How do retailers use AI to reduce overstock and markdowns?

Excess Inventory as a Costly, Persistent Problem

Overstock — inventory that doesn’t sell through at full price — has long been one of retail’s most expensive recurring problems, typically resolved through markdowns that erode profit margin. Every deeply discounted clearance rack represents a forecasting or buying decision that didn’t quite match actual demand. AI has become a significant tool for tackling this problem at multiple points in the inventory lifecycle, from the initial ordering decision through to how markdowns are timed and sized once excess stock exists.

Rather than treating overstock as an inevitable cost of doing business, retailers increasingly use AI to attack the problem at its source while also managing it more efficiently after the fact.

Preventing Overstock Before It Happens

The first line of defense is more accurate demand forecasting at the time buying decisions are made. AI models that incorporate detailed historical sales, seasonality, and broader trend signals can help retailers order quantities closer to what will actually sell, rather than relying on rougher estimates that tend to build in excess as a safety margin. Because these models can also forecast at a granular, store-by-store level, retailers can allocate initial inventory more precisely, sending more stock to locations where a product is likely to perform strongly and less to locations where demand is expected to be weaker.

This upfront precision doesn’t eliminate overstock entirely, but it reduces how much excess inventory gets created in the first place, which is generally more valuable than trying to clear it after the fact.

Redistribution and Smarter Markdown Timing

When some excess inventory does occur despite good forecasting, AI-driven systems can identify opportunities to redistribute stock from underperforming locations to better-performing ones, effectively finding demand for the product elsewhere in the network rather than immediately resorting to a discount. When redistribution isn’t a full solution, markdown optimization models step in, calculating the smallest, best-timed discount likely to clear remaining inventory by a target date, such as the end of a season.

A common insight from this kind of modeling is that earlier, smaller markdowns often clear inventory with less total margin loss than waiting and applying a single, steep discount later — since a modest early discount can capture price-sensitive shoppers before the selling window narrows further.

Bottom Line

Retailers use AI to reduce overstock and markdowns by improving upfront demand forecasting, redistributing slow-selling inventory between locations, and precisely timing and sizing markdowns to clear remaining stock with minimal margin loss. This combination addresses the problem both at its source and after the fact, though it can’t fully correct for a fundamentally mistaken initial buying decision.

Go deeper

• How Does AI Help Prevent Stockouts and Overstock Simultaneously?
• Can AI Inventory Forecasting Actually Prevent a Small Store From Overselling?
• How Does Walmart Use AI to Manage Its Supply Chain and Inventory?