Download Introduction to Quality

Assist. Prof. Dr. Benhür SATIR
02/12/2014
Outline
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Variation
Dimensions of Quality
Definition of Quality
Descriptive Statistics
Statistical Methods for Quality Improvement:
 Acceptance Sampling
 Designed Experiments
 Statistical Process Control & Magnificient Seven
 Total Quality Management
 Quality Related Costs
 Benefits
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Variation…
 Which has higher variation?
İmam Çağdaş
McDonalds
 What do you understand from this question?
 # of different meals
 the same taste for a specific meal
 Something else?
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Variation…
 Which one you like more?
İmam Çağdaş
McDonalds
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Variation…
 Which one you like more as an IE?
İmam Çağdaş
McDonalds
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Variation…
 Variation is an enemy for an IE…
 We hate variation!
 Ford T Model: Henry Ford said "You can have any
colour as long as it's black."
 If it is unavoidable, try to cope with it…
 Only black color for a Mercedes in 2015?
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Dimensions of Quality
1.
Garvin (1987)
Performance:

Will the product/service do the intended job?
2. Reliability:
 How often does the product/service fail?
3. Durability:
 How long does the product/service last?
4. Serviceability:
 How easy to repair the product / to solve the problems
in service?
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Dimensions of Quality
5. Aesthetics:
 What does the product/service
look/smell/sound/feel like?
6. Features:
 What does the product do/ service give?
7. Perceived Quality:
 What is the reputation of the company or its
products/services?
8. Conformance to Standards:
 Is the product/service made exactly as the
designer/standard intended?
9. What else? What do YOU think?
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Quality in Different Areas of
Society
Area
Examples
Airlines
On-time, comfortable, low-cost service
Food Services
Good product, fast delivery, good environment
Postal Services
fast delivery, correct delivery, cost containment
Consumer Products
Properly made, defect-free, cost effective
Insurance
Payoff on time, reasonable cost
Automotive
Defect-free
Communications
Clearer, faster, cheaper service
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Definition of Quality
 No two products are identical; i.e. There is always a certain
amount of variability.
 Modern Defn : Quality is inversely proportional to
variability.
 Quality Improvement : reduction of variability in
processes and products.
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Quality Engineering Terminology
 Quality characteristics : parameters that jointly describe the quality
from customers’ view point.
 Physical : length, weight, viscocity.
 Sensory : taste, color, appearance.
 Time orientation : reliability, durability, serviceability.
 Nominal (Target) Value : Desired value for a quality characteristic.
 Upper Specification Limit (USL) : largest allowable value for a
quality characteristic that will not influence the funciton or
performance of theproduct.
 Lower Specification Limit (LSL) : Similar to USL, it is the smallest
allowable value.
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Quality Engineering Terminology
 Nonconformity : Specific type of failure.
 Failure : Fail to meet the specification.
 Defect : Nonconformities that are serious enough to
significantly affect the safe or effective use of the
product.
 Defective: A product is defective if it has one or more
defects.
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Quality Engineering Terminology
 Quality Engineering : Set of operational, managerial and
engineering applications to ensure that the quality
characteristics are at their corresponding nominal values or
required levels.
 Remember : There is always variability and quality is
inversely proportional to it.
 Only way of describing variability : Statistics.
 Use of statistical methods are crucial in quality
improvements.
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Some Important Statistical
Definitions
Population
Sample
Use statistics to
summarize features
Use parameters to
summarize features
Inference on the population from the sample
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Some Important Statistical
Definitions
 A Population (Universe) is the whole collection of things
under consideration.
 A Sample is a portion of the population selected for
analysis.
 A Parameter is a summary measure computed to describe a
characteristic of the population.
 A Statistic is a summary measure computed to describe a
characteristic of the sample.
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Summary Measures
Mean
Central
Tendency
Summary
Measures
Median
Mode
Quartile
Range
Variation
Variance
Standard
Devation
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Mean
 Population Mean: µ (parameter)
 For a finite population with N measurements, the
population mean is
N

x
i 1
i
N
 A reasonable estimate of the population mean is the
sample mean.
 Sample Mean: x (statistic)
 If the n observations in a sample are denoted by x1, x2, …, xn,
n
the sample mean is
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x
x
i 1
n
i
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Mean
 Example: Suppose that an engineer is developing a rubber
compound for use in O-rings. The O-rings are to be
employed as seals in plasma etching tools used in the
semiconductor industry, so their resistance to acids and
other corrosive substances is an important characteristic.
The data from the modified rubber compound are:
1037 1047 1066 1048 1059 1073 1070 1040.
The sample mean strength (psi) for the eight observations
on strength is
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Mean
 Sample mean is affected by the extreme values and/or
outliers.
0 1 2 3 4 5 6 7 8 9 10
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0 1 2 3 4 5 6 7 8 9 10 12 14
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Median
 Robust measure of central tendency
 Not affected by extreme values
 In an ordered array, the median is the ‘middle’
number
 If n or N is odd, the median is the middle number
 If n or N is even, the median is the average of the 2
middle numbers
0 1 2 3 4 5 6 7 8 9 10
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0 1 2 3 4 5 6 7 8 9 10 12 14
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Median
 Example : Consider the O-rings example.
1037 1047 1066 1048 1059 1073 1070 1040.
To find the median,
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Mode
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A Measure of central tendency
Value that occurs most often
Not affected by extreme values
There may not be a mode
There may be several modes
Used for either numerical or categorical data
1 2 3 4 5 6 7 8 9 10 11 12 13 14
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Summary Measures
Data A
Mean = 15.5
11 12 13 14 15
16 17
18 19 20 21
Data B
Mean = 15.5
11 12 13
14 15 16 17 18 19 20 21
Data C
11 12 13 14 15 16 17
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19 20 21
Mean = 15.5
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Range
 Measure of variation
 Difference between the largest and the smallest
observations:
Range  X Largest  X Smallest
 Ignores how data are distributed
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9
10
11
12
7
8
9
10
11 12
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Sample Variance & Standard
Deviation
 Most important measure of variation
 Shows variation about the mean
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Sample Variance & Standard
Deviation
 If the n observations in a sample are denoted by x1,
x2,…, xn, then the sample variance is
n
s2 
 x  x 
2
i
i 1
n 1
 The sample standard deviation, s, is the positive square
root of the sample variance.
s2  s
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Sample Variance & Standard
Deviation
 Example : Consider the O-rings example.
1037 1047 1066 1048 1059 1073 1070 1040.
i
xi
1
2
3
4
5
6
7
8
1037
1047
1066
1048
1059
1073
1070
1040
x  1055
8440
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Sample Variance & Standard
Deviation
Data A
Mean = 15.5
11 12 13 14 15
16 17
18 19 20 21
s = 3.338
Data B
Mean = 15.5
11 12 13
14 15 16 17 18 19 20 21
s = .9258
Data C
Mean = 15.5
11 12 13 14 15 16 17
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19 20 21
s = 4.57
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Histogram
 The most commonly used graph to show frequency
distributions, i.e. how often each different value in a
set of data occurs .
 Used to visualize the distribution.
 Birthdate example
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Pareto Chart
 Organizes and displays information to show the relative
importance of various problems or causes of problems.
 A special form of a vertical bar chart that puts items in order
(from the highest to the lowest) relative to some measurable
effect of interest: frequency, cost, time.
 Are arranged with longest bars on the left and the shortest to the
right.
 Helps teams to focus efforts where they can have the greatest
potential impact.
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Example Pareto Chart
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Cause & Effect Diagram
 Also called Ishikawa diagram, fishbone diagram.
 Understand the root causes of a problem BEFORE you put
a “solution” into place.
 Identify and display many different possible causes for a
problem.
 See the relationships between the many causes.
 Helps determine which data to collect.
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Cause & Effect Diagram
• Clearly define the focused
problem.
Root cause
• Use brainstorming to identify
Root cause
possible causes.
• Sort causes into reasonable
clusters (no less than 3, not more
than 6).
Focused
problem
• Label the clusters (consider
people, policies, procedures,
materials if you have not already
identified labels).
• Develop and arrange bones in
Root cause
Root cause
each cluster.
• Check the logical validity of
each causal chain.
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Cause & Effect Diagram
• Bones should not include solutions.
• Bones should not include lists of process steps.
• Bones include the possible causes.
Materials
Lack of office
space
Policies
Restrictive budget
Escorting clients to
appointments and
having to wait
Procedures
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Minimal
benefits
Location
No policy on staff
screening
“Back-biting”
environment
Paperwork Burnout
overwhelming
Turnover in
staff
Lack of
supervision
Inadequate
training
People
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Statistical Methods for Quality
Improvement
 3 major areas:
 Acceptance Sampling
 Statistical Process Control (SPC)
 Design of Experiments
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Acceptance Sampling
 Inspection and testing of
 Raw materials
 Semifinished products
 Finished products
 Based on inspection
 Accept or
 Reject the product
 Type of inspection procedure is called acceptance sampling.
 Can do either 100% inspection, or inspect a sample of a few items taken
from the lot.
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Statistical Process Control
 SPC is a statistics-based methodology for achieving process stability and
improving capability by reducing variability.
 All processes have variation in output:
 Some of the variation is caused by factors that can be identified and
managed (assignable causes). Ex: improperly adjusted machines,
operator errors, defective raw materials etc.
 Some of the variation is inherent in the process (background noise) :
cumulative effect of many small, unavoidable causes. Also named as
chance causes of variation.
 A process is said to be in statistical control, if only chance causes of
variation is present and it is out of control, if there are assignable causes
of variation.
 SPC is aimed at discovering variation resulting from assignable causes so
that adjustments can be made and “bad” output is not produced.
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Control Chart
 A control chart is a presentation of data in which the
control values are plotted against time.
 Used to study how a process changes over time and to
determine if variation is chance or assignable cause.
 Immediate visualisation of problems.
 Control charts have a central line, upper and lower warning
limits, and upper and lower action limits.
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Control chart - Illustration of construction
Central line
X-chart
Copper
Action limit
Warning limit
1.3
1.2
1.1
1.0
0.9
0.8
0
10
20
30
40
50
60
70
80
90
100
Control value
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Design of Experiments
 Helpful in discovering the key variables influencing
the quality characteristics of interest.
 Systematically change the controllable factors in the
process and determine the effect of them on the
output product parameters.
 Statistically designed experiments are useful to reduce
the variability in the quality characteristics and to
determine the levels of controllable factors that
optimize process performance.
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TQM
 Consists of organization-wide efforts to install and
make permanent a climate in which
an organization continuously improves its ability to
deliver high-quality products and services to
customers.
 W. Edwards Deming
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Deming’s 14 Points for the
Transformation of Management
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1. Create constancy of purpose toward improvement of product and service, with the aim to become competitive and to stay in business, and
to provide jobs.
2. Adopt the new philosophy. We are in a new economic age. Western management must awaken to the challenge, must learn their
responsibilities, and take on leadership for change.
3. Cease dependence on inspection to achieve quality. Eliminate the need for inspection on a mass basis by building quality into the product
in the first place.
4. End the practice of awarding business on the basis of price tag. Instead, minimize total cost. Move toward a single supplier for any one
item, on a long-term relationship of loyalty and trust.
5. Improve constantly and forever the system of production and service, to improve quality and productivity, and thus constantly decrease
costs.
6. Institute training on the job.
7. Institute leadership (see Point 12 and Ch. 8). The aim of supervision should be to help people and machines and gadgets to do a better job.
Supervision of management is in need of overhaul, as well as supervision of production workers.
8. Drive out fear, so that everyone may work effectively for the company (see Ch. 3).
9. Break down barriers between departments. People in research, design, sales, and production must work as a team, to foresee problems of
production and in use that may be encountered with the product or service.
10. Eliminate slogans, exhortations, and targets for the work force asking for zero defects and new levels of productivity. Such exhortations
only create adversarial relationships, as the bulk of the causes of low quality and low productivity belong to the system and thus lie beyond
the power of the work force.
Eliminate work standards (quotas) on the factory floor. Substitute leadership.
Eliminate management by objective. Eliminate management by numbers, numerical goals. Substitute leadership.
11. Remove barriers that rob the hourly worker of his right to pride of workmanship. The responsibility of supervisors must be changed from
sheer numbers to quality.
12. Remove barriers that rob people in management and in engineering of their right to pride of workmanship. This means, inter alia,
abolishment of the annual or merit rating and of management by objective (see Ch. 3).
13. Institute a vigorous program of education and self-improvement.
14. Put everybody in the company to work to accomplish the transformation. The transformation is everybody's job.
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Deming’s 7 Deadly Diseases
 1. Lack of constancy of purpose to plan product and service that
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will have a market and keep the company in business, and
provide jobs.
2. Emphasis on short-term profits: short-term thinking (just the
opposite from constancy of purpose to stay in business), fed by
fear of unfriendly takeover, and by push from bankers and
owners for dividends.
3. Evaluation of performance, merit rating, or annual review.
4. Mobility of management; job hopping.
5. Management by use only of visible figures, with little or no
consideration of figures that are unknown or unknowable.
6. Excessive medical costs.
7. Excessive costs of liability, swelled by lawyers that work on
contingency fees.
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Deming’s Circle
 PDSA: plan–do–study–act
 OPDCA: observation-PDSA
 PDCA: plan–do–check–act or plan–do–check–adjust
 Iterative four-step management method used in
business for the control and continuous improvement
of processes and products.
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Quality-Related Costs
 Prevention costs
 Appraisal costs
 Correction costs:
 Internal Failure Costs
 External Failure Costs
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Costs of Conformance
i.e. : The cost of doing things
right the first time
Costs of Non-Conformance
i.e. : The cost incurred as a
result of things not being
done right the first time
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Benefits
Internal Benefits
Reduces costs
Increases dependability
Increases speed
Boosts morale
Increases customer retention
Increases profit
External Benefits
Customer gets correct
product or service
Correct specifications
Appropriate intangibles
Customer satisfaction
Customer retention
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