Two biggest limitations in the use of SDSS are illogical weighting
systems and lacking proper ways to interpret the alternatives. To
overcome these restrictions and improve the modeling environment,
the proposed SDSS added CFA and detailed value estimation.
CFA offered a more analytic procedure to group the input variables
with statistical significance, and made it possible to reduce 15
variables into five factors. Another advantage of using groups
instead of a large number of inputs stands on its increased sensitivity.
Detailed value estimation suggested a possible way to
examine the alternatives and provided a foundation to select the
best possible solution with the given variables.