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252 Part IV: Quality Assurance
Table 19.2 Percentage of nonconforming units in 30 different shifts.
Shift number 1 2 3 4 5 6 7 8 9 10
Percent nonconforming 5 9 12 7 12 4 11 7 15 3
Shift number 11 12 13 14 15 16 17 18 19 20
Percent nonconforming 5 8 2 5 15 4 6 15 5 5
Shift number 21 22 23 24 25 26 27 28 29 30
Percent nonconforming 8 6 10 15 7 10 13 4 8 14
Percent nonconforming
16
14
12
10
8
6
4
2
5
9
15
15 15
15
14
13
12 12
11
10 10
8
8
8
7 7
6
7
5 5
6
5 5
4
4
4
3
2
3 6 9 12 15 18 21 24 27 30
Shift
Part IV.B
Figure 19.5 Run chart.
A run chart of the data in Table 19.2 is shown in Figure 19.5, in which we have
plotted the percentage nonconforming on different shifts over a period of 10 days,
starting with the morning shift.
From this run chart we can easily see that the percentage of nonconforming
units is the least in the morning shifts and the highest in the night shifts. There are
some problems in the evening shifts too, but they are not as severe as those in the
night shift. Since such trends or patterns are usually caused by special or assignable
causes, the run chart will certainly prompt the management to explore how
the various shifts differ. Does the quality of the raw material differ from shift to
shift? Is there inadequate training of workers on the later shifts? Are evening and
late-shift workers more susceptible to fatigue? Are there environmental problems
that increase in severity as the day wears on?
Deming (1982) points out that sometimes the frequency distribution of a set
of data does not give a true picture of the data, whereas a run chart can bring
out the real problems in the data. The frequency distribution gives us the overall
picture of the data, but does not show us any trends or patterns that may be present
in the process on a short-term basis.