Holiday Returns: How AI Cuts Cost and Chaos in Q1

Holiday returns are on track to hit $160 billion in merchandise value this season. That number isn’t just a retail headline—it’s a capacity plan, a cash-flow problem, and a procurement stress test that lands squarely in Q1, when many teams are trying to reset budgets and supplier strategies.

The part most companies miss: returns aren’t a “customer service issue.” They’re a supply chain and procurement signal—a loud one. When return rates climb (B-Stock says they’ve more than doubled since 2019 and are expected to reach 17% this holiday season), your inventory, supplier performance, packaging choices, product content, and forecasting assumptions are all being graded at once.

If you’re in supply chain or procurement, here’s the practical question: How do you keep returns from eating 30% of item value, clogging warehouses, and forcing panic buys in Q1? My stance is simple: you won’t fix it with “a better policy” alone. You fix it with AI that connects demand signals, product attributes, and reverse logistics decisions into one operating loop.

The $160B returns wave is really a Q1 capacity and cash crisis

Returns peak when operational teams are least excited to handle them: right after the holidays. B-Stock reports it typically sees a 20%–30% increase in inventory sold across its B2B resale platform in Q1, and that the number of truckloads doubles from January to March. That’s the downstream reality of return volume.

Why returns break plans faster than late inbound freight

Inbound delays are painful, but they’re at least planned in your network model. Returns are messy:

• They arrive with uncertain condition (new, opened, damaged, missing parts)
• They require inspection and grading before disposition
• They rarely match the location where you need sellable inventory
• They compete for labor and space with forward fulfillment

Retailers also face a brutal economic math problem: processing a return can cost around 30% of the item’s original price, and it can be higher for low-cost items. At that point, “restock it” is often the wrong move.

The returns experience is a growth lever (or a growth killer)

Consumers are unforgiving here. B-Stock notes 71% of consumers are less likely to shop again after a poor returns experience, and 82% say free returns heavily influence where they shop.

That creates a tension every supply chain leader recognizes:

You can’t win with a clunky returns flow—but you also can’t afford to treat returns like free.

AI helps resolve that tension by reducing avoidable returns upstream and optimizing the unavoidable ones downstream.

Where returns actually come from (and why apparel is the warning light)

Return rates are expected to hit 19% for online purchases and approach 30% for online apparel. Apparel isn’t “worse” because shoppers are fickle—it’s worse because the supply chain is making customers guess.

The upstream causes are more predictable than teams admit

Across retail categories, the repeat drivers look familiar:

• Fit/size uncertainty (apparel, footwear)
• Product not as described (content quality, imagery, specs)
• Quality defects (supplier process capability, packaging damage)
• Late arrival (carrier performance, promise-date logic)
• Impulse purchases amplified by promotions

Procurement has a direct hand in at least three of those: supplier quality, packaging standards, and product data governance across vendors.

AI’s job isn’t to “predict returns.” It’s to prevent the preventable ones.

A useful definition I’ve seen work in practice:

Return prevention is demand planning plus product truth.

AI can correlate return outcomes with product attributes (materials, size chart structure, imagery types), supplier lots, shipping lanes, and even promotion mechanics. The output isn’t a dashboard. It’s an action list:

• Which SKUs should have tighter supplier inspection
• Which items need content fixes before the next promotion
• Which suppliers or DC lanes produce higher damage returns
• Which categories need a different promise-date rule

AI in reverse logistics: the fastest way to stop margin bleed

Most reverse logistics operations still run on blunt rules: return to DC, inspect, restock if possible, liquidate the rest. That’s exactly how you end up with warehouses clogged in January.

Start with an AI disposition decision—before the item moves

The single highest-leverage move is deciding disposition at the moment the return is initiated, not when it shows up.

An AI-driven disposition engine uses signals like:

• Item price, margin, and expected recovery value
• Return reason codes and customer history
• Distance to nearest restock point vs refurbishment point
• Probability of being resellable (based on category + reason + seasonality)
• Current and forecasted demand (will it sell fast if restocked?)

Then it routes the return to the right outcome:

1. Keep it (refund without return) when shipping + processing costs exceed recovery
2. Return-to-store / local restock when demand is local and immediate
3. Consolidate to a refurb/repair node for high-value items
4. Direct-to-liquidation when shelf life is short or grading risk is high

This is how AI reduces the “30% processing cost” problem: it avoids spending $12 to recover $8.

Why resale channels matter to procurement (not just logistics)

B-Stock notes that about 60% of resold merchandise is customer returns (vs. 40% excess/shelf pulls). That resale ecosystem has procurement implications:

• You need disposition partners contracted and capacity-reserved before Q1
• You need data-sharing clauses so liquidation outcomes feed back into supplier scorecards
• You need packaging and kitting standards that reduce “missing parts” grading failures

If procurement isn’t involved, reverse logistics becomes a cost center that no one can bend.

Better forecasting is the most underrated returns strategy

Returns are often treated as an after-the-fact operational headache. They’re also a forecasting blind spot.

Fix the planning equation: demand isn’t demand without expected returns

For many retailers, forward demand planning ignores the reality that 17%–30% of units may boomerang.

A more accurate view for planning and replenishment is:

• Gross demand (what customers order)
• minus expected returns (by SKU/channel/season)
• equals net demand (what you truly need to source and position)
• plus recoverable returns supply (what you can restock in time)

AI forecasting models can ingest years of order and return history, promotions, product attributes, and channel behavior. The output is a probabilistic forecast that includes return-adjusted demand.

What this changes for inventory and procurement decisions

Once you forecast returns like supply, your decisions get sharper:

• Lower safety stock where recoverable returns will replenish quickly
• Earlier supplier buy decisions for products with low recoverability
• Smarter post-holiday markdown planning based on expected return inflow
• Reduced emergency buys (and reduced spot-rate freight) in late Q1

I’m opinionated here: if your S&OP or IBP process doesn’t include return-adjusted forecasts, you’re planning a fantasy.

A practical Q4-to-Q1 playbook (what to do next week)

You don’t need a multi-year transformation to improve this season’s returns impact. You need a tight loop between operations, data, and procurement.

Step 1: Build a “returns truth table” by SKU and channel

Start with a simple set of fields:

• Return rate by SKU (store vs online)
• Time-to-return distribution (days after purchase)
• Top 3 return reasons
• Estimated processing cost and recovery value
• Restock success rate (what % returns re-enter primary stock)

This table becomes the training set for better decisions.

Step 2: Prioritize 20 SKUs that create 80% of pain

Most companies spread effort across thousands of SKUs. Don’t.

Pick the SKUs with the worst combination of:

• High volume
• High return rate
• High processing cost
• Low recovery

Then run targeted fixes: content updates, packaging changes, supplier inspections, or promise-date adjustments.

Step 3: Add AI-guided disposition rules (even if you start simple)

If you’re early on AI, start with guardrails:

• Refund-without-return thresholds by price and category
• Auto-route likely resellable items to nearest restock point
• Auto-route high-risk items to consolidation hubs for inspection

Over time, replace thresholds with model outputs.

Step 4: Make returns a supplier performance metric

Procurement teams track on-time delivery and defects, but returns often sit outside the scorecard.

Add supplier-facing metrics such as:

• Returns due to quality defects
• Damage-in-transit linked to packaging spec compliance
• “Not as described” returns linked to vendor-provided attributes

Then tie them to corrective actions and commercial terms.

“People also ask” answers your team will get (and should be ready for)

Is free returns policy the real cause of rising return rates?

Free returns amplify the behavior, but they don’t explain why certain SKUs, suppliers, or channels return more than others. The real drivers are fit, product truth, quality, and delivery reliability—all measurable.

Can AI really reduce returns costs meaningfully?

Yes—because AI changes decisions, not reports. The biggest savings come from (1) preventing avoidable returns upstream and (2) choosing the right disposition before you pay processing and transport costs.

What’s the first AI use case to implement?

An AI-assisted return disposition and routing engine is usually the fastest ROI because it directly reduces handling, shipping, and warehouse congestion in Q1.

Returns are a procurement signal—treat them like one

Holiday returns reaching $160B is the headline, but the operational reality is more specific: return rates around 17% overall, 19% online, and near 30% for online apparel, with processing costs around 30% of item value. That combination can wipe out margin while also degrading customer loyalty.

This post is part of our AI in Supply Chain & Procurement series because returns sit right at the intersection of planning, supplier management, and logistics execution. If you connect those dots with AI—return-adjusted forecasting, AI-guided disposition, and supplier scorecards built on return drivers—you don’t just “handle” returns. You reduce them, route them smarter, and recover value faster.

If you’re planning for Q1, the most useful question isn’t “How bad will returns be?” It’s: Which 10 decisions will we automate before the first wave hits the dock—and what data do we need to trust those decisions?