Returns have rapidly shifted from a routine cost of business to a central factor in accurate retail planning. With return rates averaging 40% in apparel and footwear and returned merchandise totaling $890 billion in 2024, this is no longer an issue retailers can afford to ignore. At any given moment, millions of dollars in inventory are held by customers—blind spots that distort demand signals and inventory decisions. When returned items are processed through disconnected systems, they’re often invisible to planning. This leads to a chain reaction: overbuying to cover “missing” inventory, escalated markdowns to clear overstocks, and stockouts if returned stock isn’t visible.
Forward-thinking retailers understand that integrating returns data is essential for precise planning and stronger margins. By harnessing returns trends, seasonality, and predictive analytics, they’re turning an operational cost into a strategic asset.
The hidden impact of returns on inventory planning
Most retailers monitor returns rates, but few fully integrate these data points with planning and supply chain systems. This disconnect compounds over time, creating forecasting and replenishment errors, and has a significant impact on profitability.
Planning without returns visibility leads to costly overbuying
If planners don’t know how many returns are heading back into stock, they often over-order. For example, if systems show 100 units sold without accounting for the 30 units in return transit, planners are left chasing “ghost demand.” This results in a repeated pattern: excessive purchases, swelling inventory, and margin drain.
Retailers who introduce integrated returns visibility can cut inventory levels by up to 30% and improve promise accuracy. The breakthrough comes from treating every initiated return as inventory-in-waiting—factoring it into available-to-promise from the start.
Returns patterns drive better demand forecasting
Returns data contains critical signals about true customer demand—details that traditional sales data misses. High returns for certain sizes, colors, or products unmask sizing issues, quality gaps, or misaligned assortment strategies. Feeding these insights back into planning ensures smarter procurement, sharper buys, and reduced waste.
For instance, analysis might reveal that 40% of small tops are returned for sizing. Armed with this knowledge, buyers adjust future orders and suppliers address specification gaps—preventing recurring misses driven by historic sales alone.
Integrating returns into omnichannel planning
Returns should be treated as a strategic inventory stream, not just a reverse logistics headache. By connecting returns data across all systems and channels, retailers unlock smarter forecasting and faster, more precise replenishment.
Real-time returns visibility sharpens allocation
When returned products are tracked and visible in real-time, they’re immediately eligible for allocation. There’s no reason for a sellable item to languish unseen while customers elsewhere face stockouts. AI-powered routing can redirect returns to the highest-demand locations, skipping inefficiencies of standard hub processing.
Say a jacket is returned in Boston, but Philadelphia shows strong demand for that SKU. Smart routing sends the unit where it’s needed most—Philadelphia—speeding up sell-through and maximizing availability.
Returns forecasting informs buying
Returns, like sales, are seasonal and product dependent. Machine learning models trained on returns behavior—by product, event, or geography—allow buyers to adjust quantities and reduce overstocking well before it builds up. Predictive analytics can flag styles or SKUs that are likely to drive returns, letting planners act before inventory accumulates.
