from R&D Innovator Volume 2, Number 3
Wall: Dealing with
Minor is senior staff researcher at Syntex Research, Palo Alto,
California. He and
his team received the 1992 Award for Statistics in Chemistry from
the American Statistical Association.
the "good old days," an expert researcher could adjust a
few critical variables, visualize patterns, infer useful concepts,
and make discoveries that led to new products.
Today the extreme complexity of research reduces the
possibility that this process will succeed.
Too often, dynamic behavior and patterns of noise or chaos
are the rule rather than the exception.
scientists unwittingly make research even more complex.
They tend to over-complicate explanations for their
research and either oversimplify or ignore the impact of inherent
variation and new or unknown effects.
today's research, "Murphy's law" creates such a
fundamental barrier to our study of nature that I call it
"Murphy's wall" instead.
The task of researchers is to conquer "Murphy's
wall" and reduce their ignorance about a research problem so
that it can become a manageable engineering project.
To put it another way, our experiments must eliminate vast
amounts of ignorance within reasonable limits of time and
to the Rescue
two decades of collaborating with scientists, I've developed
statistical methods to surmount these barriers to discovery and at
the same time enhance our intuitive and cognitive abilities.
About 10 years ago I developed a strategy that cut research
effort by as much as 50 percent.
Strategy of Research (SR) creates optimal experimental schedules
to reduce research complexities.
The basic procedure is to apply the power of multivariate
statistical analysis to scientific research.
SR is an efficient way to advance research from “the
bench” to the “bottom‑line.”
I recently reviewed this strategy.1
is some evidence of SR's effectiveness.
The technique shortened a high-performance liquid
chromatography of complex samples from many weeks to overnight.
It dramatically improved the sensitivity and selectivity
for automatic clinical blood analysis. And it rescued a color copying business by solving, in six
months, critical problems which had plagued their technology for
you test one variable at a time, you will need many experiments
before you get significant feedback. The beauty of SR is that it converts experiments with large,
batch‑oriented schedules to an interactive sequence of
mini‑schedules (small chunks of experiments).
some aspects of research can be made routine—such as collecting,
storing and looking at data—research by definition cannot follow
a simple formula. The
mini-schedules of SR provide feedback and stimulate an interactive
thought process that helps plan for future experiments.
Exploration within these mini-schedules can be merged with
a global view of the entire project, while filtering out
unanticipated effects or noise.
If necessary, investigators can go back and forth between
the essential experiments, all the while increasing their
knowledge of the problem.
consists of six basic types of testing:
Screening experimental procedures and/or instrumentation
for operational problems.
Simple replication to test and establish inherent
Simple ranging to explore and validate limits of operation.
Screening groups of synergistic effects.
Resolution of synergistic effects from important groups.
Diagnosis for missing effects that affect predictions.
How Does it Work?
make the process more comprehensible, I'd like to explain how SR
helped develop a process for manufacturing a three-layer laser
disc. The disc had a
polymethylmethacrylate (PMMA) layer for rigidity.
Bonded to this was a collapsible microporous (cmp) layer. At the bottom was a thin layer of reflective metal.
store information on the disc, a laser beam heats the metal, thus
collapsing the adjacent cmp structures. To read the disc, we detect these distortions by reflecting a
low-power laser beam from the metal layer through the PMMA and cmp
disc is constructed by spin-coating a solution of PMMA onto the
rigid PMMA substrate. When
it coagulates, the PMMA forms the cmp structure. When the disc is
dry, the metal layer is deposited onto it.
problem was twofold: How
to control the process to get a product—and then how to optimize
the process? If the
disc was to satisfy all criteria, 11 physical controlling factors
(e.g., cmp polymer type, solvent type, etc.) and several measured
properties (e.g., surface evenness, reflectivity) must be
considered. In a
sense, the task corresponds to finding a needle in a
SR, a minimum of 150 experiments would be needed just for the
basic data for manufacturing.
Using SR, fewer than 100 experiments identified and
validated a synthesis for the high-performance disc.
SR allowed such efficiency because so many possible
combinations were actually insignificant, so there was no need to
waste experimental time on trivial phenomena.
By testing bundles of interactions, the dominant
interactions became obvious, and that guided the selection of
learning about the relationship between performance and the 11
physical process variables, we could determine which process would
give best results. The
most promising predictions were converted into experiments, and
the results were fed back into the analysis and optimization
routines to check for missing effects, update predictions, and
home in on even better results.
project was complicated by large experimental noise, truncated or
rounded measurements, malfunctioning equipment, and human error.
As usual, the values of some factors were
the maximum permissible spin depended on the viscosity of
the melted plastic.
the end, an outside testing firm verified that the discs surpassed
all expectations. These
results supported a successful process patent.
The SR approach produced a wide enough knowledge base to
scope out the entire region of the patent application. The patent
had broad claims which will be difficult for a competitor to
the Berlin wall, Murphy's Wall will not be overturned—if
anything, it's likely to get higher. Even so, multivariate statistical methods help produce better
solutions to complex projects—within the time and cost
constraints of industry.
22, 751, 1992.