Sick of stockouts? Here’s four strategies to prevent stockouts and save your sanity

Four proven inventory strategies for demand planners hell-bent on keeping shelves stocked, cash flowing, and stress levels low

You're knee-deep in next season's forecast when an urgent email comes in. Your bestselling denim line just sold out in three key stores—again. Meanwhile, that "guaranteed hit" from last month is eating up warehouse space. Markdowns ain’t gonna to fix this. Rebalancing will take time. But you need action, now.

Demand planners know the fashion industry is brutal when it comes to forecasting, often making high-stakes buying decisions months in advance with limited data.

But by combining fashion-specific AI with real-time data, our clients move beyond gut instincts and rigid spreadsheets to make smarter, faster decisions. And their stressed-out demand planners sleep better too.

Let’s break down four strategies that autone offers to fix this broken system.

The real cost of poor demand planning

The numbers paint a stark picture of the industry's challenges: 28.1%¹ of unmet demand due to stockouts results in lost sales. Meanwhile demand planners spend a significant chunk of their time on manual calculations² and spreadsheet management – which isn't effective in preventing stockouts. It's reactive, rather than proactive.

Yet it's common practice, even at the biggest fashion brands. Prior to autone, a luxury fashion label we work with faced similar challenges. Their stockouts averaged 20% and replenishment happened every three weeks.

If you want to know how they got that number down to 5% using autone, keep reading for our tried-and-tested methods.

1. Double-down on improving forecast accuracy and speed

Bad forecasts cost money. Slow forecasts cost money. Traditional forecasting models often miss the mark because they rely too much on historical sales—which doesn’t always apply when the new season comes around.

Here’s why forecasting in fashion feels impossible (but isn't!):

Short product lifecycles: You’re making predictions for products that might only be on shelves for a few months.
Long lead times: You often need to place orders six months before a product even launches, meaning you’re working with limited data.
Size curves are unpredictable: A best-selling size in one city might barely move in another.
Visual similarity impacts demand: If two styles look alike, one might cannibalize sales from the other.
Trends shift fast: Weather, social media, and celebrity endorsements can all send demand soaring—or crashing.

AI tools like autone take a different approach. We analyze:

Product attributes to predict performance.
Visual similarities to understand how styles interact with each other.
Regional demand patterns to distribute stock where it’s needed.
Real-time sales trends so you can adjust forecasts on the fly.

2. Automate SKU-level adjustments

Managing thousands of SKUs manually isn’t just time-consuming—it’s impossible to do well.

You’re potentially dealing with millions of potential combinations. There’s no way a human can keep up.

That’s why smart retailers are automating SKU-level adjustments. Not to replace humans, but to give them time back for more meaningful work.

Here’s how it's done:

Continuous monitoring: The system tracks every SKU across all channels, flagging potential issues before they escalate.
Automated adjustments: Forecasts update automatically as new sales data rolls in, adjusting size curves and seasonal transitions without manual input.
Smart alerts: Instead of overwhelming planners with data, AI prioritizes what actually needs attention—so you can focus on high-impact decisions.

3. Fix store-to-store rebalancing

Moving stock between stores is one of the biggest headaches in fashion retail. By the time adjustments are made, the sales opportunity has passed, and stores are either left with too much of one size or completely out of another.

Instead of waiting for a full review cycle, autone constantly scans store-level demand and suggests real-time transfers to keep stock balanced.

There are three ways this approach works:

Fine-tuning: Adjusts stock levels across stores to prevent partial stockouts while keeping presentations intact.
Strategic consolidation: Moves slow-moving inventory to locations where it has a better chance of selling.
DC returns: Optimizes what should go back to the distribution center based on sales probability and transportation costs.

For our luxury fashion client this change paid itself back with a 10% revenue increase from better inventory placement. It also lowers transfer cost, directly impacting their bottom line.

4. Speed up replenishments

That same luxury brand's replenishment cycles used to take three weeks. In fashion, that's an eternity. By the time stock arrives, demand may have already shifted, and you’re either too late to fully leverage what demand there was or stuck with dead inventory.

They cut that down to a 66% faster replenishment to one day, with some stores receiving stock multiple times a week.

How? Our AI-driven replenishment model balances three critical factors:

Sales velocity (what’s selling fastest)
Size curve optimization (avoiding stock imbalances)
Cash flow impact (ensuring orders align with financial goals)

This allows for automated ordering, size-level performance tracking and an effortless integration for events and promos.

Smarter demand planning = Fewer fire drills

The future of demand planning isn’t about replacing human expertise—it’s about eliminating the manual work that slows you down.

📅 Book a demo to see how autone could work for your business.

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