“Amazon’s 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 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 enormous 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 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 backward from the customer in terms of the waste they get on their doorstep, but we are also really cognizant of how our decisions in packaging impacts speed to fulfillment,’ says Justine Mahler, Senior Manager, Packaging at Amazon.
Whether it’s sending off a water bottle or a grill, her team’s objective is to use ML to deliver packaging that delights customers, arrives undamaged, and contributes to a reduction in Amazon’s carbon footprint.
‘We try to minimize the amount of packaging customers have to dispose of, and drive toward recyclability in our packaging as well,’ Mahler says. ‘Carbon is the primary metric that we hold ourselves accountable to when we think about sustainability for the customer – and our corporate responsibility to be a leader in that space.’
Amazon sells hundreds of millions of different products, and sends billions of shipments a year. To ship all with minimal packaging, maximum speed, and customer satisfaction, the team must innovate on a large scale.
‘This is a challenge that machine learning is uniquely able to solve,’ says Matthew Bales, a manager of research science at Amazon. ‘Instead of having someone inspect these products individually for things like fragility or how they would eventually ship, 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 instead identify items that can be packed in a mailer, polybag or even paper bag instead. Both mailers (padded paper envelopes) and polybags (the familiar plastic padded bags) 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.”