AI-Powered Dynamic Pricing for Near-Expiry Food: When the Shelf Decides Before the Bin Does

Near-expiry food is where retail loses money quietly. Not in one dramatic moment, but in hundreds of small ones: a pack missed on a late shift, a markdown done too deep because nobody had time to think, a price mismatch that makes staff avoid discounting until it is almost too late. The retailers getting serious about waste reduction are moving that decision upstream. They let AI recommend discount steps based on remaining shelf life and store-level sell-through, then push those prices instantly to electronic shelf labels so the shelf and the checkout stay aligned. The result is not “prices changing all the time.” It is a tighter, earlier, calmer markdown engine that clears product before it expires.

The old markdown routine is built for good intentions, not good outcomes

Most stores still treat near-expiry discounting like an errand. Someone walks the aisles, checks dates, prints stickers, applies them, and hopes customers notice. This sounds manageable until you picture a busy store on a Friday afternoon. People are picking online orders, replenishing shelves, handling returns. Markdowns compete with everything else. That is why they drift later in the day, why some products never get touched, and why the discounts often end up blunt. When a team finally gets to the short-dated items, the safest move is a big cut. Big cut means fast sell-through, but also means margin that did not need to be sacrificed.

There is another silent problem: price integrity. If the sticker price does not match the POS price, the checkout becomes a negotiation. Over time, stores learn the wrong lesson: “better to avoid markdowns than create conflict.” Waste rises. Nobody celebrates it, but it happens.

What changes with AI plus electronic shelf labels

AI-based markdown tools split the problem into two parts: decision and execution. The decision is “how much discount, and when.” The execution is “get that price onto the shelf and into the POS, fast, with minimal errors.” Electronic shelf labels (ESLs) make the execution part real. Prices can be updated centrally in seconds, across thousands of SKUs, without printing, sticking, and re-checking labels.

Once that plumbing exists, the AI piece has room to matter. Instead of a single markdown moment, the system can run a discount path. In plain language: start smaller when there is still time, then step deeper only if the product does not move. This is the opposite of the panic markdown. It is closer to inventory control than to promotional pricing.

How the AI decides the markdown, without turning the store into a casino

In this use case, “dynamic pricing” is usually expiry-led markdown optimization, not demand-led price surging. The models work with a few practical signals that retailers already understand: remaining shelf life, store-level sales speed, on-hand inventory, time-of-day patterns, and category behavior. Some systems also learn from how different stores respond to different markdown levels over time, because a store in a commuter area behaves differently than one near a university.

The best implementations do not chase cleverness. They chase consistency. They apply guardrails: maximum discount, minimum margin, and limits on how many markdown steps are allowed. That keeps the program predictable for operations teams and less emotionally charged for shoppers.

Academic work on expiration-date discounting points out a key advantage of digital price tags: the ability to apply different discount rates to items with different expiration dates, without the sticker chaos. That is a small sentence with big operational impact, because it moves markdowns from “a label stuck on a pack” to “a controlled policy the store can run at scale.”

Where retailers are already doing this, and what it looks like on the floor

Albert Heijn and dynamic discounting as an anti-waste tool

Albert Heijn has been publicly linked to trials of AI-based “dynamic discounting” in perishables, with pricing automatically reduced based on sell-by date and with higher discounts for items that need to sell sooner. The point is not mysterious: the shelf nudges customers toward the item that is closest to expiry, and the store avoids throwing it away. The interesting part is that this model can update during the day as the clock runs down, which is hard to do with manual stickers unless you want a full-time sticker department.

Pilots and reported numbers from dynamic pricing vendors

In coverage of Wasteless, reported pilot figures have been framed as meaningful reductions in food waste alongside revenue uplift from product that would otherwise be binned. One widely cited set of numbers in public write-ups talks about roughly one-third reductions in waste and a mid-single-digit revenue lift in pilot contexts. Treat these as “reported pilot outcomes,” not universal guarantees, but they explain why retailers keep testing the model: it is one of the few sustainability levers that can pay for itself.

Why ESLs matter even if the AI is good

Retail loves to buy brains and forget the hands. With markdowns, the hands are the hard part. ESLs remove the labor bottleneck and reduce price mismatch risk because the shelf price can be synchronized with the POS automatically. Vendors and retailers also emphasize the speed benefit: a change that used to take hours of manual label swapping can become a centralized update across the store.

There is also a customer-facing angle. Shoppers do not love tiny stickers. They do respond to clarity. When the shelf label clearly shows the discounted price, and the reason is obvious (expires soon), the discount feels fair. It feels like a deal, not a trick.

The trust question: shoppers hear “dynamic pricing” and assume the worst

ESLs have triggered public anxiety in several markets because people imagine surge pricing: prices rising when demand spikes. This fear is loud, and retailers know it. What is useful for your category is that recent research-based reporting has found little evidence of demand-based surge pricing after ESL adoption in at least one studied grocery context. That does not end the debate, but it gives retailers a factual backbone for a clearer message: “we are using the technology to reduce waste and improve price accuracy.”

Practically, retailers can also design the system to avoid backlash. Many keep price increases restricted to overnight updates and use the daytime window mainly for markdowns and corrections. That policy line is simple to communicate and harder to misinterpret.

What makes these programs succeed, and what makes them quietly fail

Failures usually happen for boring reasons. Expiry data is incomplete. Inventory accuracy is shaky. Shelf-to-label mapping is wrong. The model then discounts too late, or discounts the wrong thing, and the store loses confidence fast.

The retailers that scale tend to do the opposite of what hype suggests. They start narrow. One department. One cluster of stores. They measure sell-through of short-dated inventory, shrink reduction, and margin impact. Then they expand.

A good internal KPI set looks unglamorous: short-dated sell-through, waste value by category, net margin after markdowns, labor minutes spent on markdown work, and price integrity incidents. If those move in the right direction, you have a sustainability program that survives budget season.

The sustainability payoff that actually holds up

Food waste reduction is often pitched as an ethical goal, and it is. But in stores, it becomes real when it is operational. AI-driven markdowns plus ESL execution creates repeatable behavior: earlier discounts, fewer “all or nothing” cuts, fewer missed items, and fewer checkout disputes. That is what makes it scalable across a chain.

In the best-case scenario, the store stops treating expiry as an emergency and starts treating it as a controlled variable. The bin becomes the exception, not the default. And sustainability stops being a poster and becomes a process.