# Seven Basic Quality tools documents

**Definition of Quality Management**-- it is a method for ensuring that all the activities necessary to design, develop and implement a product or service are effective and efficient with respect to the system and its performance. It is also a principle set by the company to endure the continuous advocacy of quality services and products, or the further improvement of it.

Welcome to QT-charts knowledge base section. Hopefully you will find some of them useful in your work.

(Read articles below to learn more.)

*c* Charts

original text www.freequality.org

Many tools are
available for use in statistical quality control. Control charts are useful in
determining if there is a change in the performance of a process. Some examples
of control charts include X-bar and R charts which deal with variables of a
product that can be measured, and control charts for product attributes such as
*p* charts, *u* charts, and, the focus of this paper, *c*
charts.

*C *charts, or, as they are less
commonly known, count charts, deal with the number of defects in units all of
uniform size and/or quantity. *C *charts graph the total number of
nonconformities found in each piece or unit that is inspected. If the number of
nonconformities for a particular piece lies above or below the upper or lower calculated
control limits or if all of the points lie within the control limits but behave
in a nonrandom manner, then it is likely that the process is not in control and
should be adjusted to prevent the defects before continuing with production.

There are certain
procedures that need to be followed in order to create a *c *chart. The
first step is to find the average number of defects per unit, represented by c
bar, and the standard deviation, represented by the square root of c bar, for
the process. Next, plot the average number of defects on the graph. This is
known as the central line. Then, determine the upper control limit, UCL, by
adding three times the standard deviation to the average number of defects:
UCL = c bar + 3Öc bar. Subsequently,
the lower control limit is calculated in the same manner, except instead of
adding three times the standard deviation to the average, it is subtracted: LCL
= c bar - 3Öc bar. Then the upper and
lower control limits are plotted on the graph. After that, using the observed
data from the process, plot the number of defects for each unit on the graph
and connect them with straight line segments. The x-coordinate of the graph
should represent the individual units of the process, while the y-coordinate of
the graph should represent the number of defects. Finally, using the completed *c
*chart, determine if the process is in control or not in control by checking
the plotted points to see if they are within the control limits, or if there is
a nonrandom pattern to the data. If either or both of these cases exist, the
process should be investigated and/or corrected to prevent future defects.

*C *charts
have many applications for quality control in manufacturing and processing.
For example, in automotive production, particularly painting, the number of
defects in the paint of a car can be measured and plotted on a *c *chart.
Then it can be determined whether or not the paint job is within the control
limits for the process. Other examples include any process where the number of
nonconformities can be counted. The number of defects in a woven carpet, the
number of defects in a shoe, and also the number of defects in a roll of
aluminum foil are all processes that are applicable to *c* charts.

As you can see,
the *c* chart is a very valuable tool in quality management. If the
process that the *c* chart is being applied deals with units with defects
or nonconformities that can be counted, then it is certainly advantageous to
employ the use of *c* charts to that process. With the intensifying focus
on quality and quality management, *c* charts are a very powerful asset to
the processing and manufacturing fields.

For more
information about *c* charts, please see *Statistical Process Control
and Quality Improvement* by Gerald M. Smith. This book gives good insight
into the different applications of not only *c *charts, but also the other
various control charts and tools available for use in quality management.
Another book that is recommended for more information on the applications of
statistical tools in the area of quality assurance is *Statistical Methods
for SPC and TQM* by Derek Bissell. Not only does this book give examples of
the utilization of statistical process control for products and processes, it
also gives examples as to where these methods can be applied in general
business areas.