This document discusses quality management concepts including the meaning of quality, quality assurance vs quality control, process control, and statistical process control. It defines quality as meeting customer expectations and fitness for use. Quality assurance focuses on implementing processes while quality control checks output. Process control monitors a process to ensure it behaves as expected. Statistical process control uses tools like control charts to reduce variability and identify sources of variation. The document reviews various quality measurement and process analysis tools.

2. From this lecture we will learn
Meaning of Quality and Quality Management ?
Why we measure quality?
Relationship in Quality assurance & Quality control
Process control and Statistical process control
Statistical process control tools: Histogram,
Check sheet, Pareto chart, Cause-and-
effect diagram, Defect diagram, Scatter
diagram, Control chart
Control chart basic procedure
Types of Control charts

3. Meaning of Quality
 Every quality expert defines quality is a somewhat
different way.
 A modern definition of quality derives from Juran's
"fitness for intended use."
 Quality is "meeting or exceeding customer
expectations."
• Deming states that the customer's definition of
quality is the only one that matters.
 Quality is a perceptual, conditional, and somewhat
subjective attribute and may be understood
differently by different people.
 Therefore, Quality in business, engineering and
manufacturing has a pragmatic interpretation as the
non-inferiority or superiority of something

4. Meaning of quality
 Final Perspective of quality includes both the producer’s and
customer’s perspective.
 Customer’s view must dominate.
 Fitness for consumer use at the lowest cost.
Fitness for
Consumer Use
Producer’s Perspective Consumer’s Perspective
Quality of Conformance
 Conformance to
specifications
 Cost
Quality of Design
 Quality
characteristics
 Price
MarketingProduction
Meaning of Quality

5. Quality of Design:
• A measure of how well the design specification
meets the customer requirements;
• A function of a product’s specifications.
• Starts with the market research, sales feedback
analysis and ends with development of a product
satisfying the customers.
Example: Compare a BMW with a Honda Civic.
Gitlow’s
definition:
Quality is a journey starting from design, to
conformance and ends at better performance.

6. Quality of Conformance:
A measure of how well the product meets its
designed specifications.
Example: If the Honda Civic does what it is designed to
do and does it well, quality exists.
 If an economy car is designed to provide reliable,
low-cost, low-maintenance transportation, the
desired quality exists.
 If there is a discrepancy between quality of
design and quality of conformance, it means that
defects exist in the product or product needs
rework.
 As quality of conformance improves, incidences
of defects, reworks and adjustments decline,
resulting in cost reduction and productivity gain.

7. Quality of Performance
A measure of how well customers’ needs are
satisfied by the performance of the product over a
period of time.
• A function of quality of design and quality of
conformance.
• A numerical measurement of the performance of an
organization, division, or process.
• Quality of performance can be assessed through
measurements of physical products, statistical
sampling of the output of processes, or through
surveys of purchasers of goods or services. Also
referred to as quality of service.

8. Why we measure quality?
Quality measurement is central to the process of
quality control and improvement
Measurement enables us to provide a service/product that
is more closely aligned with the expectations of the users.
 For quality control, measurement provides
feedback and early warnings of the problems.
 For quality planning, measurement quantifies
customer’s needs, and product and process
capabilities.
 For quality improvement, measurement can
motivate people, prioritize improvement
opportunities, and help in diagnosing the cause.

9. Measuring the quality
Garvin’s Product Quality: Eight dimensions of quality
heavily influence the customers.
1. Performance, primary operating characteristics of a
product.
Examples: brake horse power, specific fuel consumption,
acceleration of a car.
2. Features, secondary aspects of the performance.
These are ‘bells and whistles’ of the product that
supplement its basic functioning.
Examples: Air conditioner, CD player, Adjustable seat etc.
3. Conformance, meeting the specification or industry
standards.
Example: Dimensional accuracy, smooth start etc.

10. 5. Serviceability, how easy is it to repair the product?
• How quickly and economically a repair or routine
maintenance activities can be accomplished.
• It refers to speed, courtesy, competence, and ease
of repair.
• Example: For a car, it is how quickly and easily it
can be repaired and how long it stays repaired.
4. Reliability, how often the product fails ?
• Often measured as mean-time-between-failure
(MTBF).
• Requires a product to be in use for a specified
period of time.

11. 6. Durability, how long the product last? The effective
service life of the product.
• The amount of use from a product before it breaks
down and replacement.
• Durability is closely linked to both reliability and
serviceability. Example: Automobile and other major
appliances.
7. Aesthetics, how a product looks, feels, sounds,
tastes, or smells.
• Largely a matter of personal judgment and a
reflection of individual preference
8. Perceived quality, what is the reputation of the
company or its company?

12. Quality management is the act of overseeing all activities
and tasks needed to maintain a desired level of
excellence.
This includes:
• the determination of a quality policy,
• creating and implementing quality planning and
assurance, and
• quality control and quality improvement.
It is also referred to as total quality management (TQM).
• TQM is a business philosophy that champions the
idea that the long-term success of a company
comes from customer satisfaction. TQM requires
that all stakeholders in a business work together to
improve processes, products, services and the
culture of the company itself.
What is Quality Management ?

13. Quality assurance vs. Quality control
Quality assurance and Quality control are two very closely
related concepts and often make us confused.
Quality Assurance is a process focused concept,
where the processes are put in place to ensure the
correct steps are done in the correct way.
• If the correct processes are in place there is
some assurance that the actual results will turn
out as expected.
Quality Control is a product focused concept, where
checking of the actual results are done to ensure that
things are as expected.
• If the correct controls are in place you can
know for certain that the actual results have
been achieved because the actual results have
been checked.

14. PC refers to procedures or techniques adopted to
evaluate, maintain and improve the quality
standard in various stages of manufacture.
A process is considered satisfactory as long as it
produces items within designed specification.
 Process should be continuously monitored to
ensure that the process behaves as it is expected.
Salient features of process control
 Controling the process at the right level and
variability.
 Detecting the deviation as quickly as possible.
 not only detect trouble, but also to find out cause.
 Developing an efficient information system in order
to establish an efficient system of process control.
Process Control

15. If a product is to meet or exceed customer
expectations, generally it should be produced by a
process that is stable or repeatable.
Statistical process control (SPC) is a powerful
collection of problem-solving tools useful in
achieving process stability and improving
capability through the reduction of variability.
 Statistical process control (SPC)
 Involves inspecting the output from a process
 Quality characteristics are measured and charted
 Helpful in identifying in-process variations
SPC is easy to use, has significant impact, and can
be applied to any process.
Statistical Process Control

16. Its seven major tools are
1. Histogram or stem-and-leaf plot
2. Check sheet
3. Pareto chart
4. Cause-and-effect diagram
5. Defect concentration diagram
6. Scatter diagram
7. Control chart
Statistical evaluation of the output of a process
during production.
 Goal is to make the process stable over time and
then keep it stable unless the planned changes are
made.
 Statistical description of stability that the ‘pattern of
variation’ remains stable over time, not that there be
no variation in the variable measured.

17. Nature of defect is different in services
 Service defect is a failure to meet customer
requirements
 One way to deal with service quality is to devise
quantifiable measurement of service elements
 Number of complaints received per month,
 Number of telephone rings before call is
answered
SPC in Service
Example: Hospitals
• timeliness and quickness of care,
• staff responses to requests,
• accuracy of lab tests,
• cleanliness,
• courtesy,
• accuracy of paperwork,
• speed of admittance and checkouts

18.  Grocery Stores
◘ waiting time to check out, ◘ frequency of out-of-stock
items, ◘ quality of food items, ◘ cleanliness, ◘ customer
complaints, ◘ checkout register errors …
 Airlines
◘ flight delays, ◘ lost luggage and luggage handling, ◘
waiting time at ticket counters and check-in, ◘ agent
and flight attendant courtesy, ◘ accurate flight
information, ◘ passenger cabin cleanliness and
maintenance …
 Fast-Food Restaurants
◘ waiting time for service, ◘ customer complaints, ◘
cleanliness, ◘ food quality, ◘ order accuracy, ◘ employee
courtesy
 Catalogue-Order Companies
◘ order accuracy, ◘ operator knowledge and courtesy, ◘
packaging, ◘ delivery time, ◘ phone order waiting time.

19. Histogram
A histogram is a bar graph which shows the frequency
distribution of the data of a group about the central
value.
The histogram is an important diagnostic tool because
it gives a ‘Birds’s-eye-view’ of the variation in a
data set.
A histogram can be used for:
• comparisons of process distribution before and
after the improvement action (production,
vendor performance, administration, purchase,
inspection, etc.)
• comparison of different groups (production,
vendor to vendor difference etc.)
• relationship with specification limits.

20. Histogram is formed by marking off
intervals on the real number line on
which the measurements fall and
then constructing a bar over each
interval with height equal to the
frequency or relative frequency.
Histograms are excellent graphic
displays for focusing attention on
key aspects of the shape of a
distribution of data:
• symmetry,
• skewness,
• clustering,
• gaps, etc
They are not good tools for making
comparisons among multiple
datasets.

21. Check Sheet
A check sheet is a data gathering format
prepared in such a way that the data collection
is simplified.
 When designing a check sheet, it is important to
clearly specify :
• the type of data to be collected,
• the part or operation number,
• the date,
• The (name of) analyst, and
• any other useful information
• cause of poor performance, etc.
 The information to be recorded and simplifies in
order to make an easy to understand form used to
answer the question ‘‘how often certain events are
happening’’ ?

22. Example: A check sheet to record defects on a tank

23. The primary purpose and use of pareto diagrams
is to focus improvement efforts on the most
important causes by identifying the vital few
and trivial many causes.
The Pareto chart indicates the following :
• What are the problems,
• Which problem needs to be tackled on priority.
• What percentage (%) of the total does each
problem present.
Pareto Diagram
Sales Customer complaints analysis, warranty costs,
market share, etc.
Production Analysis of non-conformance, machine and men
utilization, maintenance, machine down time,
break down, spares requirement.
Safety Injury types and causes
Applications:

24. How to draw Pareto diagram:
1. Select the problem area (say
customer complaints).
2. Decide the method and the
period for data collection.
3. Arrange the data of the items in
the descending order.
4. 4. Draw axis on graph with the
scale of unit indicated.
5. Draw the bar graph in the
descending order.
6. Draw the cumulative
percentage value in the graph.

25. Cause and Effect Diagram
A cause and effect diagram (also known as Ishikawa
diagram or fishbone diagram) in a pictorial
representation of all possible causes which are
supposed to influence an ‘‘effect’’ which is under
consideration.
• Cause-and-effect analysis is an extremely powerful
tool. A highly detailed cause-and effect diagram
can serve as an effective troubleshooting aid.
• The construction of a cause-and-effect diagram as
a team experience tends to get people involved in
attacking a problem.
• For every effect there are likely to be several
causes. They can be classified under men,
methods, materials, machines, policies,
procedures, plant etc.

26. Fig: Cause-and-effect diagram for the tank defect problem

27. 1. Define the problem or effect to be analyzed.
2. Form the team to perform the analysis. Team will
uncover potential causes through brainstorming.
3. Draw the effect box and the center line.
4. Specify the major potential cause categories and
join them as boxes connected to the center line.
5. Identify the possible causes and classify them
into the categories in step 4. Create new
categories, if necessary.
6. Rank order the causes to identify those that
seem most likely to impact the problem.
7. Take corrective action.
How to Construct C&E Diagram

28. Defect Concentration Diagram
A defect concentration diagram is a picture of the
unit, showing all relevant views. Various types of
defects are drawn on the picture, and the diagram
is analyzed to determine whether the location of
the defects on the unit.
Surface-finish defects on a refrigerator. Defect concentration diagram for the tank.

29. Scatter Diagram
The scatter diagram is a useful plot for identifying
a potential relationship between two variables.
 Data are collected in pairs on the two variables—say,
(yi, xi), for i = 1, 2, . . . , n. Then yi is plotted against
the corresponding xi.
 The shape of the scatter diagram indicates the type
of relationship may exist between the two variables.
 It shows a clear relationship between two variables
and the strength of that relationship.
Positive relation Negative relation No-relation

30. Control Chart
A graphical display of data over time (data are
displayed in time sequence in which they
occurred/measured) used to differentiate common
cause variation from special cause variation.
 Control charts combine numerical and graphical
description of data with the use of sampling
distribution
 normal distribution is basis for control chart.
 Goal of using chart is to achieve and mainatain
process stability
 A state in which a process has displayed a
certain degree of consistency
 Consistency is characterized by a stream of
data falling within the control limits.

31. A control chart always has
 a central line usually
mathematical average of
all the samples plotted;
 upper control and lower
control limits defining
the constraints of
common variations or
range of acceptable
variation;
 Performance data
plotted over time.
Lines are determined from
historical data.
Basic Components

32.  Controlling ongoing processes by finding and
correcting problems as they occur.
 Predicting the expected range of outcomes from
a process.
 Determining whether a process is stable (in
statistical control).
 Analyzing patterns of process variation from
special causes (non-routine events) or common
causes (built into the process).
 Determining whether the quality improvement
project should aim to prevent specific problems
or to make fundamental changes to the process.
When to use a control chart?

33.  Choose the appropriate control chart for the data.
 Determine the appropriate time period for
collecting and plotting data.
 Collect data, construct the chart and analyze the
data.
 Look for “out-of-control signals” on the control
chart.
 When one is identified, mark it on the chart and
investigate the cause. Document, what you
learned, the cause and how it was corrected.
 Continue to plot data as they are generated. As
each new data point is plotted, check for new out-
of-control signals
Control chart basic procedure?

34.  Interpretation of control chart
 Points between control limits
are due to random chance
variation
 One or more data points above
an UCL or below a LCL mark
statistically significant
changes in the process
 A process is in control if
 No sample points outside limits
 Most points near process average
 About equal number of points above and below CLine
 Points appear randomly distributed
Time period
Measured
characteristics

35.  A process is assumed to be out of control if
 Rule 1: A single point plots outside the control
limits;
 Rule 2: Two out of three consecutive points fall
outside the two sigma warning limits on the
same side of the center line;
 Rule 3: Four out of five consecutive points fall
beyond the 1 sigma limit on the same side of the
center line;
 Rule 4: Nine or more consecutive points fall to
one side of the center line;
 Rule 5: There is a run of six or more consecutive
points steadily increasing or decreasing

36.  Type I error
Concluding a process is not in control when it
actually is.
 Type II error
Concluding a process is in control when it is not.
In control Out of control
In control No Error
Type I error
(producers risk)
Out of control
Type II Error
(consumers risk)
No error
Mean
LCL UCL
/2 /2
Probability
of Type I error
Mean
LCL UCL
/2 /2
Probability
of Type I error
Setting Control Limits

37. UCL = μ + kσ
CL = μ
LCL = μ – kσ
where
 μ is the mean of the variable
σ is the standard deviation of the variable
UCL= upper control limit; LCL = lower control limit;
 CL = center line.
 k is the distance of the control limits from the
center line, expressed in terms of standard
deviation units.
 When k is set to 3, we speak of 3-sigma control
charts.
 Historically, k = 3 has become an accepted
standard in industry.
 General model

38. 1. Variables control charts
 Variable data are measured on a continuous scale.
• For example: time, weight, distance or
temperature can be measured in fractions or
decimals.
Applied to data with continuous distribution
2. Attributes control charts
 Attribute data are counted and cannot have
fractions or decimals.
• Attribute data arise when you are determining
only the presence or absence of something:
• success or failure,
• accept or reject,
• correct or not correct.
• For example, a report can have four errors or five
errors, but it cannot have four and a half errors.
Applied to data discrete distribution
 Types of Control Charts

39.  X-bar (mean chart)
 R chart (range chart)
 S chart (sigma chart)
 Individual or run chart
i-chart
Moving range chart
Median chart
EWMA (exponentially weighted moving average
chart)
 General formulae for a control chart
 UCL or UAL = μ + kσx k = 3 ; Accepted
Standard
 UWL = μ + 2/3 kσx
 CL = μ
 LWL = μ – 2/3 kσx
 LCL or LAL = μ – kσx
 

m
i
i
X
X
m

X
n


 Variable control charts

40. Attributes are discrete events: yes/no or pass/fail
Construction and interpretation are same as that
of variable control charts.
Attributes control charts
 p chart, uses proportion nonconforming (defective) items in
a sample
 Based on a binomial distribution
 Can be used for varying sample size.
 np chart, Uses number of nonconforming items in a sample
 Should not be used when sample size varies
 c chart, Uses total number of nonconformities or defects in
samples of constant size.
 Occurence of nonconformities follows poisson dist..n.
 u chart, when the sample size varies, the number of
nonconformities per unit is used as a basis for this control
chart.
 Attribute control charts