Can Data Solve Retail’s Goldilocks Problem?

As the retail industry embraces a more data-driven and collaborative approach to inventory forecasting, there’s still much work to be done in this area.

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Overstocks and out-of-stocks are both long-standing problems in the retail industry, where empty store shelves drive customers to look elsewhere and burgeoning stockrooms are expensive. And with a small percentage of products falling into the “fast-selling” category, and the rest often left up to the whims of consumers, getting inventory management right in retail hasn’t gotten any easier over the last decade.

Consider this: Research conducted in the early 1990s revealed that about 8.3% of retail merchandise was out of stock at any given time. A similar study done a few years ago pegged the out-of-stock rate at about 8.1%. That’s a pretty significant number, and one that obviously hasn’t changed much over the last three decades despite the proliferation of retail inventory management technology.

While the pandemic may have awakened retailers to the need for improvements in this area, in the end most are still dealing with the same percentage of out-of-stocks that they were 30-40 years ago. “When you think about it, it’s kind of sad that the 8% rate has hardly changed during that time,” Mike Doherty, partner with Demand Clarity in Ontario, Canada, and author of the book Flowcasting the Retail Supply Chain, says.

The shock of the pandemic raised awareness to the issue that inventory needs to be holistically planned and more carefully monitored, Doherty explains, but it also created a bullwhip effect, with many retailers ending up with too much stock just as the global pandemic wound down. Now, many retailers are back to where they were in 2019 when it comes to inventory management. “Things are getting more back to ‘normal’ now,” Doherty says. “Still, the industry won’t be much higher or lower than 8% out of stock at this point, in my opinion.”

Getting suppliers involved and in the loop

The fact that retail hasn’t chipped away at its stockout rate in the last 30-40 years may have something to do with the fact that organizations focus on forecasting what they “think” consumers are going to buy. Then they calculate their inventory needs based on those projections, which have become pretty opaque in a business world where one TikTok influencer’s interest in a product can send loyal followers to online or brick-and-mortar stores to clear the shelves of those products.

Along the way, consumers have also come to expect a fully omnichannel experience: buy a new dress online, return the ill-fitting garment at a local store, and then order the right size from their smartphones. Calculating inventory needs across these channels, which continue to proliferate, isn’t always easy or clear-cut. Doherty says one solution is to calculate inventory needs—versus always forecasting—and then share projected shipments/requirements with suppliers who, in turn, can support the sales that the retailer thinks are going to take place.

Technology and data both play key roles in this information-sharing process, which has caught on with some retailers but is still somewhat of an enigma for others. “I think the [retail industry] is slowly starting to understand that, but it’s taking a while to get there,” Doherty points out. Borrowing a phrase from supply chain planning expert Joseph Orlicky, he says the “never forecast what you can calculate” mantra applies well in the retail world.

For example, instead of suppliers forecasting for each major retailer, the retailers should be providing actionable data on projected and planned shipments to their suppliers. That data should detail what suppliers need shipped per item, in what quantities and where the goods need to be shipped to. “This would lessen the need for suppliers having to try to guess or forecast,” Doherty says. “If suppliers could supply better, it would help [drive] retailers’ in-stock rates up and out-of-stocks down.”

Keeping up with demand

Constantly changing consumer expectations have also complicated things for retailers who have to one, predict what those buyers want, and then two, have the goods on the physical or virtual shelves when the order arrives. Buying patterns are very different than they were just 10 years ago, Doherty says, and often involve consumers who want to buy online and have the orders delivered to their homes or offices quickly. The pandemic moved the needle somewhat in this area, he adds, but at its core, retail inventory management still has to be based on consumer demand.

Data can play a significant role in helping retailers tackle their inventory management issues and whittle down the 8% stock-out number that they’ve been dealing with for decades. Doherty says having clean point-of-sale (POS) data is a good starting point for retailers that want to do a better job in this area. The data should indicate periods of abnormality (e.g., sales increased on one product that’s now out-of-stock) and help retailers flag and address those issues before they become real problems.

“For a long time, retail inventory management was just about ‘do whatever you need to do to get the product out there,’” says Doherty, who reminds retailers that the cleaner their POS history is, the better the odds that they’ll be able to forecast the future. He also tells retailers to focus on simplicity when it comes to data, and to not get too overwhelmed by the “next best thing” on the technology front.

In other words, if the data isn’t going to make a material difference to your forecast going forward, then you may not want to waste your time worrying about it. “The smart retailers are looking at the variables that actually matter,” says Doherty. “The juice has to be worth the squeeze in terms of data, so the smart companies are looking at the variables that actually matter and [ignoring] the rest.”

Better forecasts and longer lead times aren’t the answers

In many cases, retailers will use a patchwork of point solutions to solve their immediate inventory challenges, often ending up with multiple, unintegrated systems. Not all of those systems are optimized to be able to handle supply chain volatility, and most of them wind up competing against one another instead of working holistically. This approach can get costly in a sector where the two biggest expenses are stockouts and overstocks—both of which are addressed with improved forecasts and longer lead times. These are both failed approaches, according to George Stalk, senior advisor with Boston Consulting Group, Inc.

“The longer the lead time, the more likely that the forecast will be wrong,” Stalk says. “The two most important things a retailer can do are to one, get real demand to every point in the supply chain, and two, drastically reduce lead times throughout the supply chain.” Making both happen traces back to the factory, where the focus needs to be on how quickly the goods can be made and how fast the shipments can be sent out to suppliers.

“The volatility of the system is very sensitive to time, and the longer the lead times in the system, the greater the impact of volatility,” Stalk explains. “In fact, the longer the lead times in the system, the more likely the system itself is going to produce volatility. We’ve seen this happening as companies experience a huge amount of self-generated volatility, which is the washout effect of the bullwhip.”

Steps to a solution

As he surveys the retail landscape, Doherty says some organizations are waking up to the fact that they need to do more of what he calls “flowcasting”—thinking beyond forecasting and bringing suppliers into the data loop early—knowing that major retail supply chain shifts are probably in order.

When suppliers are oriented to what’s happening at the consumer level, and when technology supports an integrated supply chain, things like stockouts and overstocks begin to decrease. The problem is that less than 10% of global retailers are taking this approach. “We’re just not there yet,” Doherty says.

Going forward, Stalk says retailers should put less emphasis on finding technology that solves their problems and start “manually fixing” their existing inventory management systems. “Once you manually fix it, it’s a whole lot easier to automate it,” he notes. Next, come up with ways to do things that your competitors aren’t doing on the inventory front, and then keep those moves close to the vest, knowing that dissecting a company’s inner inventory management strategies can be extremely difficult.

Take the women’s lingerie retailer that decided to use a hot hatching-esque (i.e., paying a premium to have your box loaded last, unloaded first and either flown or unit-trained directly to the end destination) approach to getting its fast-moving products stocked and restocked quickly. “That all looks expensive on the surface and probably adds a 7% premium, but compare that to the out-of-stock gross margin, which is about 90% on women’s lingerie, and the extra cost pales in comparison to the cost of an out-of-stock,” Stalk says.

Outside of these individual examples of companies using data, technology and intuition to buck the 8% out-of-stock/overstock retail trends, Stalk says for the most part these are issues that retailers will continue to struggle with. Some of the problems are rooted in the fact that organizations still view the supply as a “cost to be managed” versus a strategic business opportunity. “There’s a real chance here for retailers to use this crisis against their competitors,” Stalk says. “Now that’s actionable.”

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About the Author

Bridget McCrea, Contributing Editor
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Bridget McCrea is a Contributing Editor for Logistics Management based in Clearwater, Fla. She has covered the transportation and supply chain space since 1996 and has covered all aspects of the industry for Logistics Management and Supply Chain Management Review. She can be reached at [email protected], or on Twitter @BridgetMcCrea

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