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Part Four
Improvement
Pareto analysis
useful. Pareto analysis is based on the phenomenon of relatively few causes explaining the
majority of effects. For example, most revenue for any company is likely to come from
relatively few of the company’s customers. Similarly, relatively few of a doctor’s patients will
probably occupy most of his or her time.
Worked example
Kaston Pyral Services Ltd (D)
The KPS improvement team which was investigating unscheduled returns from emergency
servicing (the issue which was described in the cause–effect diagram in Fig. 18.11)
examined all occasions over the previous 12 months on which an unscheduled return
had been made. They categorized the reasons for unscheduled returns as follows:
1 The wrong part had been taken to a job because, although the information which the
engineer received was sound, he or she had incorrectly predicted the nature of the
fault.
2 The wrong part had been taken to the job because there was insufficient information
given when the call was taken.
3 The wrong part had been taken to the job because the system had been modified in
some way not recorded on KPS’s records.
4 The wrong part had been taken to the job because the part had been incorrectly issued
to the engineer by stores.
5 No part had been taken because the relevant part was out of stock.
6 The wrong equipment had been taken for whatever reason.
7 Any other reason.
The relative frequency of occurrence of these causes is shown in Figure 18.11. About a
third of all unscheduled returns were due to the first category, and more than half the
returns were accounted for by the first and second categories together. It was decided
that the problem could best be tackled by concentrating on how to get more information
to the engineers which would enable them to predict the causes of failure accurately.
Figure 18.11 Pareto diagram for causes of unscheduled returns