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- Classical Approaches vs. Quantisweb:
This slide compares various points between classical methods like One Factor At A Time, the more modern 6 sigma way of: "Defining, Measuring, Analyzing, Influencing, Controlling" using Design of Experiments methods and Quantisweb's Multivariate approach.
- Application Examples from the Quantisweb Innovation
Formulation Methodologies white paper…
In
this section four examples are presented, where standard methods and
the novel Quantisweb methodology are used to allow for a scientific
comparison. The first example is a classical formulation case. The
second is a mixture case where the formulation is provided by a mixture
of two or more major compounds, within which, a mixture of two or more
compound subset is obtained. The third is a case where the
characteristics have very large variability. In the last case, a data
set is processed with a full factorial design involving 4 parameters of
two levels each, hence a total number of 16 experiments. The goal here
is to show that the generated behavioral laws by Quantisweb coincide
with those generated by any statistics software package in case the
number of experiments becomes large enough.
- Current Industry Practice :
The current product development process varies from
industry to industry. In many cases, it is a mix of the following
methods, but mostly dependent on both the scientists, engineer and or
the statistician knowledge and expertise, and gathering information.
....
The Quantisweb Practice focuses the scientist/engineer on
the ultimate ideal product or process. It is dependent on properties,
ingredients/inputs, and constraints based on the scientist and
engineer’s knowledge. The statistical analyses and optimization are
performed by Quantisweb.
A study in 2006 demonstrates that the Quantisweb tool integrates all functionalities such as: multi-criteria methods, statistical analysis, constraints in design of experiments, independent variables, dependent variables, discrete variables, continuous variables, an unlimited number of variables, and optimization algorithms with the exception of knowledge data bank.
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