In the News: How Amazon is Using Machine Learning to Eliminate 915,000 Tons of Packaging
The following article originally appeared on Venture Beat.
Amazon is the co-founder and was first signatory of The Climate Pledge, which calls on signatories to commit to achieving the goals of the Paris Agreement 10 years early — reaching net zero carbon by 2040. Since 2015, the company has reduced the weight of its outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.6 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon’s network is a dramatic reduction in carbon emissions.
To make this happen, the customer packaging experience team partnered with AWS to build a machine learning (ML) solution powered by Amazon SageMaker. The primary goal was to make more sustainable packaging decisions, while keeping the customer experience bar high.
“When we make packaging decisions, we think about the end-to-end supply chain, working backwards from the customer in terms of the waste they get on their doorstep, but also being cognizant of how our decisions in packaging impact delivery speed,” says Justine Mahler, Packaging Senior Manager at Amazon.
Whether the customer orders a water bottle or a grill, Justine’s team uses ML to make sure products come in packaging that delights customers, arrives undamaged, and contributes to reduce Amazon’s carbon footprint.
“Our goal is to minimize the amount of packaging customers have to dispose of, and to increase recyclability of our packaging,” Mahler says. “Carbon is the primary metric we hold ourselves accountable to when we think about sustainability for the customer — and our corporate responsibility is to be a leader in this space.”
The sustainable packaging challenge
Amazon sells hundreds of millions of different products and ships billions of items a year. To ship with minimal packaging at maximum speed and ensure the customer’s order arrives at their doorstep undamaged, the team must innovate at a large scale.
“This is a challenge that machine learning is uniquely able to solve,” says Matthew Bales, Research Science Manager at Amazon. “Instead of having someone inspect these products individually for their fragility or their volume, we use machine learning.”
The goal was to scale decision making across the hundreds of millions of products that are shipped — to not automatically default to boxes, but to identify items that can be packed in a padded paper mailer or in a paper bag instead. Mailers (padded paper envelopes) are more sustainable choices. They’re 75% lighter than a similarly sized box, and will conform around a product, taking up 40% less space than a box during shipping — which means a lot fewer trucks on the road.
The machine learning difference
In practice, this meant creating a machine learning algorithm built on terabytes of product data from product descriptions to customer feedback. Working closely with AWS professional services, these terabytes of data are cleaned, catalogued, and ready for mining. The ML algorithm then ingests that data to identify the best packaging with the least waste.
Some of the most impactful ML models identify products that don’t need any packaging at all — like diapers. Other models can look at a product category like toys and identify items where the condition of the original packaging is important to ensure those toys are shipped with the protection of an Amazon shipping box.
By 2020, ML tools changed the packaging mix significantly, reducing the use of boxes from 69% to 42%.
“It turns out that we know a lot of things about the items in our catalog, but for many items we don’t have detailed fragility information that is relevant for Amazon’s complex shipment process,” Bales says. “Before we built this model, we relied on general rules. For example, vinyl toys under $25 would go in a flexible mailer. However, it turns out that there are a lot of exceptions to those rules.”
The model allows them to dig into all the exceptions — like collectible action figures that require the extra protection of a box. It ensures every item is packaged in the correct size mailer or box, or no box at all, and all at scale.
Using Amazon SageMaker, the packaging teams can analyze hundreds of millions of products, billions of customer shipments, and multiple channels of customer feedback, providing actionable insights in real time.
SageMaker ended up being key for them, Bales says, in part because it offers full customizability. As the models got more complex, the team was able to move from built-in models to custom models. Amazon SageMaker made it possible to launch new models in just weeks, allowing them to continually invent new ways to eliminate waste. From ML models that predict what products might leak, to identifying products that can be shipped in a paper bag or can be folded into smaller packages — the possibilities are endless.
Looking to the future of sustainable packaging
As the packaging experience team monitors social media, they’ve seen that customers are noticing the change and offering positive feedback. And today, thanks to Amazon’s efforts, thousands of vendors are working alongside the company to improve their own packaging to make more sustainable choices.
The team’s customer obsession is driving them to see how far they can reduce wasteful packaging, to evaluate new items quicker, to design better packaging, and to meet their larger carbon goal.
“We’re now focused even more increasingly on carbon elimination to reach these goals,” says Mahler. “That’s going to require more machine learning, infrastructure investments, and breakthroughs in materials science. These efforts have certainly given us a head start.”