The Economic Benefit of Natural Process Limits
There are several ways to determine the natural process
limits for a capability baseline. The expectation
is that the limits will be defined based on a statistical
technique, such as control charts or prediction
intervals, although simple graphical techniques may
be sufficient to address the analytical needs (Wood,
Capon, and Kaye 1998).
Classical SPC uses 3σ limits for reasons of economic
benefit. There is an economic cost associated
with analyzing process observations. Any data points
outside the 3σ limits are unlikely to be part of the
normal operation of the process, so there will be few
“false alarms” (Wheeler’s Empirical Rule for a set of
homogenous data, that is, the common cause system,
is that more than 99 percent of the observations will be
within the 3σ limits with no requirement of normality
(Wheeler and Chambers 1992; Wheeler 2000)).
Using 5 percent and 95 percent limits is not statistically
“wrong,” but identifying 10 percent of the
data as signals of possible special causes is inefficient
since 90 percent or more of the signals are likely to be
false alarms. Unnecessarily analyzing 9 percent of the
events being controlled (90 percent false alarms of the
10 percent of the observations that are signals) is inefficient
when more effective tools are available. Control
chart limits are designed to be economically efficient
and simple to calculate (Wheeler and Chambers 1992;
Wheeler 2003). The 3σ limits in control charts have
been demonstrated to balance signals and false alarms
at an economically useful point. While other criteria
may be used to trigger action, they should be used by
people who understand the tradeoffs they are making.
The prescription for failing to use effective and
efficient tools builds on applying both relatively
sophisticated statistical expertise and a profound
knowledge of the engineering methodologies and
application domains associated with the processes
to be controlled. Inefficient techniques are a cost
reduction opportunity, but inefficient techniques are
arguably superior to the heuristics and intuition used
in measurement and analysis at the lower levels.
The Economic Benefit of Natural Process Limits
There are several ways to determine the natural process
limits for a capability baseline. The expectation
is that the limits will be defined based on a statistical
technique, such as control charts or prediction
intervals, although simple graphical techniques may
be sufficient to address the analytical needs (Wood,
Capon, and Kaye 1998).
Classical SPC uses 3σ limits for reasons of economic
benefit. There is an economic cost associated
with analyzing process observations. Any data points
outside the 3σ limits are unlikely to be part of the
normal operation of the process, so there will be few
“false alarms” (Wheeler’s Empirical Rule for a set of
homogenous data, that is, the common cause system,
is that more than 99 percent of the observations will be
within the 3σ limits with no requirement of normality
(Wheeler and Chambers 1992; Wheeler 2000)).
Using 5 percent and 95 percent limits is not statistically
“wrong,” but identifying 10 percent of the
data as signals of possible special causes is inefficient
since 90 percent or more of the signals are likely to be
false alarms. Unnecessarily analyzing 9 percent of the
events being controlled (90 percent false alarms of the
10 percent of the observations that are signals) is inefficient
when more effective tools are available. Control
chart limits are designed to be economically efficient
and simple to calculate (Wheeler and Chambers 1992;
Wheeler 2003). The 3σ limits in control charts have
been demonstrated to balance signals and false alarms
at an economically useful point. While other criteria
may be used to trigger action, they should be used by
people who understand the tradeoffs they are making.
The prescription for failing to use effective and
efficient tools builds on applying both relatively
sophisticated statistical expertise and a profound
knowledge of the engineering methodologies and
application domains associated with the processes
to be controlled. Inefficient techniques are a cost
reduction opportunity, but inefficient techniques are
arguably superior to the heuristics and intuition used
in measurement and analysis at the lower levels.
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