Learning to Reduce Waste

How Machine Learning Is Rebuilding Food Supply Chains Into Smarter, Cleaner Systems

The Food We Waste Isn’t “Just Food”

Admit it—we’ve all done it.

A bag of spinach that looked flawless at the store ends up slimy two days later. Strawberries that were “fine yesterday” suddenly grow fuzzy overnight. A yogurt slips past its best-before date and feels questionable, so it gets tossed.

It feels small in the moment. But collectively, it’s enormous.

Around the world, humans waste 1.3 billion tons of food every year—about one-third of all food produced. That’s while two billion people experience hunger or undernourishment. Even on average, we waste about 163 pounds of food per person per year. [1]

And the worst part? Food waste doesn’t just disappear when it leaves our homes.

Much of it ends up in landfills, where it decomposes and releases methane, a potent greenhouse gas. In the U.S., rotting food waste is responsible for nearly a quarter of methane emissions. But the environmental impact starts long before that—food production takes water, energy, land, fertilizer, labor, and transportation. When food is wasted, so are those resources. As one report puts it, food waste causes all of the environmental impacts of food production without any of the benefits of actually feeding people. [1]

At a global level, food waste is also a symptom of something bigger: a “take, make, dispose” economy that prioritizes consumption over regeneration. This linear model has brought growth, but it’s also created a hidden bill. In fact, researchers estimate that every $1 spent producing food leads to $2 in environmental, social, and economic costs. [2]

So if food waste is this massive, why does it still happen?

Because food systems are complex, and complexity is exactly where machine learning shines.

How AI Can Reduce Food Waste (Without “Just Recycling It”)

Artificial intelligence doesn’t just help businesses move faster. It helps them make more intelligent decisions before waste happens.

A significant report by the Ellen MacArthur Foundation and Google, supported by McKinsey research, found that AI can create value rather than extract it, and even support biological systems. The report identifies three areas where AI can make the most significant difference in the transition of food to a circular economy:

• sourcing regeneratively and locally where appropriate

• designing out avoidable food waste

• designing and marketing healthier food products [2]

In other words: AI doesn’t just reduce waste after it occurs—it helps prevent waste from being created in the first place.

1. On the Farm: Catching Waste at the Starting Line

Food waste can begin before food even enters a truck or warehouse.

AI helps farmers and suppliers reduce waste through:

• visual inspection tools (image-based quality control)

• AI-enabled tracking to prevent edible food from being discarded

• forecasting models that predict ordering needs and to avoid overproduction [2]

These systems help restaurants, retailers, and suppliers match what people actually need with what gets grown and shipped.

McKinsey estimates that this ability to “design out food waste” could unlock up to $127 billion per year in economic opportunity by 2030—not just through cost-cutting, but through building efficiency into the system. [2]

2. Manufacturing: The “Hidden Waste” No One Sees

Most people assume food waste happens in kitchens and grocery stores.

But manufacturing waste is one of the most significant hidden contributors.

WRAP estimates that only 2% of food waste comes from retail and 10% from hospitality—but 28% comes from farming and manufacturing combined. That means over a quarter of food waste occurs before food is even ready to eat. [3]

This is where next-generation AI becomes a game-changer.

Dini McGrath—who worked in the food industry and later co-founded Zest Solutions—describes how waste piles up through everything from equipment issues to forecasting errors. But she also points out something important: the problem isn’t a lack of data. It’s that the data is too massive and complex for humans to process on their own fully. [3]

A Real Example: Kit Kats on a Production Line

Imagine a Kit Kat being produced.

Ingredients are coming in, outputs are going out, and there are multiple production stages—melting chocolate, cutting wafers, shaping, weighing, and packaging. McGrath explains that food loss happens at every stage, and Zest’s AI can build a real-time picture using sensor data to detect where the waste streams are forming. [3]

Even better: if waste can’t be prevented, AI can help manufacturers decide what to do with it—redistribute, repurpose, or sell it into another production loop, rather than discarding it. [3]

3. “Waste” as a Resource: AI Can Help Choose What Happens Next

Even the decision after waste happens can be optimized.

Professor Nicholas Watson (University of Leeds) explains that transforming food waste into microbial protein through fermentation is complex: the composition changes, pH and temperature matter, organisms matter, and each experiment takes time and money.

AI speeds up this process by dramatically reducing the number of needed experiments—turning long trial-and-error cycles into a smaller set of smarter tests that leverage scientific literature and real lab data. [3]

This is how waste stops being “trash”… and starts being a usable ingredient for new food systems.

4. Processing: Designing Foods That Waste Less

AI doesn’t only optimize supply chains—it can also redesign products.

Companies are using AI tools to source more regeneratively grown ingredients and develop alternative foods with less resource intensity. McKinsey highlights examples like:

• egg-free mayonnaise alternatives

• plant-based foods that replace meat, fish, dairy, and egg products [2]

While food innovation is often marketed as “cool,” what matters more is what it prevents: overuse of land, water, and carbon-heavy agricultural inputs.

5. Retail: Dynamic Pricing That Saves Food Before It Expires

We’ve all seen it: that bright yellow discount sticker.

But markdown pricing is more complicated than it looks. Discount too early and stores lose money. The discount is too late, and the food is unsellable.

That’s precisely why a startup called Wasteless built AI for dynamic pricing—adjusting prices depending on how close a product is to expiration. Their logic is simple: it doesn’t make sense for cheese expiring in two days to cost the same as cheese expiring in seven. [1]

During a pilot with a Spanish retailer, Wasteless reported about a 32.7% reduction in waste, and they aim to improve even further over time. [1]

Less waste, more revenue, and less landfill methane. This is what a “win-win” solution actually looks like.

6. Forecasting + Distribution: The Quietest Way AI Reduces Waste

One of the most potent ways to reduce food waste is also the least visible:

don’t produce or ship extra food in the first place.

AI improves demand predictions by using more than historical sales data. It can incorporate:

• weather patterns

• customer preferences

• marketplace trends

• regional demand fluctuations [4]

Better forecasting leads to more innovative inventory management, better production planning, and fewer “just in case” orders that become expired inventory.

AI can also support route optimization and distribution efficiency by analyzing real-time trends to reduce transportation waste and fuel consumption. [4]

Not all innovation is flashy; sometimes it’s just making sure food arrives at the right place, at the right time, in the right amount.

What This Means for Students + Young Innovators

Food waste is one of the most human problems on Earth: it involves economics, hunger, climate change, labor, culture, and logistics all at once.

Which is precisely why it’s such an exciting space for student innovators.

If you’re interested in AI, sustainability, or entrepreneurship, this field is full of fundamental challenges to solve:

• building computer vision tools that classify food quality

• predicting demand using time-series forecasting

• designing optimization algorithms for routing and distribution

• creating circular systems that connect waste streams to new products

• developing ethical AI that supports communities, not just profit

Food waste reduction is no longer just about compost bins and awareness posters. With machine learning, it becomes a data problem, a systems problem, and an engineering opportunity, one that can feed more people while protecting the planet.

And in a world built on “take, make, dispose,” maybe the most powerful innovation is learning to waste less.

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Author Bio:

I’m Anika Bhat, a student at Moreau Catholic High School. I’m passionate about tackling real-world challenges through research and advocating for food security. With writing and innovation, I aim to inspire sustainable, equitable solutions for the future.