Back in 2010, in an effort to reduce overhead and clean out low-performing products, Wal-Mart Stores Inc. moved to cut back on its SKU assortment. Publicly, the retailer said it wanted to satisfy consumers' appetite for major brands, while eliminating the "fatigue" they supposedly experienced by encountering a dizzying array of choices. (And whose fault was that?)

Within a year, Walmart was reversing course. Sales were down, and buyers were patronizing rival retailers when they couldn't find their favorite products on Walmart's shelves. Walmart ended up adding back 11 percent more products – around 8,500 items. So much for the latest experiment in the world of big-box merchandising.

What went wrong? Didn't mighty Walmart, with all of the data and analytical resources at its command, have the numbers to support its belief that fewer SKUs would make for happier customers? Didn't it know which low-selling products it could afford to dump?

Yes and no. The numbers were there – but numbers, considered in isolation from "softer" factors, can lie.

For years, chain retailers have struggled to assess the sales potential of their individual stores. They have long known that each location has its unique characteristics in terms of consumer profile and behavior. But sussing out the differences and commonalities among all those stores has proved to be devilishly difficult. So retailers have fallen back on averages and aggregations of data. In addition, they have tended to rely solely on the formula of sales per labor hour as means of assessing store performance and labor requirements. Hence the tendency to get things wrong.

What Walmart missed was the importance of seemingly unpopular items to individual consumers, says Jeff Primeau, senior manager of the supply chain practice at West Monroe Partners. Studies have shown that consumer loyalty to a particular product can trump the advantages that big-box stores otherwise offer. “A product might be seen as unprofitable,” says Primeau, “but that one item might be important to a consumer.”

Retailers need to understand the impact of single-store variables, when it comes to elements such as local demographics, product assortment and the hours that people shop. An “average” figure for the time of peak shopping activity, for example, won’t help an individual store manager to determine the right labor schedule for that location.

Primeau believes retailers are getting smarter. “Technology is coming to the rescue,” he says, “in the sense that business analysts now have some tools where they can make more predictive analyses in a much faster time. They can correlate a lot more data than they used to do just a few years back.”

Putting the brakes on that trend is the difficulty of finding skilled analysts who can read the numbers correctly, while taking into account factors that are more difficult to quantify. “The expertise in that field is still very sparse because [the technology] is so new,” says Primeau. “It’s hard for retailers to have that in-house.”

He says retailers are beginning to pay more attention to the needs of individual stores, rather than setting strict guidelines for the entire chain “and making everybody the same.” Among their biggest challenges is figuring out the right mix of product to ship to each location. One might require continuous replenishment of an item that sits on the shelf for months at another. Figuring out the perfect formula “is something that will never be solved,” Primeau says. “But it’s important to be flexible and understand the differences.”

In the age of Big Data, the information is becoming both more plentiful and accessible. A retailer looking to assess the impact of SKU rationalization can review millions of records in a matter of minutes, instead of five or six hours. But it has to be able to distinguish between meaningful data and “noise.”

Which is where pure technology, for all it offers to modern-day planners, falls short. Primeau says analysts must be “street smart” in their assessment of the numbers. They need to take into account the human factor – including the psychology of consumers and their ever-changing habits and desires – before locking in a demand or supply plan.

Retailers continue to have problems when addressing the finer points of labor planning, both in store and at the distribution center. New software applications offer an unprecedented level of sophistication in this area, generating productivity standards down to the minute, and individual pick or putaway.

Again, though, the human factor intrudes. Primeau says many decisions are still being made on a siloed basis. A planner at store level, for example, will be concerned with ensuring that product is always available. Frequently, though, the procurement decision fails to take into account the complexities involved in receiving and storing the item in question. The supplier might offer what looks like a great deal – say in the form of a 10-percent rebate if the buyer purchases six months of product. But what are the additional costs related to storage, if the fulfillment facility is already operating near capacity?

Bottom line, says Primeau: retailers need to understand the true cost to serve customers, based on decisions related to procurement, storage, distribution, and that final intangible, customer behavior.

Breaking down the silos isn’t easy. Even sales and operations planning (S&OP), which was supposed to achieve that goal, doesn’t automatically account for the needs of procurement and distribution, Primeau says. To remedy that oversight, individual departments must be able to share information freely, and understand one another’s priorities.

Believe it or not, additional measurements might be needed. If a company were to understand the costs that are incurred every time a product is touched or moved – which, by the way, is the means by which many third-party logistics providers make their money – “then we would have more informed decisions,” Primeau says.

But not perfect ones. Only by factoring in the intangibles – things that can’t be boiled down to a mathematical formula – will retailers achieve anything resembling the truth.

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