This section answers the following questions:

• Which dimensions to define or notfeatures should be dimensions?
• Where In which space to situate the criteria?
• How to define aggregating computations?

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Choose Dimensions in Spaces

Main idea: dimensions are non-contingent features to the space required  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 required to the space required to situate a variable or a criterion in the problem.

The Granularity of Levers and Criteria

Main idea: when possible, situate levers and criteria in the same space, in other words, place them at the same level of granularity.

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Levers are often imposed by the customer (typically some kind of prices and discounts): if the customer wants discounts at a product x customer family level, then so be it. Criteria, however, are often more vaguely described. It is up to optimization engine Optimization Engine configuration to translate the customer needs and requirements into actual criteria. A good practice is to seek to simplify the work at the future agents' local level, i.e. at the level of the lever. What is simple for a Value Finder? It is when it has few neighbors (ideally just one) and when these neighbors are shared with as few other Value Finders as possible (ideally zero).

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Of course, in some cases, these three propositions may not be exactly the same from a domain point of view and there are often other considerations to weigh in when making this choice. This is the whole question of understanding the customer's needs.

Intermediate Variables

Main idea: large aggregating computations with lots of inputs and parameters should be avoided.

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