1. Control Charts

2. GE6757- Total Quality Management
Control Charts
Prepared By,
V.Sutha Jebakumari, AP/CSE
L.Shanmuga Priya, AP/CSE,
Kamaraj College of Engineering & Technology,
Virudhunagar.

3. Control Charts
• Walter Shewhart first utilized control charts in
1924 to aid the world of manufacturing. Control
charts have two general uses in an improvement
project.
• The most common application is as a tool to
monitor process stability and control.

4. Control Charts
• “No matter how well the process is designed,
there exists a certain amount of nature variability
in output measurements.“
• A control chart tells us how much variation the
process causes.
• A stable process produces predictable results
consistently. An example of a control chart that
shows an unstable process means variables
affected must be analyzed and controlled before
the improvement process can begin.

5. Types of control charts
• There are two types of control charts; those that
analyze attributes and those that look at variables
in a process or project. Examples of a control chart
include:
• X-Bar & R Control Charts
• X-Bar & S Control Charts
• U Charts
• P Control Charts
• C Control Charts

6. Examples of Control Charts
• U Charts – These variable types of control charts utilize an upper and lower
range. Elements falling in the upper range need attention and analysis in order
for the problem to be corrected.
• X-Bar & R Charts – These variable charts utilize the X-Bar or the Mean to
determine subgroups. The R or Range plots the subgroups based on upper and
lower control limits. X-Bar & R Charts are the most widely utilized charts in
project management, however, are only successful if 5 or less subgroups are
analyzed.
• X-Bar & S Charts – Using this example of a variable control chart is effective for
5 or more subgroups and the S or Standard Deviations are considered in both
upper and lower control limits based on the X-Bar or Mean.
• p Control Charts – This attribute-type chart is effective when elements are not
equal. A p Control Chart might be used to determine how many accidents
occur each day at a chosen intersection.
• c Control Charts – Another attribute-type control chart, the c Control Chart
explores elements that are nonconforming. A c Control Chart might be used to
explore mass-production of one similar product where the elements per unit
do not conform to the norm.

7. Two causes of fluctuation
• Most examples of a control chart considers
two causes of fluctuation, common causes
and special causes.

8. Three categories of variation
1. Within piecewise variation(eg. Roughness of a
piece)
2. Piece to piece variation (light intensity of four
bulbs produced from a machine will be
different)
3. Time to time variation (difference in product
produced at different times of the day)

9. Second categories of variation
Variation is due to a combination of
1.Equipment
2.Materials
3.Environment
4.Operator

10. 1. Equipment
• Source includes tool wear, machine vibrations
and electrical fluctuations.
• Even identical machines will have different
capabilities.
• It becomes crucial when scheduling the
manufacture of critical parts.

11. 2. Material
• Because variation occurs in the finished
product, it must also occur in the raw
material.
• Quality characteristics as tensile strength,
ductility, thickness, porosity and moisture
content can be expected to contribute to the
overall variation in the final product.

12. 3. Environment
• Temperature, light, radiation, particle size,
pressure and humidity all can contribute to
variation in the product.
• Experiments are conducted in outer space to
learn more about the effect of the environment
on product variation.

13. 4. Operator
• The source of variation includes the method by
which the operator performs the operation.
• The operator’s physical and emotional well-
being contribute to the variation.
• A cut finger, a personal problem or a headache
can make an operator’s quality performance
vary.

14. Elements of a Control Chart
• A control chart begins with a time series graph.
• A central line (X) is added as a visual reference
for detecting shifts or trends – this is also
referred to as the process location.
• Upper and lower control limits (UCL and LCL) are
computed from available data and placed
equidistant from the central line. This is also
referred to as process dispersion.
•

16. • Control limits (CLs) ensure time is not wasted looking for
unnecessary trouble – the goal of any process
improvement practitioner should be to only take action
when warranted. Control limits are calculated by:
• Estimating the standard deviation, σ, of the sample data
• Multiplying that number by three
• Adding (3 x σ to the average) for the UCL and subtracting (3
x σ from the average) for the LCL
• Mathematically, the calculation of control limits looks like:
•

17. Run Charts
• Monitor the performance of one or more
processes over time to detect trends, shifts or
cycles.
• Allow us to compare a performance measure
before and after implementation of a solution to
measure its impact.
• Focuses attention on truly vital changes in the
process.
• Assess whether improved performance has been
sustained.

18. How to Create a Run Chart
There are seven steps to creating a run chart.
• Decide on the measure to be analyzed (assuming there is a
reliable measurement system in place).
• Gather the data – have a minimum of 10 data points.
• Draw a graph with a vertical line and a horizontal line.
• On the vertical line, or the y-axis, draw the scale relative to
the variable you are measuring.
• On the horizontal line, or the x-axis, draw the time or
sequence scale.
• Calculate the mean/median (whichever the data set
indicates to be appropriate) and draw a horizontal line at
that value – going across the graph.
• Plot the data in the sequence, or the time order, in which
the data was collected.

19. Characteristics of a run chart
• On the X axis you have data in some sort of
chronological order e.g. Jan, Feb, Mar
• On the Y axis you have the measure of interest
e.g. %, count
• Once the data points are connected you put a
centre line (CL) between the graph. For a run
chart the CL is called the Median.

20. Interpreting a run chart
• There are four rules that can be used to
interpret a run chart. Non-random variation
can be recognized by looking for:
• Rule 1 – Shift
Six or more consecutive points either all above
or all below the centre line (CL). Values that
fall on the CL do not add to nor break a shift.
Skip values that fall on the median and
continue counting.

21. Rule 1 – Shift

22. Rule 2 – Trend
• Five or more consecutive points all going up or
all going down. If the value of two or more
successive points is the same (repeats), ignore
the like points when counting.

23. Rule 2 – Trend

24. Rule 3 – Too many or too few runs
• A non-random pattern is signalled by too few or too
many runs, or crossings of the median line. If there are
too many or too few runs, this is a sign of non-random
variation. An easy way to count the number of runs is
to count the number of times the line connecting all
the data points crosses the median and add one. If the
number of runs you have are:
• Within the range outlined in the table, then you have a
random pattern.
• Outside the range outline in the table, then you have a
non-random pattern or signal of change.

25. Too many or too few runs

26. Rule 4 – An astronomical data point
• This is a data point that is clearly different
from all others. Different people looking at
the same graph would be expected to
recognize the same data point as
astronomical.

27. Rule 4 – An astronomical data point

28. Rule 4 – An astronomical data point
By applying each of the four rules, you can evaluate the
run chart for a signal for change (through a non-
random variation). However, it is not necessary to find
evidence of change with each of the four rules to
determine that a change has occurred. Any single rule
occurring is sufficient evidence of a non-random signal
of change.

29. Benefits of Run Charts
The following are a few benefits of a run chart:
• Easy to draft.
• Easy to analyze and interpret.
• Does not require much technical skill.
• Straightforward representation of data.