Eating soup with a fork . .
. is easier than attempting producibility
measurement. Everybody does the latter differently and, as would be imagined,
with varying degrees of success.
We suggest, based on the experiences of a number of major DoD contractors,
that the two tools which will be described shortly be considered for the
measurements used to predict producibility.
Properly applied, they offer companies and DoD a flexible but consistent
means of determining producibility by evaluating standard factors that impact
the production process.
The tools are markedly different but not mutually exclusive. In fact, they
can complement each other in the process of predicting producibility.
Both are data based; one relies on experience and intuition, the other on
statistics and quantification. While they can be used separately, it is
recommended that, where practical, both be used. Similar results from each will
serve as confirmation of the validity of the producibility measurement;
disparity will signal a need for checking of the data used and how results were
Tool 1 . . . is judgmental in nature, relying to a
great extent on the past experiences of the persons using it and the data they
have available as they relate to the current situation and evaluation of the
design, processes, technologies, materials, and other resources that are being
considered for the program. It can be used throughout product development and
production but is particularly useful in the early development stages of a
Tool 2 . . . is goal oriented, using data derived
from similar past efforts and from the day-to-day work being done on the new
program. It can be used throughout product development and production but is
particularly useful in a continuing assessment role as data from development and
Used properly, both tools . . . can provide management the information it
needs to improve producibility by identifying weaknesses as well as new
processes and technologies needed to improve manufacturability.
These tools should be applied to each distinctive part that makes up the
whole. That way a specific component or module that could pose producibility
problems can be identified at the earliest possible point and action taken to
correct the difficulty or another production process