data model book Vol I page 76-79.
=========================
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Wai Tam sent the following on 6/30/2010 8:13 AM:
Hello,
Any takers for this question?
Thanks,
Wai
----------------------
Take for example I have the following virtual products and associated
variants with the shown features
polo shirt
color:
red...price $1
green...price $2
size:
small...price $3
medium...price $4
clown shirt
color:
red...price $5
green...price $6
size:
small...price $7
medium...price $8
dress pant
color:
red...price $9
green...price $10
size:
small...price $11
medium...price $12
All of these product variants use the same similar product feature
categories 'color' and 'size' and the features have differing pricing
depending on what virtual product it applies to. Does that mean it is
necessary to create separate feature categories and associated features for
Polo shirt, Clown shirt, and dress pant as specified below?
ProductFeatureCategory:
POLOSHIRT_COLOR
ProductFeature:
POLOSHIRT_COLOR_RED
POLOSHIRT_COLOR_GREEN
POLOSHIRT_SIZE_SMALL
POLOSHIRT_SIZE_MEDIUM
ProductFeatureCategory:
CLOWNSHIRT_COLOR
ProductFeature:
CLOWNSHIRT_COLOR_RED
CLOWNSHIRT_COLOR_GREEN
CLOWNSHIRT_SIZE_SMALL
CLOWNSHIRT_SIZE_MEDIUM
ProductFeatureCategory:
DRESSPANT_COLOR
ProductFeature:
DRESSPANT_COLOR_RED
DRESSPANT_COLOR_GREEN
DRESSPANT_SIZE_SMALL
DRESSPANT_SIZE_MEDIUM
If the above configuration is correct, does that imply that in practice,
features and feature categories easily grow to be very large and difficult
to manage?
Thanks
Wai
