The Over-Under of Inventory Buying: A Response


A recent survey of 200 “senior decision makers” in U.S. retail companies conducted by Coresight Research and Celect, found that 43% of respondents chose overbuying as a company challenge, and 36% chose underbuying. The survey went on to say that those respondents using “manual inventory management processes were more likely to say they were struggling” with issues related to over or under buying.

Now that this survey has actually quantified over and underbuying as a problem, what is the next step?  Given the vast number of solutions available at the push of a button for almost any problem facing us today, I find it inconceivable why so many retailers choose to struggle with an issue that is relatively simple to identify and affordable to remedy. Having invested the last forty years working for forecasting companies that offer automated inventory planning and open-to-buy models along with ongoing consulting by industry experts, there is no reason I can think of as to why a merchant would not consider outsourcing this function. What else needs to change for retailers to use the correct data to make more informed buying decisions?

Among common reasons (aka excuses) that I often hear for not utilizing an outside company for merchandise planning include ego, lack of accurate data, and cost.

A strong, healthy ego or belief in oneself is certainly one major cornerstone of every successful entrepreneur and clearly every independent retailer. However, once self-confidence becomes so over-inflated that it is detrimental to the organization, it is important that management recognize this and look for an alternative independent, outside perspective.

If a company lacks accurate data to develop and maintain a plan, either due to deficiencies in the point-of-sale system or lack of understanding of how to use the system (often the case), inventory mishaps are likely to occur. The easy remedy for this is to invest in a system that will actually provide the data you need after consulting with informed sources, then take the time to learn how to use it. The initial “cost” pales in comparison to the money being left on the table due to poor buying decisions resulting from inadequate data or no data at all.

Here’s a question to ponder. If you were struggling with cash flow issues due to past buying mistakes, shrinking margins as a result of too many markdowns, or out of balance inventories hindering the store’s upside volume potential, would you be willing to invest a reasonable amount of money to alleviate those issues? Let me share a typical example that I often encounter. I was approached by a store with a sales volume of just over a million dollars. The margins were fine and the operating expenses were in line with industry norms. The issue was cash flow. When the merchandising data was analyzed, the problem was obvious. There was so much old merchandise that the store was losing customers. Most of the operating cash was tied up in the inventory. This problem was caused by buying more merchandise than the store could sell profitably; the classic definition of overbuying. Overbuying is the result of either A) not having an adequate merchandise plan with which to make sound buying decisions by classification or B) not following the plan.

The store had an average inventory of approximately $800,000 @ retail, a large percentage being from past seasons. This worked out to be an inventory turnover of 1.25 times annually. Based on the mix of merchandise, it was determined that an initial turnover goal of 2.7 was achievable and a strategy was put into place to liquidate the old merchandise and begin buying the right amount in the correct classifications.  After the first year of working with this retailer, the inventory was reduced by approximately $430,000 @ retail or $215,000 @ cost.  The sales started rising as new customers began finding the store through word of mouth, and old customers returned. The margins did decrease temporarily due to clearance markdowns needed to move the old merchandise. However, the cash was so much better that the owner could hardly believe the transformation. The line of credit was paid off and the store’s buyers go to market armed with a solid plan knowing exactly how much to buy, when to land it and when to mark it down.

This example is typical of first year results. Most retailers will end up spending less on inventory, but wind up doing more business because the dollars are invested where they are needed most. On average a minimum three-time return-on-investment is typical for every dollar spent on merchandise planning services according to Marc Weiss, President of Management-One.

I see examples such as this all the time. Some stores choose to take advantage of the help they are offered and regain control of their stores and, by extension, their lives. Others unfortunately do not and almost always suffer the consequences. I often refer to the old definition of insanity which is repeating the same behavior time and time again in anticipation of a more favorable outcome.

Since roughly 60% of non-grocery items were sold at full price in 2018, resulting in an estimated $300 billion in lost revenues, it is easy to deduce that poor buying decisions can be expensive. The good news however, is that with the increased utilization of technology, “specifically better forecasting tools that leverage machine learning, that can help retailers know what to keep in stock and when.” As trite as it may sound, failing to plan IS planning to fail.  You decide!