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Optimizing Inventory with XpertMartTM
Using Minimums with No Sales HistoryOne of the myths that keeps retailers from aggressively using Minimums to control their inventory is that you need months of historical sales data in order to properly set the parameters. Minimums are fine and well for styles that have been around for a long time, the myth goes, but are irrelevant when it comes to new styles being stocked for the first time, for example at the beginning of a season. However, we believe that you should always use Minimums, even as you just begin receiving merchandise.
Suppose you receive a new shipment of men’s casual western boots you’ve never carried in stock before. To start off, you are obviously going to have to use an educated guess to set your initial stock level. The best thing to do is to look for a style that is similar to the new style, in this case another casual western boot, and use its historic sales data to set your initial Minimums. If there is no similar style to rely on, try picking another style that is in the same Type in your Departments catalogue and move up to Sub-Class and Class until you find a good fit. For example, if you cannot find a men’s casual western boot, perhaps you can find a men’s dress boot. So far so good. Now once the boot is on the shelves, we begin collecting sales data. As soon as we get our first sale for a particular item, we can begin to calibrate our Minimums. Suppose we are looking at a size 11 brown boot. For the first three days there is no sale, and then on the fourth day there is a sale. This means that so far we can expect one sale every four days.
Now suppose your goal is to keep 45 days of sales in stock. If we were to calculate this item’s Minimum, we would need to use a multiplication factor of 11.25 (since there are 11.25 4-day periods in 45 days). However, we do not yet have enough reliable data on which to calculate a Minimum (your multiplication factor should never be higher than 3.5 or 4). Therefore, the best option at this point is to set our Minimum so that we are only ordering merchandise for the next two weeks and then recalculate our Minimum when we have more data. If we want to keep 14 days of sales in stock, we need a multiplication factor of 3.5, barely within our acceptable range for a multiplication parameter. So far, we can conclude that we need to have 4 pairs of the size 11 brown boot in stock to cover the sales we are anticipating over the next two weeks.
Now obviously this Minimum is not terribly accurate since we are only relying on four days of data. For all we know, that sale was a fluke and there won’t be another sale for days to come. Or maybe the first few days were flukes, and the size 11 brown boot will really start to sell. As we mentioned earlier, the larger the Multiplication Factor, the less accurate the Minimum. However, it is better than our earlier educated guess since it is using actual sales data, and it is far better than no guess at all. Obviously, as time goes by and we gather more data, we can continue to calibrate our Minimums and increase their accuracy.
To continue with our example, let’s suppose that after two weeks of sales we have sold 5 pairs of the size 11 brown boot. Now we can recalculate our Minimums, and this time we have enough data to aim for 45 days of sales in stock. Our multiplication factor of 3.2 (45 divided by 14) is within the acceptable range. We would use the following parameters:
April 1 – April 14
Our new Minimum is 16. As we’ve gathered more data, it turns out that the first four days were somewhat atypical and that our first Minimum was not very accurate. Now we have a new Minimum and it is based on 14 days of actual sales, not 4—so our educated guess is getting increasingly educated. The closer our multiplication factor gets to 1, the more accurate our Minimum will be. The ideal, of course, is to have 45 days of stock as our Minimum based on 45 days of actual sales.
Now suppose that after three weeks of having our new boot on the shelf, we have sold 10 pairs of the size 11 brown boot. Let’s calculate our Minimum once more, with the following parameters:
April 1 – April 21
Our new Minimum is 21, or 22 if we choose to round up. Notice that the difference between this Minimum and our last Minimum is not as great as it was at first. This is because our accuracy is increasing (the multiplication factor is down to 2.14). We are starting to settle on a Minimum that is most likely in the 16 – 22 pairs range—not bad after only 3 weeks of sales! Let’s finish our example by supposing that during the first 45 days, the size 11 brown boot has sold 20 pairs. Let’s look at our parameters one last time:
April 1 – May 15
We now know that our Minimum should be 20 pairs. We can have great confidence in this number as our multiplication factor is 1, i.e. we are relying on actual sales for the full 45 days we want to keep in stock. We can now use this Minimum to determine future purchases and stocking levels. More importantly, along the way we were able to use Minimums for the same purposes that became increasingly more accurate and useful (we began with 11 as our Minimum but after less than two weeks were already at 16, not too far from the real Minimum). So even when we are receiving totally new merchandise, we can begin to optimize our inventory within a short period of time. There is some question about how often we should calibrate our Minimums. In the beginning, our Minimums become increasingly more accurate, as even the difference between 4 days of sales data and 10 days can be significant. At this point, we can keep a higher percentage of new styles at the warehouse: if Minimums go up, then we ship more to the stores; if the Minimum is lowered, then since it is still early in the season, the excess will be sold down. A good rule of thumb is to recalculate your Minimums once a week until you reach a Multiplication Factor of 1. After that, it’s still a good idea to recalculate your Minimums every week. As we’ve mentioned, you will need to calibrate your Minimums with greater frequency if sales seem to be rising or falling more than usual.
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