The file explores the quality control from a scientific calculation point of view

1. Quality Control
Course material

2. Introduction
• Quality means fitness for use. Controlling and improving quality has become an
important business strategy for many organization; manufacturers, distributors,
transportation companies, financial services organizations; health care providers,
and government agencies. Quality is a competitive advantage. A business that can
delight customers by improving and controlling quality can dominate its
competitors.
• This course is about the technical methods for achieving success in quality control
and improvement, and offers guidance on how to successfully implement these
method. Quality always has been an integral part of virtually all products and
services.

3. Introduction
• However, our awareness of its importance and the introduction of formal
methods for quality control and improvement have been an evolutionary
development. This course discuss about the basic definitions of quality and
quality improvement, provides a brief overview of the tools and methods
and discusses the management systems for quality improvement
methodology and overview the statistical tools essential for modern
professional practice.
• A brief discussion of some management and business aspects for
implementing quality improvement is also given.

4. Course Objectives
• Define and discuss quality and quality improvement.
• Discuss the different dimensions of quality.
• Discuss the evolution of modern quality improvement methods.
• Discuss the role that variability and statistical methods play in controlling and
improving quality.
• Discuss total quality management, the Malcolm Baldrige National Quality Award,
six-sigma, and quality systems and standards.
• Explain the links between quality and productivity and between quality and cost.

5. Course Objectives
• Discuss product liability.
• Discuss the three functions:
quality planning, quality
assurance, and quality control
and improvement.
Examples of Quality Product
Product Quality parameter
Nitrogen fertilizer Nitrogen content
City air Carbon monoxide
Petrol Sulfur content
Food Contaminants as pesticides present

6. History of Quality Control
• Statistical Quality Control (SQC) established along with a mass production structure
in USA in the 20th century. F.W. Taylor introduced some principles of scientific
management as mass production industries began to develop prior to 1900. Taylor
pioneered dividing work into tasks so that the product could be manufactured and
assembled more easily. Since 1980, there has been a profound growth in the use of
statistical methods for quality and overall business improvement in the US. This has
been motivated, in part, by the widespread loss of business and markets suffered by
many domestic companies that began to during the 1970s. Various management
systems have also emerged as frameworks in which to implement quality
improvement.

7. Quality and Quality Improvement
• Quality can be defined due to several
concepts, as relating to one or more
desirable characteristics that a product or
service should possess. Although this
concept is certain useful but more precise
definition is needed. Quality has become
one of the most important consumer
decision factors in the selection among
products and services.
Two analytical tools are used to
determine the quality of products:
• Qualitative Analysis:
The components of a sample.
• Quantitative Analysis:
How much of each component in a
sample.

8. The Importance of Quality
• The phenomenon is widespread, regardless of whether the consumer is an
individual, an industrial organization or any service/product provider.
Consequently, understanding and improving quality are key factors leading to
business success, growth, and enhanced competitiveness. There is substantial
return on investment from improved quality and from successfully
employing quality as an integral part of overall business strategy.

9. Dimensions of Quality
• The quality of a product can be described and evaluated in several ways. It is often
very important to differentiate these different dimensions of quality. Quality has 8
dimensions that has been set by Garvin (1987), they include:
Performance (will the product do the intended job?)
• Potential customers usually evaluate a product to determine if it will perform certain
specific functions and determine how well it performs them. E.g. you could evaluate
spreadsheet software packages for a PC to determine which data manipulation
operations they performs. You may discover that one outperforms another with
respect to the execution speed.

10. Reliability
(How often does the product fail?)
• Complex products, such as many appliances, automobiles, or airplanes, will
usually require some require some repair over their service life. For example,
you should expect that an automobile will require occasional repair, but if
the car requires frequent repair, we say that it is unreliable. There are many
industries in which the customer’s view of quality is greatly impacted by the
reliability dimension of quality.

11. Durability
(How long does the product last?)
• This is the effective service life of the product. Customers obviously want
products that perform satisfactorily over a long period of time. The
automobile and major appliance industries are examples of businesses where
this dimension of quality is very important to most customers.

12. Serviceability
(How easy is it to repair the product?)
• There are many industries in which the customer’s view of quality is directly
influenced by how quickly and economically a repair or routine maintenance
activity can be accomplished. Examples include the appliance and
automobile industries and many types of service industries.
(How long did it take a credit card company to correct an error in your bill?)

13. Aesthetics
(What does the product look alike?)
• This is the visual appeal of the product, often taking into account factors
such as style, color, shape, packaging alternatives, tactile characteristics, and
other sensory features. For example, soft-drink beverage manufacturers have
relied on the visual appeal of their packaging to differentiate their product
from other competitors.

14. Features
(What does the product do?)
• Usually, customers associate high quality with products that have added
features; that’s, those that have features beyond the basic performance of the
competition. E.g. you might consider a spreadsheet software package to be
superior quality if it had built in statistical analysis features while its
competitors did not.

15. Perceived Quality
(What is the reputation of the company or its product?)
• In many cases, customers rely on the past reputation of the company concerning
quality of its products. This reputation is directly influenced by failures of the
product that are highly visible to the public or that require product recalls, and by
how the customer is treated when a quality-related problem with the product is
reported. Perceived quality, customer loyalty, and repeated business are closely
interconnected. E.g. if you make regular business trips using a particular airline, and
the flight almost always arrives on time and the airline company does not lose or
damage your luggage, you will probably prefer to fly on that carrier instead of its
competitors.

16. Conformance to Standards
(Is the product made exactly as the designer intended?)
• We usually think of a high quality product as one that exactly meets the
requirements placed on it. E.g., how well does the hood fit on a new car? Is it
perfectly flush with the fender height, and is the gap exactly the same on all sides?
Manufactured parts that do not exactly meet the designer’s requirements can cause
significant quality problems when they are used as the components of a more
complex assembly. An automobile consists of several thousands parts. If each one
is just slightly too big or too small, many of the components will not fit together
properly, and the vehicle (or its major subsystems) may not perform as the designer
intended.

17. Terms and definitions
Meaning
Dimension
No,
Primary product characteristics
Performance
1.
Secondary characteristic
Features
2.
Meeting specifications or industry standards
Conformance
3.
Consistency of performance over time
Reliability
4.
Useful life
Durability
5.
Resolution of problems and complaints
Service
6.
Human-to-human interface
Response
7.
Sensory characteristic
Aesthetics
8.
Past performance and other intangibles
Reputaion
9.

18. Quality Aspects
• There are two general aspects of fitness for use
1. Quality of design.
2. Quality of conformance.
• All goods/services are produced are produced in various grades or levels of
quality. These variations in grades or levels of quality are intentional, and
consequently, the appropriate technical term is quality of design.

19. The Quality of Conformance
• Is how well the product conforms to the specifications required by the design.
Quality of conformance is influenced by a number of factors, including the choice
of manufacturing processes, the training and supervision of the workforce, the
types of process controls, tests, and inspection activities that are employed, the
extent to which these procedures are followed, and the motivation of the workforce
to achieve quality.
• Unfortunately, this definition has become associated more with the conformance
aspect of quality than with design. This is in part due to the lack of formal
education most designers and engineers receive in quality engineering methodology.

20. The Quality of Conformance
• This also leads to much less focus on the customer and more of a “conformance to
specifications” approach to quality, regardless of whether the product, even when
produced to standards, was actually “fit for use” by the customer. Also, there is still
a widespread belief that quality is a problem that can be dealt with solely in
manufacturing, or that the only way quality can be improved is by “gold plating” the
product.
• A modern definition of quality is inversely proportional to variability. Quality
improvement is the reduction of variability in process and products. Another
definition is that quality improvement is the reduction of waste, which is effective in
service industries, where that can be directly measured.

21. Quality Engineering Terminology
Every product possesses a number of quality measures. These parameters are often
called quality characteristics critical to quality (CTQ). Quality characteristics may be of
several types:
• Physical: length, weight, voltage, viscosity.
• Sensory: taste, appearance, color.
• Time orientation: reliability, durability, serviceability.
Note that the different types of quality characteristics can relate directly or indirectly to
the dimensions of quality discussed earlier.

22. An important element of quality management
is its function to:
• Establish quality control programs.
• Assist in the operation of inspection and test function.
• Assist other departments as purchasing, engineering, marketing,
manufacturing and administration.

23. Quality Engineering
• It is the set of operational, managerial, and engineering activities that a company
uses to ensure that the quality characteristics of a product are at the nominal or
required levels and that the variability around these desired levels in minimum. The
technique discussed in this material are from much of the basic methodology used
by engineering and other technical professionals to achieve these goals.
• Most organizations find it difficult (and expensive) to provide the customer with
products that have quality characteristics that are always identical from unit to unit,
or are at levels that match customer expectations. A major reason for this is
variability; consequently, no two products are ever identical.

24. Sources of quality variation
• e.g. the thickness of blades on a jet turbine engine is not identical even on the same
impeller. If this variation in blade thickness is small, then it may have no effect
impact on the customer, the customer will notice if the variation is large. Sources of
this variability include
• Differences in materials.
• Differences in the performance.
• Operation of the manufacturing equipment.
• Differences in the way the operators perform their tasks.

25. Quality variations
Since variability can only be described in statistical terms, statistical methods play a
central role in quality improvement efforts. In the application of statistical methods to
quality engineering, it is fairly typical to classify data on quality characteristics as either
attributes or variables data. Variables data are usually continuous measurements, such
as length, voltage, or viscosity. Attributes data, on the other hand, are usually discrete
data, often taking the form of counts. Such as the number of loan applications that
could not be properly processed because of missing required information, or the
number of emergency room arrivals that have to wait more than 30min to receive
medical attention.

26. Quality Engineering Technology
• Quality engineering technology is the body of technical knowledge for
formulating policy and analyzing and planning product quality to yield full
customer satisfaction at minimum cost.

27. Techniques used in quality engineering
• Preparation of product policy.
• Product quality analysis.
• Quality operations planning.
• Activities of quality Engineer/Technologist.
• Training.

28. Quality standards
• Measurement and analytical facilities.
• Methods and procedures.
• Audit of quality programs.

29. Quality circles
• Trained individuals (group of 5-15) from different levels of the plant who
meet regularly to define, select and solve quality problems.
Advantages of quality circles
• Improvement of quality, safety, skills, communication and productivity.
• Reduction of waste and cost.
• Attitude change, team building, increased job satisfaction.

30. Quality policy
• A directive given by management to company that defines the statement of
principles to be used for quality, written in a clearly understood way by all the
members of the organization.
• Quality characteristics are often evaluated relative to specifications. For a
manufactured product, the specifications are the desired measurements for
the quality characteristics of the components and subassemblies that make
up the product, as well as the desired values for the quality characteristics in
the final product. Specifications are typically in terms of the deadline time to
process an order to provide a particular service.

31. Quality policy
• A value of a measurement that corresponds to the desired value for that quality
characteristics is called the nominal or target value for that characteristic. These
target values are usually bounded by a range of values that, most typically, we
believe will be sufficiently close to the target so as to not impact the function of
performance of the product if the quality characteristic is in that range. The largest
allowable value for a quality characteristic is called the upper specification limit
(USL), and the smallest allowable value for a quality characteristic is called the lower
specification limit (LSL). Some quality characteristics have specification limits on
only one side of the target (not more than/not less than).

32. Quality policy
• Specifications are usually the result of the engineering design process for the
product. Traditionally, design engineers have arrived at a product design
configuration through the use of engineering science principles, which often results
in the designer specifying the target values for the critical design parameters. Then
prototype construction and testing follow. This testing is often done in a much
unstructured manner, without the use of statistically based experimental design
procedures, and without much interaction with or knowledge of the manufacturing
processes that must produce the component parts and final product. However,
through this general procedure, the specification limits are usually determined by the
design engineer. Then the final product is released to manufacturing. We refer to
this as the overall approach to design.

33. Quality policy
• Problems in product quality usually are greater when the overall approach to design
is used. In this approach, specifications are often set without regard to the inherent
variability that exist in materials, processes, and other parts of the system, which
result in components or products that are nonconforming, that’s, nonconforming
products are those that fail to meet one or more of its specifications. A specific type
of failure is called non-conformity. A nonconforming product is not necessarily
unfit for use.
• A non conforming product is considered defective if it has one or more defects,
which are non conformities that are serious enough to significantly affect the safe or
effective use of the product. Obviously, failure on the part of a company to
improve its manufacturing processes can also cause nonconformities and defects.

34. Statistical methods for Quality Control &
Improvement
• Specifically, we focus on 3 major areas:
statistical process control, design of
experiments, and (to a lesser extent)
acceptance sampling. The role of some
of these tools is illustrated in this
diagram.
• In the case of a manufacturing process
the controllable input factor X1, X2, ..
Xp are process variable such as
temperatures, pressures, feed rates, and
other process variables.

35. Statistical methods for Quality Control &
Improvement
• The inputs Z1, Z2, … Zq are uncontrollable (or difficult to control) inputs,
such as environmental factors or properties of raw materials provided by an
external supplier. The production process transforms the input raw materials,
component parts, and subassemblies into a finished product that has several
quality characteristics. The output variable y is a quality characteristic, that’s, a
measure of process and product quality. This model can also be used to
represent nonmanufacturing of service processes.
• A designed experiment is extremely helpful in discovering the key variables
influencing the quality characteristics of interest in the process.

36. Factorial Design
• A designed experiment is an approach to systematically varying the controllable
input factors in the process and determining the effect these factors have on the
output product parameters. Statistically designed experiments are invaluable in
reducing the variability in the quality characteristics and determining the levels of
the controllable variables that optimize process performance. Often significant
breakthroughs in process performance and product quality also result from using
designed experiments. One major type of designed experiments is the factorial
design, in which factors are varied together in such a way that all possible
combinations of factor levels are tested.

37. Control Chart
• A control chart is one of the primary techniques of statistical process
control (SPC). The chart has a center line (CL) and upper and lower control
limits (UCL and LCL). The center line represents where this process
characteristics should fall if there are no unusual sources of variability
present. The control limits are determined from some simple statistical
considerations. In conclusion, quality control process is the art of checking
and making sure that the standard working conditions of the factory are
compiled and the product quality is to specifications.

38. Chapter Two
Data analysis in statistical Quality Control

39. Uncertainty
• All measurements are approximations, no measuring device can give perfect
measurements without experimental uncertainty. By convention, a mass
measured to 13.2g is said to have an absolute uncertainty of ±0.1g and is said
to have been measured to the nearest 0.1g. In other words, we are somewhat
uncertain about that last digit–it could be a “2”; then again, it could be a “1”
or “3”. A mass of 13.2g indicates an absolute uncertainty of ±0.01g.

40. The valid or meaningful digits in a measured or
calculated value
• Minimum number of digits needed to write a given value in scientific
notation without loss of accuracy.
• Exact numbers are those whose values are known exactly and inexact
numbers are those whose values have some uncertainty.
• Uncertainties always exist in measured quantities. Measured quantities are
reported with an uncertainty of at least +1 in the last digit.
• Significant figures include all the digits known with certainty plus the first
uncertain digit.

41. Significant Figures
What is a “significant figure”?
• The number of significant figures in a result is simply the number of figures
that are known with some degree of reliability. The number 13.2 is said to
have 3 significant figures. The number 13.2 is said to have 4 significant
figures.

42. Rules deciding number of significant figures
A. All nonzero digits are significant:
• 1.234g has 4 significant figures.
• 1.2g has 2 significant figures.
B. Zeroes between nonzero digits are significant:
• 1002kg has 4 significant figures.
• 3.07ml 3 significant figures.

43. Rules deciding number of significant figures
C. Leading zeros to the left of the first nonzero digits are not significant; they
just indicate the position of the decimal point:
• 0.001 has only 1 significant figure.
• 0.012 has 2 significant figures.
D. Trailing zeroes that are also to the right of a decimal point in a number are
significant
• 0.0230 has 3 significant figures.
• 0.20 has 2 significant figures.

44. Rules deciding number of significant figures
E. When a number ends in zeroes that are not to the right of a decimal point, the zeroes are
not necessarily significant:
• 190 miles may be 2 or 3 significant figures.
• 50,600 maybe 3, 4, or 5 significant figures.
F. The potential ambiguity in the last rule can be avoided by the use of standard
experimental, or “scientific”, notation. e.g., depending on whether the number of
significant figures is 3, 4, or 5, we would write 50,600 calories as:
• 5.06x104 calories = 3 significant figures.
• 5.060x104 calories = 4 significant figures.
• 5.0600x104 calories = 5 significant figures.

45. Counting significant figures
• By writing a number in scientific notation, the number of significant figures
is clearly indicated by the number of numerical figures in the ‘digit’ term.
This approach is a reasonable convention to follow.
• In conclusion, all digits are significant except zeros at the beginning of a
number:
• Nonzero digits are significant.
• Zero digit is significant in the middle of nonzero digit, or at the end and to
the right of a decimal point.

46. Exact Number
• Exact numbers contain infinite number of significant figures. Some numbers
are exact because they are known with complete certainty. Most exact
numbers are integers: 1000 ml is in 1L, 23 students in a class. Exact numbers
are often found as conversion factors or as counts of objects.
• Exact numbers can be considered to have an infinite number of significant
figures. Thus, the number of apparent significant figures in any exact number
can be ignored as a limiting factor in determining the number of significant
figures in the result of a calculation.