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100-Metodos-de-Qualidade-Total

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228 <strong>100</strong> METHODS FOR TOTAL QUALITY MANAGEMENT<br />

Method 99<br />

X moving range (X-MR) chart<br />

Purpose<br />

To i<strong>de</strong>ntify when a value is 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 mean value changes. X<br />

moving range is used to monitor variables data, when the values can vary<br />

over a continuous range, and when the sample size at each sampling point<br />

is 1. This, for example is the case with monthly sales figures, when there is<br />

only one figure, production figures or when the cost of taking the<br />

measurement is very high.<br />

How to use<br />

There are six simple steps involved:<br />

1 Collect the data. Draw up a table showing the readings for each of the<br />

samples at each lot. The number of samples in each lot is 1. The total<br />

number of lots is called rn.<br />

2 For each lot calculate the moving range by subtracting the X value from<br />

the previous X value. There is no moving range for the first X value.<br />

3 Working down the table, add together all the X values and divi<strong>de</strong> the<br />

result by the number of lots rn. This value is the grand mean and is written<br />

X (called X bar). Again, working down the table, add together all the<br />

moving range values and divi<strong>de</strong> by rn-l. This value is written MR.<br />

4 Calculate the control limits for the X-MR chart using the formula:<br />

Upper control limit (UCL) = X + 3 X MR/d2<br />

Lower control limit (LCL) = X - 3 X MR/d2<br />

d2 is a constant that when multiplied by 3MR gives 3 s.d. for the X-MR<br />

chart.<br />

S Plot the data from the table onto the X-MR control chart. The successive<br />

lot numbers are shown on the horizontal axis; the X values are shown<br />

on the vertical axis.<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

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