This document discusses quality management tools. It begins by introducing 7 key quality control tools used in Japanese manufacturing: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and flowcharts. It then provides more detailed descriptions of each tool, including their purposes and how they are constructed and interpreted. Finally, it lists additional topics related to quality management that have further resources available for download.

1. Quality management tool
In this file, you can ref useful information about quality management tool such as quality
management toolforms, tools for quality management tool, quality management toolstrategies …
If you need more assistant for quality management tool, please leave your comment at the end of
file.
Other useful material for quality management tool:
• qualitymanagement123.com/23-free-ebooks-for-quality-management
• qualitymanagement123.com/185-free-quality-management-forms
• qualitymanagement123.com/free-98-ISO-9001-templates-and-forms
• qualitymanagement123.com/top-84-quality-management-KPIs
• qualitymanagement123.com/top-18-quality-management-job-descriptions
• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of quality management tool
==================
The Japanese began applying the thinking developed by Walter Shewhart and W Edward
Deming during the 1930s and 1940s. Japan's progress in continuous improvement led to the
expansion of the use of these tools.
Kaoru Ishikawa, the then head of the Japanese Union of Scientists and Engineers (JUSE), thus,
decided to expand the use of these approaches in Japanese manufacturing in the 1960s with the
introduction of the seven quality control (7QC) tools.
7QC tools are fundamental instruments to improve the quality of products. They are used to
analyse the production process, identify major problems, control fluctuations of product quality
and provide solutions to avoid future defects.
These tools use statistical techniques and knowledge to accumulate data and analyse them. They
help organise the collected data in a way that is easy to understand. Moreover, by using 7QC
tools, specific problems in a process can be identified.
The first is the check sheet, which shows the history and pattern of variations. This tool is used
at the beginning of the change process to identify the problems and collect data easily.
The team using it can study observed data (a performance measure of a process) for patterns over
a specified period of time. It is also used at the end of the change process to see whether the
change has resulted in permanent improvement.
The Pareto chart is named after Wilfredo Pareto, the Italian economist who determined that
wealth is not evenly distributed. The chart shows the distribution of items and arranges them
from the most frequent to the least frequent, with the final bar being miscellaneous.
The Pareto chart is used to define problems, to set their priority, to illustrate the problems
detected and determine their frequency in the process. It is a graphic picture of the most frequent
causes of a particular problem. Most people use it to determine where to put their initial efforts to
get maximum gain.

2. The cause and effect diagram is also called the "fishbone chart" because of its appearance and
the Ishikawa chart after the man who popularised its use in Japan. It is used to list the cause of
particular problems. Lines come off the core horizontal line to display the main causes; the lines
coming off the main causes are the subcauses.
This tool is used to figure out any possible causes of a problem. It allows a team to identify,
explore, and graphically display, in increasing detail, all of the possible causes related to a
problem or condition to discover its root cause(s).
The histogram is a bar chart showing a distribution of variables. This tool helps identify the
cause of problems in a process by the shape as well as the width of the distribution. It shows a
bar chart of accumulated data and provides the easiest way to evaluate the distribution of data.
Then there's the scatter diagram, which shows the pattern of relationship between two variables
that are thought to be related.
The closer the points are to the diagonal line, the more closely there is a one-to-one relationship.
The scatter diagram is a graphical tool that plots many data points and shows a pattern of
correlation between two variables.
Graphs are among the simplest and best techniques to analyse and display data for easy
communication in a visual format. Data can be depicted graphically using bar graphs, line charts,
pie charts and control charts. While the first three are commonly used, the last is a line chart with
control limits.
By mathematically constructing control limits at three standard deviations above and below the
average, one can determine what variation is due to normal ongoing causes (common causes)
and what variation is produced by unique events (special causes).
By eliminating the special causes first and then reducing common causes, quality can be
improved. Control chart provides control limits that are three standard deviations above and
below average, whether or not our process is in control.
This tool enables the user to monitor, control and improve process performance over time by
studying variation and its source.
==================
III. Quality management tools
1. Check sheet

3. 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
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

4. 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
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

5. 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
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

6. 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.
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

7. 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]
III. Other topics related to Quality management tool (pdf download)
quality management systems
quality management courses
quality management tools
iso 9001 quality management system
quality management process
quality management system example
quality system management
quality management techniques
quality management standards
quality management policy
quality management strategy
quality management books