100-Metodos-de-Qualidade-Total
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DATA COLLECTION, ANALYSIS AND DISPLAY 161<br />
Method 71<br />
C chart<br />
Purpose<br />
To i<strong>de</strong>ntify when the number of <strong>de</strong>fects in a sample of constant size is<br />
changing over time.<br />
When to use<br />
When monitoring a process to <strong>de</strong>tect changes or, when a change has been<br />
ma<strong>de</strong> to process inputs, to find out whether the number of <strong>de</strong>fects or<br />
problems also changes. C charts are used when the sample size is constant,<br />
or does not vary by more than 25 per cent of the average sample size.<br />
How to use<br />
There are six simple steps involved:<br />
1 Collect data showing the number of problems or <strong>de</strong>fects over time.<br />
Draw up a table showing the number of <strong>de</strong>fects for each lot number.<br />
The number of <strong>de</strong>fects is called C. The total number of lots is called m.<br />
2 Plot the data from the table onto the C control chart. The successive lot<br />
numbers are shown on the horizontal axis, the number of <strong>de</strong>fects or<br />
problems C is shown on the vertical axis.<br />
3 Calculate the centre line C. This is calculated as the sum of all the Cs<br />
divi<strong>de</strong>d by the sum of all the ns and is written as:<br />
C = "'i.CI"'i.n<br />
4 Calculate the control limits which are ± 3 s.d. about the central line.<br />
They are calculated as<br />
Upper control limit (UCL) = C + 3YC<br />
Lower control limit (LCL) = C - 3YC<br />
If the lower control limit is less than zero it is taken to be zero.<br />
5 Draw the central line and the control limits on the control chart.<br />
6 Interpret the results.<br />
Benefits<br />
It can be difficult to separate out random variation (often called common<br />
cause or non-assignable variation) from real variation caused by changes to