The document discusses quality management principles and tools. It begins by listing several quality management principles including customer focus, leadership, involvement of people, process approach, and continual improvement. It then describes six common quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Finally, it provides a list of additional quality management topics for further reading.

1. Quality management principle
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I. Contents of quality management principle
==================
ISO 9001 is based upon 8 Principles of Quality Management. As well as being guiding principles
for the development of the most popular quality standard, they are also useful resources for
management professionals looking to implement or improve a quality management programme.
Principe 1: Customer Focus
This standard relates to customer needs and customer service: a business should understand their
customers and seek to meet their requirements. Where possible, they should aim to exceed
customer expectations.
The benefits of this are increased customer loyalty, increased revenue due to the ability to spot
new customer opportunities and increased effectiveness of processes related to customer
satisfaction.
Principle 2: Leadership
This standard relates to the direction of the organisation: a business should have clear objectives
and employees should be actively involved in achieving this.
The benefits of this are primarily employee engagement and increased motivation: research has
shown that if employees are kept ‘in the loop’ with regards to business vision they are likely to
be more productive. One of the most comments employee complaints is lack of communication;
this principle seeks to rectify that.
Principle 3: Involvement of people
This principle recognizes that an organisation is nothing without its staff and that their abilities
should be used to full effect for business success.

2. The benefits of this principle are employee motivation and increased innovation. When people
feel that their skills are being used well they are more likely to work to their maximum potential
and contribute ideas. This principle also emphasises the importance of making employees
accountable for their actions, leading to a greater feeling of responsibility.
Principle 4: Process Approach
The process approach relates to efficiency and the understanding that appropriate processes will
speed up activities.
The main benefits of this, aside from efficiency, are reduced costs due to effective use of
resources, improved and consistent results and focussed improvements.
Principle 5: Systemapproach to management
Closely related to system 4, ISO define this principle as:
‘Identifying, understanding and managing interrelated processes as a system contributes to the
organisation’s effectiveness and efficiency in achieving its objectives.’
This means that multiple processes are managed together as a system which should lead to
greater efficiency.
When implemented, this principle allows a business to focus their efforts on the processes that
are key to their success as well as aligning complementary processes for improved efficiency.
This process fosters a greater understanding of the interrelation of various business elements.
Principle 6: Continual improvement
This principle is very straight forward: continual improvement should be an active business
objective.
The benefits of this are clear: increased ability to embrace new opportunities, organisational
flexibility and improved performance. Especially in difficult economic times, the businesses that
thrive are those that can adapt to new market situations.
Principle 7: Factual approach to decision making
A logical approach, based on data and analysis, is good business sense. Unfortunately, in a fact
paced workplace, decisions can often be made rashly, without proper thought. The efficiency that
will have been imbued in the organisation after the implementation of prior principles will allow
decisions to be made with clarity.
Informed decisions lead to improved understanding of the marketplace as data is collated and
analysed, and the ability to defend past decisions.
Principle 8: Mutually beneficial supplier relations
This principle relates to supply chains and acknowledges that the relationship between an
organisation and its suppliers is interdependent. A strong relationship between the two will
enhance productivity and encourage seamless working practices.
ISO state that the benefits of this principle are optimisation of costs and resources, fostering long
term relationships and the ‘flexibility of joint responses to changing market or customer needs
and expectations’.

3. ==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)
 Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected
2. Control chart

4. Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart

5. A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that

6. is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.

7. 5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

8. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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