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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 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 a as few other Value Finders as possible (ideally zero).

The best way to achieve this is to situate levers and criteria in the same space, in other words, place them at the same level of granularity. For instance, one of our typical cases is turnover stability when a customer wants to streamline its pricing: the customer wants to change list prices (situated in a product space) and discounts (situated in a product family x customer group space) but wants its global turnover to stay the same. Where should we situate the constraint of equality between current and historical turnovers?

• Placing this constraint at the highest granularity - in what would be the root global space - is the most straightforward solution. But this sole criterion would constrain all the prices and all the discounts, augmenting their interferences and risking to overconstrain lots of them if there are other criteria elsewhere in the system.
  
  • It also can cause accuracy problems if the minimum amplitude of the Value Finders is not small enough: the more Value Finders the smaller it should be to ensure precision since the error of each local Value Finders is cumulated in the global criterion.
• Placing the constraint at the lowest granularity, in product x customer spaces, would create lots of agents and give lots of neighbors to Value Finders. For instance, each discount would have as many neighbors as there are (product, customer) pairs for a given product family x customer group space. This can be a lot, and these neighbors may be antinomic at times so it would take more time to converge.
• Placing the constraint at the same granularity as the discounts is the best solution here as it would ensure that each discount has only one neighbor, and each constraint is influenced by only one discount and some prices. Then, discounts would always be able to satisfy their constraint whatever the prices do.

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