Incentives for procurers. Automatically calculated and controlled performance

February, 2015
How to make your procurers' work, i.e. pack the warehouse with what really sells? And not cite lame excuses like "our suppliers have none", "they want a high price, soon I’ll bring it cheaper". "I know my merchandise better" and so on.
How can we make a typical PCM change his approach to composing and updating the assortment based on Top X?
Few will embrace the argument that profit on his category and so his own income will grow by tens of percentage points or will even multiply. In the first episode we discussed why.
Most typical PCMs will respond to reasonably structured economic incentives (especially stubborn "idiot stars" must be replaced).


The maintenance of the Top X assortment is included in the motivation scheme. The technology and mathematics may vary and depend strongly on the KPI system and motivation formulae adopted by your company. For reference purposes we shall cite a practically tested working example.

We assume that a PCM’s income is a bonus for the results in his goods category (if the PCM caters to a number of categories, then his total income = sum of the bonuses for each).
Here the bonus is calculated from some formula (into which we do not delve):

The PCM’s Bonus = Formula.

This is how it works now in your company.

What we do:

  1. We enter into the formula the TopX factor, let’s call it KX.
    The PCM’s bonus will now be calculated as follows: The PCM’s New Bonus = KX * Formula.
  2. To calculate KX, we first define the rule for translating compliance with the category’s TopX assortment matrix into a percentage (we mark it as kAMcat).
    To do it, we take any mathematical distribution, which is added up to a unity for the given number of members, that equals to one. It is more reasonable to use a sharply decreasing distribution, as having the top-rating TopX goods item in stock will be far more important for your sales than the last one. The distribution function, along with the X value for the given category, produces each goods item’s relative weight in TopX. Thus, for certainty, if X = 5, then there are five such goods items, and each one’s weight is considerably lower than the preceding one’s (this is our recommendation; mathematically, all can be assigned equal weights), and all the five goods items" weight add up to a unity.

    Here is an example of the distribution function:

    The function was empirically selected on the basis of an analysis of relevant sales statistics — but those pertaining to one goods category in one company. So it is not positioned as a universal one.
    Still, we shall hardly err much if we suppose that this distribution will be suitable, with a satisfactory level of accuracy, for any goods category consisting of well-selling consumer goods.

  3. Goods items" popularity and their resulting are constantly changing, and their availability in stock changes even more often, so a period’s (e.g. a month’s) average kAMcat value will be usable in practice.

    dia$par will automatically take an availability snapshot in the background at the required regularity (this is adjustable; it is usually enough to do this every morning and evening) and translates it into kAMcat. After the period’s end, an average kAMcat is provided for each goods category.

  4. kAMcat Translation into KX: kAMcat is converted into the KX factor via preset "buskets" of values (as the simplest option, you may invent a formula yourself).
    For example, if we consider a 75% kAMcat a good one (this is experientially determined and depends much on the distribution function, the X value, the market situation and the work format), then if kAMcat falls into the 75% to 80% range, KX=1; 80% to 90%, KX=1.2; 90% to 100%, KX=1.7 (here you risk nothing).
    And, conversely, if TopX compliance is less than 75% but higher than 70%; KX=0.9; between 60% to 70%, KX=0.75; 50% to 70%, KX=0.5; and less than 50%, KX=0, i.e. you are fired.

    Accordingly, if a PCM’s compliance with the Top X requirement in his category is 75%, his bonus remains the same. If he performs better, his bonus grows considerably, and if worse, it plummets.

    A possible (though not mandatory) consequence is that some very shrewd members of the PCM race might artificially hold back goods that are hard to purchase by overpricing them; but, on the other hand, there is some sound economic logic to it (scarcity causes price growth and the company may profit on the whole), and, on the other hand, this practice can be stopped by completely eliminating PCMs" interference in automatic price management.

    In the next episode:
    — Optimal allocation of goods within the retail chain

Being inside dia$par. Some stories
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