Honeywell’s
POMS Explorer™
POMS Explorer’s Data Analysis and
Graphics
The following general workflows are typical of those
used for an analysis
- Correlated Variables Identified.
POMS Explorer creats a customized matrix to look
for single pairs of correlated variables. The matrix
uses
all available data. Missing values are replaced with
the mean of the remaining values using data-conditioning
routines. POMS Explorer’s ability to do this type
of data conditioning can be extremely valuable when
compensating for the effects of missing data—a
benefit not readily available in most statistics
packages.
- Outcome Modeled. Principal Component Analysis and
Stepwise Multiple Linear Regression (PCA/SMLR)
can be
used to condense the correlation information in
the raw variables into a set of key factors.
These factors
are then used to model the outcome variables using
SMLR with cross-validation. This multivariate
analysis method
is called Principal Component Regression or PCR.
- Patterns Identified. POMS Explorer uses static
and animated multi-dimensional imaging techniques
to
show
patterns in the data. This allows examination of
the behavior of critical process parameters in
groups
of
lots ranked, for example, either by lot number
(production date) or by the process outcome parameter.
Critical Process Parameters Displayed
POMS Explorer creates sophisticated Visual Process
Signatures™ that are easy to understand. These
Signatures use a single, animated image to display the
relationships of many factors to each other, to the
rest of the data, and to the process outcomes. Combining
multi-dimensional visual enhancements of the tabular
information along with the quantitative results of the
PCR analysis, the Visual Process Signatures provides
clear confirmation of the major quantitative findings
in a readily understandable form. This is especially
beneficial for those whose core technical expertise
is in areas other than statistics but who do have a
high degree of influence over process outcomes –
chemists, engineers, and supervisors as well as the
manufacturing operators. By illustrating how and why
their process is performing the way it is, without resorting
to mind-numbing tables of numbers and statistics, they
quickly achieve significant enhancements in process
performance.
POMS Explorer displays the Process Signatures as dynamic
images that can rotate in three dimensions on the screen
to show additional information. Historical performance
of the manufacturing process can also be displayed as
rolling averages.
POMS Explorer Shows All the Key Drivers
to Maximize the Outcome
Using PCR, the combination of most critical, controllable
process parameters can be identified. POMS Explorer
finds the smallest combination of process parameters
that have the greatest effect on the process outcomes.
Specific recommendations for process improvements are
then formulated based on the combination of key process
indicators that POMS Explorer has identified.
IImproved Process Outcome With Lower Costs
For all the data used in a typical study, the process
parameters for each batch are set within their approved
ranges. Therefore, the manufacturer is able to test
POMS Explorer’s findings directly within the manufacturing
process without the need for additional small-scale
experimentation – a major cost advantage of retrospective
data analysis. The process improvement recommendations
are derived from the variations that occurred in the
manufacturing process when operated under approved conditions.
When implemented the recommended changes allow the manufacturing
staff to bias their process toward the best possible
outcomes without the need to change their manufacturing
technology or get it re-approved by the FDA –
a tremendous savings in time and money.
Click here to learn more about POMS Explorer
( Aegis Discoverant)
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