, they form a pattern called a distribution
The solid line represents the distribution
Freq
uenc
y
WeightFigure S6.1
Samples
To measure the process, we take samples and analyze the sample statistics following these steps
(c) There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape
Weight
Central tendency
Weight
Variation
Weight
Shape
Freq
uenc
y
Figure S6.1
Samples
To measure the process, we take samples and analyze the sample statistics following these steps
(d) If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable Weight
Time
Freq
uenc
y Prediction
Figure S6.1
Samples
To measure the process, we take samples and analyze the sample statistics following these steps
(e) If assignable causes are present, the process output is not stable over time and is not predicable
WeightTime
Freq
uenc
y Prediction
????
???
???
??????
???
Figure S6.1
Control Charts
Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes
Types of Data
Characteristics that can take any real value
May be in whole or in fractional numbers
Continuous random variables
Variables Attributes Defect-related
characteristics Classify products as
either good or bad or count defects
Categorical or discrete random variables
Process Control
Figure S6.2
Frequency
(weight, length, speed, etc.)Size
Lower control limit Upper control limit
(a) In statistical control and capable of producing within control limits
(b) In statistical control but not capable of producing within control limits
(c) Out of control
Population and Sampling Distributions
Three population distributions
Beta
Normal
Uniform
Distribution of sample means
Standard deviation of the sample means
= x = n
Mean of sample means = x
| | | | | | |
-3x -2x -1x x +1x +2x +3x
99.73% of all xfall within ± 3x
95.45% fall within ± 2x
Figure S6.3
Sampling Distribution
x = m(mean)
Sampling distribution of means
Process distribution of means
Figure S6.4
Control Charts for Variables
For variables that have continuous dimensions Weight, speed, length, strength, etc.
x-charts are to control the central tendency of the process
R-charts are to control the dispersion of the process
These two charts must be used together
Setting Chart Limits
For x-Charts when we know
Upper control limit (UCL) = x + zx
Lower control limit (LCL) = x - zx
where x = mean of the sample means or a target value set for the processz = number of normal standard deviationsx = standard deviation of the sample means
= / n = population standard deviationn = sample size
Setting Control LimitsHour 1
Sample Weight ofNumber Oat Flakes
1 172 133 164 185 176 167 158 179 16
Mean 16.1 = 1
Hour Mean Hour Mean1 16.1 7 15.22 16.8 8 16.43 15.5 9 16.34 16.5 10 14.85 16.5 11 14.26 16.4 12 17.3
n = 9
LCLx = x - zx = 16 - 3(1/3) = 15 ozs
For 99.73% control limits, z = 3
UCLx = x + zx = 16 + 3(1/3) = 17 ozs
17 = UCL
15 = LCL
16 = Mean
Setting Control Limits
Control Chart for sample of 9 boxes
Sample number
| | | | | | | | | | | |1 2 3 4 5 6 7 8 9 10 11 12
Variation due to assignable
causes
Variation due to assignable
causes
Variation due to natural causes
Out of control
Out of control
Setting Chart Limits
For x-Charts when we don’t know
Lower control limit (LCL) = x - A2R
Upper control limit (UCL) = x + A2R
where R = average range of the samplesA2 = control chart factor found in Table S6.1 x = mean of the sample means
Control Chart Factors
Table S6.1
Sample Size Mean Factor Upper Range Lower Range n A2 D4 D3
2 1.880 3.268 03 1.023 2.574 04 .729 2.282 05 .577 2.115 06 .483 2.004 07 .419 1.924 0.0768 .373 1.864 0.1369 .337 1.816 0.184
10 .308 1.777 0.22312 .266 1.716 0.284
Setting Control LimitsProcess average x = 16.01 ouncesAverage range R = .25Sample size n = 5
Setting Control Limits
UCLx = x + A2R= 16.01 + (.577)(.25)= 16.01 + .144= 16.154 ounces
Process average x = 16.01 ouncesAverage range R = .25Sample size n = 5
From Table S6.1
Setting Control Limits
UCLx = x + A2R= 16.01 + (.577)(.25)= 16.01 + .144= 16.154 ounces
LCLx = x - A2R= 16.01 - .144= 15.866 ounces
Process average x = 16.01 ouncesAverage range R = .25Sample size n = 5
UCL = 16.154
Mean = 16.01
LCL = 15.866
R – Chart
Type of variables control chart Shows sample ranges over time
Difference between smallest and largest values in sample
Monitors process variability Independent from process mean
Setting Chart Limits
For R-Charts
Lower control limit (LCLR) = D3R
Upper control limit (UCLR) = D4R
whereR = average range of the samplesD3 and D4 = control chart factors from Table S6.1
Setting Control Limits
UCLR = D4R= (2.115)(5.3)= 11.2 pounds
LCLR = D3R= (0)(5.3)= 0 pounds
Average range R = 5.3 poundsSample size n = 5From Table S6.1 D4 = 2.115, D3 = 0
UCL = 11.2
Mean = 5.3
LCL = 0
Mean and Range Charts
(a)These sampling distributions result in the charts below
(Sampling mean is shifting upward but range is consistent)
R-chart(R-chart does not detect change in mean)
UCL
LCL
Figure S6.5
x-chart(x-chart detects shift in central tendency)
UCL
LCL
Mean and Range Charts
R-chart(R-chart detects increase in dispersion)
UCL
LCL
Figure S6.5
(b)These sampling distributions result in the charts below
(Sampling mean is constant but dispersion is increasing)
x-chart(x-chart does not detect the increase in dispersion)
UCL
LCL
Automated Control Charts
Control Charts for Attributes
For variables that are categoricalGood/bad, yes/no, acceptable/unacceptable
Measurement is typically counting defectives
Charts may measurePercent defective (p-chart)Number of defects (c-chart)
Control Limits for p-Charts
Population will be a binomial distribution, but applying the Central Limit Theorem allows us to
assume a normal distribution for the sample statistics
UCLp = p + zp^
LCLp = p - zp^
where p = mean fraction defective in the samplez = number of standard deviationsp = standard deviation of the sampling distributionn = sample size
^
p(1 - p)np =^
p-Chart for Data EntrySample Number Fraction Sample Number FractionNumber of Errors Defective Number of Errors Defective
1 6 .06 11 6 .062 5 .05 12 1 .013 0 .00 13 8 .084 1 .01 14 7 .075 4 .04 15 5 .056 2 .02 16 4 .047 5 .05 17 11 .118 3 .03 18 3 .039 3 .03 19 0 .00
10 2 .02 20 4 .04Total = 80
(.04)(1 - .04)100p = = .02^p = = .0480
(100)(20)
.11 –
.10 –
.09 –
.08 –
.07 –
.06 –
.05 –
.04 –
.03 –
.02 –
.01 –
.00 –
Sample number
Frac
tion
defe
ctiv
e
| | | | | | | | | |
2 4 6 8 10 12 14 16 18 20
p-Chart for Data EntryUCLp = p + zp = .04 + 3(.02) = .10^
LCLp = p - zp = .04 - 3(.02) = 0^
UCLp = 0.10
LCLp = 0.00
p = 0.04
.11 –
.10 –
.09 –
.08 –
.07 –
.06 –
.05 –
.04 –
.03 –
.02 –
.01 –
.00 –
Sample number
Frac
tion
defe
ctiv
e
| | | | | | | | | |
2 4 6 8 10 12 14 16 18 20
UCLp = p + zp = .04 + 3(.02) = .10^
LCLp = p - zp = .04 - 3(.02) = 0^
UCLp = 0.10
LCLp = 0.00
p = 0.04
p-Chart for Data Entry
Possible assignable causes present
Control Limits for c-Charts
Population will be a Poisson distribution, but applying the Central Limit Theorem allows us to
assume a normal distribution for the sample statistics
where c = mean number defective in the sample
UCLc = c + 3 c LCLc = c - 3 c
c-Chart for Cab Company
c = 54 complaints/9 days = 6 complaints/day
|1
|2
|3
|4
|5
|6
|7
|8
|9
Day
Num
ber d
efec
tive 14 –
12 –10 –8 –6 –4 –2 –0 –
UCLc = c + 3 c= 6 + 3 6= 13.35
LCLc = c - 3 c= 3 - 3 6= 0
UCLc = 13.35
LCLc = 0
c = 6
Patterns in Control Charts
Normal behavior. Process is “in control.”
Upper control limit
Target
Lower control limit
Figure S6.7
Upper control limit
Target
Lower control limit
Patterns in Control Charts
One plot out above (or below). Investigate for cause. Process is “out of control.”
Figure S6.7
Upper control limit
Target
Lower control limit
Patterns in Control Charts
Trends in either direction, 5 plots. Investigate for cause of progressive change.Figure S6.7
Upper control limit
Target
Lower control limit
Patterns in Control Charts
Two plots very near lower (or upper) control. Investigate for cause.Figure S6.7
Upper control limit
Target
Lower control limit
Patterns in Control Charts
Run of 5 above (or below) central line. Investigate for cause. Figure S6.7
Upper control limit
Target
Lower control limit
Patterns in Control Charts
Erratic behavior. Investigate.
Figure S6.7
Which Control Chart to Use
Using an x-chart and R-chart:Observations are variablesCollect 20 - 25 samples of n = 4, or n = 5, or
more, each from a stable process and compute the mean for the x-chart and range for the R-chart
Track samples of n observations each
Variables Data
Which Control Chart to Use
Using the p-chart:Observations are attributes that can be
categorized in two states We deal with fraction, proportion, or percent
defectivesHave several samples, each with many
observations
Attribute Data
Which Control Chart to Use
Using a c-Chart:Observations are attributes whose defects per
unit of output can be countedThe number counted is often a small part of
the possible occurrencesDefects such as number of blemishes on a
desk, number of typos in a page of text, flaws in a bolt of cloth
Attribute Data