The document discusses 7 quality management tools that are commonly used in quality control processes. It provides descriptions of each tool, including cause and effect diagrams, flowcharts, checksheets, Pareto diagrams, histograms, control charts, and scatter diagrams. For each tool, it explains what the tool is used for and how it can help identify issues, optimize processes, ensure consistency, prioritize problems, analyze distributions, determine if a process is stable/predictable, and determine relationships between variables. It also includes more detailed explanations and examples of checksheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms as specific quality management tools.

1. 7 quality management tools
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I. Contents of 7 quality management tools
==================
There are seven quality tools, known as the “7QC tools” in quality management circles. You
should familiarize yourself with these for the PMP Certification Exam. Here is a brief
description of each tool.
Cause and effect diagram: Used to identify the root causes or contributors to a problem, error,
or defect. The problem statement is the effect and the possible contributing factors are the
causes. For example, a failure in a system integration test could be due to
 Improper coding
 Unskilled coder
 Inappropriate environment
 Inappropriate test script
 Insufficient bandwidth
The preceding are just a few examples. And each cause can be further explored for a more
elaborate cause-and-effect diagram, also known as a “fishbone” diagram (because when drawn, it
looks like a fish) or an “Ishikawa” diagram (named for the person who developed it).
Flowcharts. A flowchart can help you see the relationship between the process steps. You can
use this information to optimize the process and to see where problems and defects can occur.
Flowcharts are useful in process improvement projects or to document any process.

2. Checksheets. Used to ensure that a series of steps are followed consistently. Checksheets (also
known as “tally sheets”) can be used to organize data around a quality problem. For example,
you can tally the number of times that a specific cause is the source of a defect, and then use that
when creating a histogram or Pareto chart to prioritize quality problems.
Pareto diagrams. A vertical bar chart that creates a graphic display of events (such as causes of
defects or types of defects) in descending order. The objective is to rank problems based on the
frequency of occurrence to determine the order in which to resolve them.
Histograms. A vertical bar chart (like the Pareto diagram), but a histogram is arranged to show
the shape of distribution of an event: for example, the shape of distribution of calls coming into a
call center. It can show the spread of results (dispersion) and the median (or mean or mode).
Control charts. Used to determine whether a process is stable and predictable. The planned
value of a process is the centerline. For many processes, the upper and lower control limits are
+/–3 standard deviations from the plan, or the mean, depending on the circumstances. The upper
and lower specification limits are the limits specified in the quality requirements.
If a measurement is getting close to the control limit, you should take action to get it back toward
the midline. Here are the definitions from the PMBOK Guide:
Control chart. A graphic display of process data over time and against established control limits,
and that has a centerline that assists in detecting a trend of plotted values toward either control
limit.
Control limits. The area composed of three standard deviations on either side of the centerline,
or mean of a normal distribution of data plotted on a control chart that reflects the expected
variation in the data.
Specification limits. The area, on either side of the centerline, or mean, of data plotted on a
control chart that meets the customer’s requirements for a product or service. This area may be
greater than or less than the area defined by the control limits.
Although control charts were developed to track manufacturing and repetitive processes, they
can be used to track defects, cost and schedule variance, or any other predictable event on a
project. For example, it is predictable that you will have cost and schedule variances on your
project.
However, you want to make sure they are within an allowable limit. You can track the variances
using a control chart to spot trends and to monitor the quality of cost and schedule performance.
When planning for quality, establish the items that you will measure via a control chart and also
set the upper and lower control limits as well as upper and lower specification limits.
Scatter diagrams. An X,Y matrix that plots the relationship between two variables to determine
whether a relationship exists: for example, the number of hours worked in a week and the
number of errors made. A positive correlation would show that the more hours were worked, the
more errors per hour occurred.

3. Each of these seven basic quality tools can be used in the Control Quality process. During the
planning process, the team meets and determines which tools to use, the parameters and
measurements that should be used, and under which circumstances they will be used.
==================
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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