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Demand Planning Dilemma: How to Buy for an Uncertain Fall 2021 Season

Though fashion season is in full swing, this year’s Fall/Winter shows look different, with designers presenting their looks via predominantly digital shows and alternative content. The typical market scene this year has been disrupted due to Covid, but planning retail assortments and forecasting for the season ahead presents its own unique hurdle for merchants.

For the past year, life has been upended by coronavirus lockdowns. Consumers’ fashion taste has followed suit as they traded in street clothes and career wear for comfortable loungewear and activewear. While casualization had been growing in fashion pre-pandemic, the current moment has accelerated the adoption of relaxed attire.

Per data from NPD, overall apparel sales declined by 19 percent in 2020, but at the same time sales of sweatpants climbed 17 percent and sleepwear was up 6 percent. Footwear sales fared similarly with a category-wide decrease of 27 percent over the year, while slippers and clogs respectively rose by 21 and 33 percent. Illustrating this trend, Crocs achieved record revenues for the year that were up 12.6 percent over 2019.

While 2020 and early 2021 saw sweatpants reign supreme, apparel and footwear demand for the latter half of 2021 is proving more difficult to predict. The vaccine rollout hints at a possible return to the office and in-person events, but uncertainty remains regarding what life will look like in the months ahead.

“One of the key challenges the brands and retailers are facing is demand estimation,” said Ganesh Subramanian, founder and CEO of fashion forecasting and demand planning firm Stylumia. “With this fall and winter having demand contraction due to Covid, it is challenging to estimate the recovery for the future season. While the quantitative estimate is a challenge, also qualitative estimates of the kind of trends that would emerge post-recovery is also a non-trivial problem.”

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Even if lifestyles return to some degree of normalcy in the coming months, including resuming social activities and heading to the office, 70 percent of consumers told NPD that they plan to dress either equally or more comfortably than they did before Covid as they emerge from lockdowns. Only 54 percent of consumers say they miss getting dressed up, with this portion skewing younger. Around one-third said they don’t yearn for opportunities to wear dressier attire, while around 13 percent said they never dressed up before Covid.

Although the vaccine could make in-person office work more feasible, employees might continue to work from home at least part of the time. This creates a need for multifunctional attire. Working remotely during Covid gave rise to the “Zoom shirt” phenomenon, in which professionals wore loungewear and then changed into more career-appropriate clothing for video calls. This creates an opening for garments that straddle the dual needs of coziness and conference call readiness.

Against this backdrop, NPD Group’s apparel industry analyst Maria Rugolo sees an opportunity for hybrid apparel that blends comfort elements—such as elastic waistbands or stretchy materials—with more structured looks. This trend dates back pre-Covid, with women’s dress pants that had yoga pant-style properties and men’s pants that resembled sweats.

“We’ve been in a comfort movement…we’ve kind of been headed that way for a while now,” Rugolo said. “A lot of aspects of our lives have been leading us to seek out comfort in whatever way that may be, and that really is going to impact our future spend on wardrobe, how we’re going to invest in it.”

Judging from past recession recoveries, Wendy Choi, chief operating officer at predictive analytics company Chain of Demand, expects color trends to slant toward muted neutrals such as beige, white and black rather than statement hues. When buying, she sees the typical ratio of 70 to 80 percent neutral colorways to about 20 percent bolder options as one way for retailers to hedge their bets.

Even if retailers play it safer with their color palettes, differentiation remains important as styles converge. “The brands that stand out, make connections and aren’t everything to everyone and know who their audience is are the ones that are going to succeed in this space,” said Rugolo.

Data-driven decision making

The pandemic has made historical data less accurate for forecasting, since the situation looks vastly different now than it did in 2020 or even 2019. However, retailers can use performance data from last year, provided they take the impact of current events into account. Carlos Casado, vice president of growth at retail merchandising platform Nextail, suggests a flexible forecasting model that allows companies to eliminate “noise” caused by irregularities such as store closures, as well as differentiating between impacts that occurred on a seasonal or local basis.

Retailers also need to plan for possible changes in buying patterns. Casado noted that compared to pre-pandemic, shoppers could lean toward making larger purchases less frequently, with more returns.

Given the difficulties of using past data for planning, retailers can instead focus on the factors that they can assess now. According to Choi, this should go beyond internal data to look at outside aspects such as whether an anchor store in a particular mall is open. “I would take more of a data-driven approach to how you would be planning your assortment. I mean, a lot of merchants, it’s a combination of looking at data and using your unique touch and experience in that category,” she said. “I think that you just need to rely more heavily on looking at macro factors and external data sets, at least this time around.”

In addition to pulling in store closure data, retailers can also pay attention to social media sentiment to gauge consumers’ spending and popular trends. Other external factors could include weather, the stock market or upcoming stimulus checks.

“Good demand planning needs good data discipline,” said Subramanian. “From an algorithm perspective, traditional forecasting techniques are not helping the fashion business.” For fashion, a best practice is to be able to demand plan down to specific styles, colors or sizes. Since it can be difficult to verbalize trends, using forecasting that incorporates both words and images is beneficial.

To get a better pulse on item- and store-level demand, Casado advises taking a “bottom-up” approach to physical store data rather than looking “top-down” at data from clusters of stores. Bottom-up planning helped retailers adjust allocation as needed last year, but it also has applications outside of turbulent times.

“Since past store and size clusters are now distorted, oversimplifications in terms of buying and assortment planning are even riskier than before,” said Casado. “Thus, those retailers who are able to capture hyper-local demand at store level will be best positioned when making buying decisions, as well any of their other merchandising decisions.”

Getting down to granular data is also important since any recovery will likely vary greatly in different locations.

Per Subramanian, instead of looking at trends on a seasonal or batch level, retailers need to move toward continuously checking on trends to be able to change up assortments as demand fluctuates. With demand tougher to predict in advance, experts agree that pushing decision making as late as possible is prudent. “For assortments later this year, if the brand has the ability to respond, we would strongly recommend the brands to keep a higher proportion of the budget for a close-to-season response,” said Subramanian.

NPD data shows that about six in 10 shoppers buy fashion merchandise when they need it. “The consumer is really staying in the season that they’re in,” said Rugolo. “We need to rethink when things hit the floor.” Given the level of uncertainty that consumers are facing, this behavior is expected to continue. Subramanian suggests holding inventory in a centralized place and distributing merchandise when it is actually required. Another option is to purchase and stock raw materials and use delayed differentiation to be able to produce closer to demand.

To guard against potential changes in the retail environment, Choi suggests that retailers not put all their eggs in one basket. By buying wider and shallower, they can avoid ending up with huge overstocks if a style they committed to doesn’t sell well.

Aside from preventing overstocks, smaller assortments enable brands to speak to consumers’ sustainability demands. “Fashion retailers should be more agile with their overall assortment, perhaps by introducing products more frequently as opposed to traditional fixed collections, and find new ways to strengthen customer relationships and reconsider product offerings to prove their value with less volume,” said Casado. “This will allow them to tap into more purpose-driven customer spending, make a more sustainable and responsible use of inventory, and increase their own agility and reduce operating costs.”

Retailers want to avoid buying mistakes, but if they do end up with inventory they can’t move, they have some options. Artificial intelligence is powerful in helping companies model and predict what will sell ahead of the season, but it can also be beneficial at projecting performance at a SKU level once items are in stores. This can help merchants plan markdowns more effectively to clear out merchandise in a timely fashion. Casado estimates that this type of probabilistic analytics can help reduce order volumes by 20 percent while also lowering stockouts by 60 percent.

In addition to flash sales and discounting, Choi has heard of creative solutions to extra stock, such as companies shortening sleeves or pant legs to transition them to a different season.

Part of demand planning in the current environment is having a backup plan. “If things do go back to normal, it’s like you need to have a plan A, plan B, plan C, just kind of like how you did last year for back to school, for the holiday season as well,” said Choi.