The document provides information on quality control tools and techniques including seven traditional QC tools (Pareto chart, flowchart, cause-and-effect diagram, check sheet, histogram, scatter diagram, and control chart). It describes each tool's purpose and methodology. For example, it explains that a Pareto chart identifies the most significant factors impacting a process, a flowchart provides a visual map of process steps, and a cause-and-effect diagram helps identify potential causes for an observed effect or problem. The document also provides examples and comparisons (such as the difference between a histogram and bar graph).

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Syllabus – As per AKTU 2019-20
UNIT –III
Tools and Techniques:
Seven QC tools (Histogram, Check sheet, Ishikawa diagram, Pareto, Scatter
diagram, Control chart, flow chart).
Control Charts:
Theory of control charts, measurement range, construction and analysis of R
charts, process
capability study, use of control charts, P-charts and C-charts.
Tools and Techniques
SEVEN QC TOOLS- There are seven traditional quality control tools
The Old Seven." "The First Seven." "The Basic Seven."-
Quality pros have many names for these seven basic tools of quality, first
emphasized by Kaoru Ishikawa, a professor of engineering at Tokyo University
and the father of "quality circles." Start your quality journey by mastering these
tools, and you'll have a name for them too: indispensable.
1. Pareto chart: A bar graph that shows which factors are more significant.
2. Stratification: A technique that separates data gathered from a variety of
sources so that patterns can be seen (some lists replace stratification with
flowchart or run chart).
3. Cause-and-effect diagram (also called Ishikawa or fishbone diagrams):
Identifies many possible causes for an effect or problem and sorts ideas
into useful categories.

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4. Check sheet: A structured, prepared form for collecting and analyzing
data; a generic tool that can be adapted for a wide variety of purposes.
5. Histogram: The most commonly used graph for showing frequency
distributions, or how often each different value in a set of data occurs.
6. Scatter diagram: Graphs pairs of numerical data, one variable on each
axis, to look for a relationship.
7. Control chart: Graph used to study how a process changes over time.
Comparing current data to historical control limits leads to conclusions
about whether the process variation is consistent (in control) or is
unpredictable (out of control, affected by special causes of variation).
PARETO CHART:
Italian economist Vilfredo Pareto Shows on a bar graph which factors are more
significant. This method helps to find the vital few contributing maximum
impact.
Purpose: The purpose of the Pareto chart is to prioritize problems No company
has enough resources to tackle every problem, so they must prioritize.
Pareto Principle: The Pareto concept was developed by the describing the
frequency distribution of any given characteristic of a population. Also called
the 20-80 rule, he determined that a small percentage of any given group (20%)
account for a high amount of a certain characteristic (80%).
Conclusion: The most important thing in improving quality is to start
somewhere, doing something. As you begin using the Pareto chart to decide
where your problems are, you will discover many things about your processes
and will come because you will know where to improve.
FLOWCHART:
A technique that separates data gathered from a variety of sources so that
patterns can be seen (some lists replace "stratification" with or "run chart").
Purpose: Flow Charts provide a visual illustration of the sequence of operations
required to complete a task.

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A picture of the steps the process undergoes to complete it's task. Every process
will require input(s) to complete it's task, and will provide output(s) when the
task is completed. Flow charts can be drawn in many styles. Flow charts can be
used to describe a single process, parts of a process, or a set of processes. There
is no right or wrong way to draw a flow chart. The true test of a flow chart is
how well those who create and use it can understand it.
Input Process Output
CAUSE-AND-EFFECT DIAGRAMS ( or Fishbone
Diagram )
1943 by Mr. Kaoru Ishikawa at the University of Tokyo
Purpose: One important part of process improvement is continuously striving to
obtain more information about the process and it's output. Cause-and-effect
diagrams allow us to do not just that, but also can lead us to the root cause, or
causes, of problems

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Constructing the Cause-and-Effect Diagram:
Step 1: Select the team members and a leader. Team members knowledgeable
about the quality.
Team members focus on the problem under investigation.
Step 2: Write the problem statement on the right hand side of the page, and
draw a box around it with an arrow running to it. This quality concern is now
the effect.
Step 3: Brain-storming. The team members generate ideas as to what is causing
the effect.
Step 4: This step could be combined with step 3. Identify, for each main cause,
its related sub-causes that might affect our quality concern or problem (our
Effect). Always check to see if all the factors contributing to the problem have
been identified. Start by asking why the problem exists.
Step 5: Focus on one or two causes for which an improvement action(s) can be
developed using other quality tools such as Pareto charts, check sheets, and
other gathering and analysis tools.
Conclusion: Improvement requires knowledge. The more information we have
about our processes the better we are at improving them. Cause-and-effect
diagrams are one quality tool that is simple yet very powerful in helping us
better understand our processes.
CHECK SHEETS
Purpose: Check sheets allow the user to collect data from a process in an easy,
systematic, and organized manner it is also called defect concentration
diagram.
Data Collection: Before we can talk about check sheets we need to understand
what we mean by data collection. This collected data needs to be accurate and
relevant to the quality problem. The first is to establish a purpose for collecting
this data. Second, we need to define the type of data that is going to be
collected. Measurable data such as length, size, weight, time,...etc., and
Countable data such as the number of defects. The third step is to determine
who is going to collect that data and when it should be collected.

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Check Sheet Procedure
1. Decide what event or problem will be observed. Develop operational
definitions.
2. Decide when data will be collected and for how long.
3. Design the form. Set it up so that data can be recorded simply by making
check marks or X's or similar symbols and so that data do not have to be
recopied for analysis.
4. Label all spaces on the form.
5. Test the check sheet for a short trial period to be sure it collects the
appropriate data and is easy to use.
6. Each time the targeted event or problem occurs, record data on the check
sheet
Check Sheet Example
The figure below shows a check sheet used to collect data on telephone
interruptions. The tick marks were added as data was collected over several
weeks
HISTOGRAMS
Histograms: A histogram is a tool for summarizing, analyzing, and displaying
data. It provides the user with a graphical representation of the amount of
variation found in a set of data.

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In other words, a diagram involving rectangles whose area is proportional to the
frequency of a variable and width is equal to the class interval
How to Make Histogram?
You need to follow the below steps to construct a histogram.
1. Begin by marking the class intervals on X-axis and Frequencies on Y-
axis.
2. The scales for both the axes have to be same.
3. Class intervals need to be exclusive.
4. Draw rectangles with bases as class intervals and corresponding
frequencies as heights.
5. A rectangle is built on each class interval since the class limits are
marked on the horizontal axis, and the frequencies are indicated on the
vertical axis.
6. The height of each rectangle is proportional to the corresponding class
frequency if the intervals are equal.
7. The area of every individual rectangle is proportional to the
corresponding class frequency if the intervals are unequal.
Although histograms seem similar to graphs, there is a slight difference between
them. The histogram does not involve any gaps between the two successive
bars.
Difference Between Histogram and Bar Graph
The difference between the histogram and the bar graph is given below:
Histogram Bar Graph
It is a two-dimensional figure It is a one-dimensional figure
The frequency is shown by the
area of each rectangle
The height shows the frequency and the
width has no significance.
It shows rectangles touching
each other
It consists of rectangles separated from
each other with equal spaces

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Conclusion: Histogram is simple tools that allow the user to identify and
interpret the variation found in a set of data points. It is important to remember
that histograms do not give solutions to problems.
SCATTER DIAGRAM –
Purpose: To identify correlations that might exist between a quality
characteristic and a factor that might be driving it.
Scatter Diagrams: A scatter diagram is a nonmathematical or graphical
approach for identifying relationships between a performance measure and
factors that might be driving it. This graphical approach is quick, easy to
communicate to others, and generally easy to interpret.
.
When to Use a Scatter Diagram
 When you have paired numerical data
 When your dependent variable may have multiple values for each value
of your independent variable
 When trying to determine whether the two variables are related, such as:
o When trying to identify potential root causes of problems

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o After brainstorming causes and effects using a fishbone diagram to
determine objectively whether a particular cause and effect are
related
o When determining whether two effects that appear to be related
both occur with the same cause
o When testing for autocorrelation before constructing a control chart
Scatter Diagram Procedure
1. Collect pairs of data where a relationship is suspected.
2. Draw a graph with the independent variable on the horizontal axis and the
dependent variable on the vertical axis. For each pair of data, put a dot or
a symbol where the x-axis value intersects the y-axis value. (If two dots
fall together, put them side by side, touching, so that you can see both.)
3. Look at the pattern of points to see if a relationship is obvious. If the data
clearly form a line or a curve, you may stop because variables are
correlated. You may wish to use regression or correlation analysis now.
Otherwise, complete steps 4 through 7.
4. Divide points on the graph into four quadrants. If there are X points on
the graph:
o Count X/2 points from top to bottom and draw a horizontal line.
o Count X/2 points from left to right and draw a vertical line.
o If number of points is odd, draw the line through the middle point.
5. Count the points in each quadrant. Do not count points on a line.
6. Add the diagonally opposite quadrants. Find the smaller sum and the total
of points in all quadrants.
A = points in upper left + points in lower right
B = points in upper right + points in lower left
Q = the smaller of A and B
N = A + B
7. Look up the limit for N on the trend test table.
o If Q is less than the limit, the two variables are related.
o If Q is greater than or equal to the limit, the pattern could have
occurred from random chance.

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Scatter Diagram Example
The ZZ-400 manufacturing team suspects a relationship between
product purity (percent purity) and the amount of iron (measured in parts
per million or ppm). Purity and iron are plotted against each other as a
scatter diagram, as shown in the figure below.
There are 24 data points. Median lines are drawn so that 12 points fall on each
side for both percent purity and ppm iron.
To test for a relationship, they calculate:
A = points in upper left + points in lower right = 9 + 9 = 18
B = points in upper right + points in lower left = 3 + 3 = 6
Q = the smaller of A and B = the smaller of 18 and 6 = 6
N = A + B = 18 + 6 = 24
Then they look up the limit for N on the trend test table. For N = 24, the limit is
6.
Q is equal to the limit. Therefore, the pattern could have occurred from random
chance, and no relationship is demonstrated.

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CONTROL CHARTS –
Purpose: Process is in control and to monitor process variation on a continuous
basis. Identifying the tolerance level in the variations. Control charts is one SPC
tool that enables us to monitor and control process variation. Types of variation
Common and Special Cause Variation
Control charts: Developed in the mid 1920's by Walter Shewhart of Bell labs.
There are two basic types of control charts, the average and range control
charts. The first deals with how close the process is to the nominal design value,
while the range chart indicates the amount of spread or variability around the
nominal design value. A control chart has basically three line: the upper control
limit UCL, the center line CL, and the lower control limit LCL. A minimum of
25 points is required for a control chart to be accurate.