1. process of qualitymanagement
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I. Contents of process of quality management
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Most quality management systems are designed with customers’ satisfaction in mind. It is the
procedures, processes, organization, and resources that are needed to keep the organization
focused on its goals and the activities it puts in place to achieve its goals. Often these techniques
lead to higher levels of customer satisfaction because organizations have developed products,
services and programs that not only please customers, but also ensured the consistent delivery of
what it promised. Though there are many steps in the implementation of a Quality Management
System, the following seven steps are essential:
Step1: Identify Organizational Goals
The process of quality management starts by defining how employees’ jobs are tied to your
company or organizations goals. Employees need to know the organization’s mission, vision,
values, how they relate to the company and their role in it. All new employees should receive a
thorough orientation with regards to the company’s vision, mission, values and goals. Knowing
their individual goals and how it relates to the organization’s goals is the first step in the process
of quality management.
Step2: Identify Critical SuccessFactors
The factors that make an organization’s quality management system successful should be
identified. These factors can be a well-designed production process, great product, technical

2. support, customer support, financial security, or employee satisfaction. Make a list of the
primary factors that influence the process of quality management and continuously and
consistently manage those factors.
Step3: Identify Internal and External Customers
Identify the key groups of customers that make your quality management system work. Knowing
these customers and their needs can help you develop programs and services for these people.
Often customers are vendors, suppliers, employees, volunteers or direct customers.
Step4: Customer Feedback
Customer feedback is essential in the process of quality management. Consistent customer
feedback enables organizations to detect and solve quality problems before it become a serious
issue. Customer feedback could be obtained through regular customer surveys, by phone, email,
focus groups or in person. Calling customers after they buy a product or use a service is an easy
feedback mechanism to determine customer satisfaction. Another method to consider is in-
person surveys directly after the work or product is delivered. An important method of
monitoring customer satisfaction (dissatisfaction) is to review customer complaints and Returned
Products. Whatever method your organization use, the objective is continuous feedback to ensure
a successful process of quality management.
Step5: Implement Continuous Improvements
Quality management is synonymous with continuous improvement. The results or information
gleamed from an organization’s survey or other customer feedback tools must now be used to
make the necessary changes to the quality management process. This could entail more
leadership development, customer service training, higher levels of staffing, corrections to the
production process, changes to the product or service you manufacture or deliver, etc. The
essential factor is to study the feedback received and uses it to continuously improve the
organizations processes in delivering the products and services it sells to its customers.
Step6: SelectQuality Management Software
Select quality management software that not only helps you to implement a quality management
process, but also helps you to maintain and improve the process.
Step7: MeasureResults
Measure your results. Though there may be many reasons for implementing a process of quality
management, one of the main reasons is to ensure your organization executes on its goals. When
an organization does not reach its goals, it is difficult to show a positive ROI and the existence of

3. the organization is brought into question. Right from the first moment make sure that there are
measurable goals and everyone involved knows what needs to be achieved. When they are not
achieved make sure everyone is clear on what required corrective action is necessary to ensure
the goals are achieved.
==================
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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