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Systems of interest to scientists, engineers, manufacturers and
economists have become so complex that making optimal decisions about
them is now impossible without sophisticated help. Success
increasingly relies upon accurate and reliable models of system
behaviour – using experimental data, subject area knowledge
and sophisticated empirical techniques. The inputs to these
models are those variables that the investigator has control over or
can observe. The outputs are the responses that measure system
performance. Integrating such models into a collaborative design
process is essential if you want the best solutions. We
will help you to create and use the best models available.
No model will capture all the subtle behaviours of a
complex system, but once you have models that are 'good enough' (which
can be measured if you know how), you have the ability to carry out the
following with unprecedented efficiency – leading to better
solutions, faster
- Visualisation & communication of trends in
system behaviour
- ‘Intelligent’ high-dimensional, multi-response
optimisation
- Sophisticated simulation/capability studies
- Assessment of complex trade-offs between cost &
system performance
- Measuring system sensitivities & average
performance in the presence of uncertainty
Our techniques are unique in producing this valuable
information in an integrated and reliable way. We can convert the
intractable complexity of essentially any
system into a convenient virtual surrogate that can be
manipulated on a computer. The key (and difficult
challenge) is to produce models that are good enough from necessarily
limited data. For most contemporary
problems this requires mathematical sophistication, original project
management and problem-solving skills, experience and proprietary
software tools. Commercial software with two weeks of training
are not good enough, however much some managers may hope.
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