This section answers the following questions:
- Which features should be dimensions to define? Or none?
- In which space to situate the criteria?
- How to define aggregating computations?
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Main idea: dimensions are non-contingent features required to the space required to situate a variable or a criterion in the problem. For instance if there are the following features: color, length, width, surface; then only color, length and width should be dimensions, but not surface, as it can be inferred from two of the other features (length and width).
A critical step of problem modeling is the definition of the dimensions and of the spaces they form.
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If you make a Brand dimension, you will end up with a "holey cheese" space that is difficult to navigate and the system will be way larger than needednecessary. Instead, the Brand feature should be used to define a scope within the Category x Assortment x Packaging space.
A ground-rule could be: dimensions are non-contingent features to the space required required to situate a variable or a criterion in the problem.
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