The document provides information about quality management system implementation including forms, tools, and strategies. It discusses common elements that should be present in a total quality management system, such as top management commitment, assessing current processes, developing a master plan, and establishing teams for process improvement. The document also outlines five strategies for developing a quality management process and describes several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Other related topics on quality management systems are provided for further reading.

1. Quality management system implementation
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I. Contents of quality management system implementation
==================
When planning and implementing a total quality management system there is no one solution to
every situation.
Each organization is unique in terms of the culture, management practices, and the processes
used to create and deliver its products and services. The TQM strategy will then vary from
organization to organization; however, a set of primary elements should be present in some
format.
GENERIC MODEL FOR IMPLEMENTING TQM
1. Top management learns about and decides to commit to TQM. TQM is identified as one of
the organization’s strategies.
2. The organization assesses current culture, customer satisfaction, and quality management
systems.
3. Top management identifies core values and principles to be used, and communicates them.
4. A TQM master plan is developed on the basis of steps 1, 2, and 3.
5. The organization identifies and prioritizes customer demands and aligns products and services
to meet those demands.
6. Management maps the critical processes through which the organization meets its customers’
needs.

2. 7. Management oversees the formation of teams for process improvement efforts.
8. The momentum of the TQM effort is managed by the steering committee.
9. Managers contribute individually to the effort through hoshin planning, training, coaching, or
other methods.
10. Daily process management and standardization take place.
11. Progress is evaluated and the plan is revised as needed.
12. Constant employee awareness and feedback on status are provided and a reward/recognition
process is established.
FIVE STRATEGIES TO DEVELOP THE TQM PROCESS
Strategy 1: The TQM element approach
The TQM element approach takes key business processes and/or organizational units and uses
the tools of TQM to foster improvements. This method was widely used in the early 1980s as
companies tried to implement parts of TQM as they learned them.
Examples of this approach include quality circles, statistical process control, Taguchi
methods, and quality function deployment.
Strategy 2: The guru approach
The guru approach uses the teachings and writings of one or more of the leading quality
thinkers as a guide against which to determine where the organization has deficiencies. Then,
the organization makes appropriate changes to remedy those deficiencies.
For example, managers might study Deming’s 14 points or attend the Crosby College. They
would then work on implementing the approach learned.
Strategy 3: The organization model approach
In this approach, individuals or teams visit organizations that have taken a leadership role in
TQM and determine their processes and reasons for success. They then integrate these ideas
with their own ideas to develop an organizational model adapted for their specific
organization.
This method was used widely in the late 1980s and is exemplified by the initial recipients of
theMalcolm Baldrige National Quality Award.
Strategy 4: The Japanese total quality approach
Organizations using the Japanese total quality approach examine the detailed implementation
techniques and strategies employed by Deming Prize–winning companies and use this
experience to develop a long-range master plan for in-house use.
This approach was used by Florida Power and Light—among others—to implement TQM
and to compete for and win the Deming Prize.
Strategy 5: The award criteria approach

3. When using this model, an organization uses the criteria of a quality award, for example, the
Deming Prize, the European Quality Award, or the Malcolm Baldrige National Quality
Award, to identify areas for improvement. Under this approach, TQM implementation
focuses on meeting specific award criteria.
Although some argue that this is not an appropriate use of award criteria, some organizations
do use this approach and it can result in improvement.
==================
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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