Measuring for Business Success

How Standard Measures of Inventory Profitability Can Mislead You

If you are the owner of a small to medium-sized retail store (or stores), typically 754573314f your investment is in inventory. In addition, except for add-on services and custom orders, nearly all sales revenue and profits are generated by your inventory. Most importantly, excess inventory can suck up all your cash, and force you to insolvency and closure.

Therefore, one of your primary goals is to maximize the return on your inventory.

The standard industry measure of how well you are accomplishing that is called “gross margin return on inventory investment,” or GMROI. GMROI is defined as:

GMROI can be calculated for the whole store, for individual items, for departments, and for any arbitrary group of items.

The higher the GMROI, the more money you make for every dollar you have invested in inventory.

Retail consultants typically give the following advice:

• Calculate the GMROI of your inventory items
• Identify the items with the lowest GMROI, and those with the highest GMROI
• Look to replace the items with the lowest GMROI, with items more like those that have the highest GMROI

In principle, the above is very good advice. By focusing on the types of merchandise that yield the greatest profit, a retailer will be able to improve his cash flow and profits.

It therefore seems that all you have to do is to install a POS and inventory-management system that provides that type of information (a caveat – except for the high end, most POS systems do NOT track average inventory, and therefore cannot compute GMROI or inventory terms at the item level).

In practice, however, the solution is not as easy as the above suggests. This is because an item’s GMROI is greatly affected by the retailer’s own actions. In many cases, to improve GMROI, what the retailer has to do is change his processes, not his merchandise!

Put it another way – no matter what you replace an item with, GMROI will remain low, if the underlying cause of the low GMROI is one of these:

• Excessive stocking of slower-moving items

  If you carry the same average inventory of a slow-moving item as a fast-moving one, it will naturally have a lower GMROI. For example, we had a customer that would always purchase more of any item in one department, if his stock was down to 3 dozen of the item. For his top sellers, that represented only 1 week’s sales. But for the slower-moving ones, it would take more than a month to sell 3 dozen. Naturally, the GMROI of the slower-moving items were much less than the GMROI of the faster-selling ones.

  If you can reduce your stocking level of a slow-moving item, you will increase its GMROI. That’s because your average investment will be lower. In general, you don’t have to replace merchandise with a low GMROI, if you can reduce its average inventory without reducing its sales.

• Buying overly large lots at a time

  You may be buying large quantities of an item because you get a volume discount, or because the vendor convinced you to stock up on it. For example, one of our retailers was tempted by a vendor’s volume discounts. He would purchase quantities large enough to get to the discount level of much larger stores than his. Unfortunately, those orders were so large they would take over 6 months to sell. As a result, his cash flow suffered greatly, and the GMROI of those items was very low.

  The good news is that you don’t have to look for replacements for items with this problem. All you need to do is exercise cash-flow discipline, and order smaller quantities more frequently. GMROI will rise proportionately, and you will find yourself with more cash.

• Extreme markdowns

  You may be using an item as a “loss-leader” by pricing it at cost, or even lower. Or, the selling season may have been shorter or less pronounced than normal, and you ended up having to offer a drastic clearance sale on the seasonal items. Either one of those will cause the items to have a low measured GMROI.

  Again, the good news is that you don’t have to replace the merchandise. Just ignore the apparently low GMROI – the profitability is actually higher than your calculations indicate. If you are using the item as a loss-leader, you should charge the losses to marketing. If you had offered a clearance sale on seasonal items, resolve to be more conservative next time. The point is: if you manage the item’s pricing the same way you do your other items, its GMROI will be higher, and you may not have to replace it, after all.

• Moving items to slow-moving display locations

  In any store, the merchandise in some parts of the store move faster simply because of their location. Island displays, eye-level shelves, the front of the store – merchandise in those areas will sell more than the merchandise in other areas. If an item is not in those high-traffic areas, expect its sales to be lower than if the item were displayed in the high-traffic areas. That will bring GMROI lower.

  Replacing those items won’t be likely to raise your sales, profits, or GMROI. Any replacement item you place in a low-traffic area will still have lower sales than if it were in the high-traffic areas.

  Unfortunately, the high-traffic area of any store is limited. So, you can't put all of your merchandise in high-traffic areas.

  The solution is to keep less stock of the items you place in the low-traffic areas. By lowering average inventory in proportion to the lower sales volume, you will be able to free up cash and raise the GMROI.

As you can see, there are a lot of reasons why GMROI can be lower for some items than others. You should first try to pinpoint why the GMROI is lower, and to try ways to bring it up. Ask how to have your system calculate the “potential GMROI” of an item, which is what it will be after you make all the adjustments suggested above. You should only replace items if their “potential GMROI” is still low.

How to See the Underlying Trends in Performance Indicators

If you own or manage a business in a highly volatile industry like the retail trades, hospitality, transportation, and foods, and you rely on the raw data in daily or monthly reports, you won't be able to spot the real trends in the indicators. This will make it impossible to use the indicators to detect opportunities and threats early.

Your goal is to be able to spot when trends of your indicators are changing: when growth has started to slow, or even to turn negative. That tells you that momentum has been lost, and you need to take action to reverse the trend.

When we've charted revenue data from customers in the retail trade, for example, we find that the values for hourly, daily, weekly, and even monthly sales cause the charts to zigzag violently. Trying to determine whether revenues are going up or down based on those numbers would be useless. If you acted based on the values of the last few measurements, you would be flip-flopping all the time.

Thus, the first thing to do is to "smoothen the curve" of the chart. There are various statistical techniques for doing this. Ensure that your decision-support system uses one. You can tell if it does, by graphing the report data. It should look reasonably smooth -- enough for you to have some idea of the trends when you view it.

Even after “smoothening”, however, there is more to be done. If the business is seasonal, most indicators will also be greatly affected by the seasons. Think of the impact of the holiday shopping season on the retail trade. If you just compared monthly sales against the previous month, you would always be euphoric in December and totally despondent by February. Again, relying on the raw data and comparing one period against the preceding periods will not make sense.

The simple answer seems to be: compare each period against the same period a year ago. This is what all public companies do in their quarterly and yearly financial reports. However, this raises a problem: how do you detect a trend? And, how do you detect a change in the trend? If you compare one period's measurement against the previous month's and against the year-ago month's, you still have no idea of the trend.

Let's consider an example. If you were told that: "profits are up 5vs. last month, and 10vs. the same month a year ago", could you safely conclude that profits are trending up? Not really. What if the profits of the prior 6 months had been up 200ver their year-ago equivalents? If that were the case, profits are actually starting to trend down after a huge growth 6 months ago.

That example shows the need to have a continuous chart of smoothened, seasonally-adjusted values. You need to be sure that your reporting systems can do this.

Finally, the business indicators may also be highly cyclical. For example, retail stores and restaurants cycle within a weekly period. Sales tend to be consistently higher on some days than on others. This cycle has an effect similar to the seasons: the underlying trend is not apparent in the raw data.

In net, to spot the real trends in your business indicators, you need to ensure that your systems are removing the effects of irregularity, seasonality, and cyclicality in your raw data.