From Weight Loss to Returns Forecasting: A New Test for Retail Supply Chains

With the holiday shopping season getting closer, retailers are focused on one of the biggest planning challenges of the year: getting the right quantity of products to the right places, at the right time.

But while holiday buying may already be largely locked in, there is another wave coming that supply chain leaders need to plan for now: returns.

The week after Christmas is traditionally one of the busiest periods for retail returns. This often extends into a month-long phenomenon known in retail and logistics as "Returnuary”. Adding to this, the growing influence of GLP-1 weight-loss medications is creating another layer of complexity, particularly for apparel.

In our recent blog, Why The GLP-1 Effect Is Creating a Demand Planning Blind Spot, we explored how the rapid adoption of GLP-1 medications is creating a new consumer segment with distinctly different purchasing behaviors. That shift is becoming increasingly difficult for retail supply chains to ignore.

New Consumers, New Demand Patterns

The scale of GLP-1 adoption is significant, and the number of users will rapidly increase. Analyst projections show the GLP-1 market reaching $150 billion by 2030, driven by the easing of manufacturing constraints, new market entrants, increased acceptance, and the introduction of simpler-to-take and easier-to-produce oral products.

This isn't simply a healthcare story; it is a consumer behavior story. Studies show that users are spending more on clothing and fitness, with meaningful changes in clothing size and styles. Demand isn't simply moving up or down, but it’s moving between categories, sizes, channels, and product types. In apparel, the implications are particularly significant.

For decades, retailers have relied on historical sales distributions to establish size curves and determine how much Small, Medium, Large, XL and extended-size inventory to carry. But what happens when customers' sizes are changing faster than historical data can capture?

The Returns Problem Retailers Can't Ignore

The challenge becomes even more pronounced online, where customers cannot try clothing before purchasing. Reportedly, GLP-1 users are purchasing multiple sizes of the same item with the intention of returning what doesn't fit. This creates a very different inventory equation.

A retailer may forecast strong demand for a particular size based on historical sales, only to see that demand change rapidly. Meanwhile, customers may order several sizes, temporarily removing inventory from availability before returning some, or all of it.

This leads to potential excess stock in some sizes, shortages in others, higher fulfilment and reverse-logistics costs, and greater pressure on margins.

The issue is already making headlines. Some retailers are experiencing significant increases in clothing returns linked to GLP-1 users, including a 50% increase in returns reported by a retailer.

The question is: Are returns being treated as a predictable demand signal or as a post-sale operational problem? The answer isn't simply to start buying more smaller sizes.

Recent developments in fashion demonstrate the danger of reacting too aggressively to a single trend. H&M's decision to discontinue its 3XL and 4XL ranges has sparked debate about whether retailers are overcorrecting in response to changing demand, particularly as the broader plus-size market remains substantial.

This is the key planning challenge of the GLP-1 era. Retailers need to respond to changing demand without abandoning customers whose needs have not changed.

That requires a more dynamic approach to assortment and inventory planning. It’s important to recognize that demand can vary by geography, gender, demographic, product category, and customer segment.

Smarter Supply Chain Planning for What Comes Next

Retailers should consider three priorities.

1. Reevaluate Demand Drivers.

Historical sales remain valuable, but they can no longer be the only source of truth. External signals (including GLP-1 adoption, demographic trends, consumer sentiment, health and wellness trends, economic indicators and market intelligence) can provide earlier insight into where demand is heading.

2. Make Returns part of the Forecast.

Returns should not be an afterthought. Retailers need to understand where returns are likely to occur, when they will happen, and how returned inventory will affect future availability. Returns forecasting can turn an unpredictable cost into a more manageable planning variable.

3. Continuously Revisit and Optimize Inventory Strategies.

Safety stock, replenishment policies, size curves, and assortment allocations should be evaluated against changing demand signals. What worked last season may not be the right strategy next season.

This is where advanced supply chain planning software makes the difference. An advanced solution like the Atlas Planning Platform from John Galt Solutions enables retailers to bring together internal operational data with external market signals to create a more complete view of future demand. AI-driven demand planning incorporates factors such as consumer behavior, demographic trends, economic data and market intelligence, while returns forecasting helps teams anticipate the reverse flow of inventory.

Atlas also supports dynamic inventory planning strategies and scenario planning, allowing teams to test questions such as:

What happens if GLP-1 adoption accelerates?  

What if demand shifts more quickly between sizes?

What happens to inventory if return rates increase?

More than a temporary shift in consumer behavior, the GLP-1 boom represents a test of how quickly planning processes can recognize structural changes in demand and respond without overreacting.

As retailers head into the holiday season, and the inevitable returns that follow, the lesson is clear: the next planning challenge isn’t just about what consumers buy – it’s also about what no longer fits.

Let’s have a chat and show you how Atlas can help you identify demand patterns early to apply different policies, and combine this with advanced what-if analysis to run scenarios, sense changes earlier and continuously align inventory investments with rapidly changing market conditions.