An overhead shot inside an Amazon fulfillment center shows hundreds of boxes on conveyor belts along with people monitoring the flow of those packages
Amazon's scale makes picking the right package for each product a challenge. Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale. These tools have helped Amazon reduce per-shipment packaging weight by 36% and eliminate more than a million tons of packaging.

How pioneering deep learning is reducing Amazon’s packaging waste

A combination of deep learning, natural language processing, and computer vision enables Amazon to hone in on the right amount of packaging for each product.

Finding the right amount of packaging to ship an item can be challenging — and at Amazon, an ever-changing catalog of hundreds of millions of products makes it an ongoing challenge. In addition, Amazon’s scale also means it is impossible to solve this challenge using manual inspection to choose packaging for each and every item. For the same reason, general packaging rules and run-of-the-mill logic just won’t cut it. What’s required is a cutting-edge-smart automated mechanism that can adapt on the fly to changing circumstances.

Prasanth Meiyappan, top right, an applied scientist, and Matthew Bales, a research science manager, authored "Reducing Amazon’s packaging waste using multimodal deep learning". Their position paper was one of the 10 most read research papers on Amazon Science in 2021.

Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale, and a pioneering combination of natural language processing and computer vision is enabling Amazon to hone in on using the right amount of packaging. These tools have helped Amazon drive change over the past six years, reducing per-shipment packaging weight by 36% and eliminating more than a million tons of packaging, equivalent to more than 2 billion shipping boxes.

“When I started at Amazon in 2017, we had a lot of physical testing of products going on, but not a scalable mechanism that could assess hundreds of millions of products to identify the optimal packaging type for each product,” says research science manager Matthew Bales. Bales, who is also a physicist, heads up machine learning within Amazon’s Customer Packaging Experience team.

“Statistical tests were the first piece, but they are essentially only useful when products have already been shipped in more than one package type. We wanted the capability to predict how a product would fare in a less-protective, lighter, and more sustainable package type. And once you're in that predictive space, you need machine learning,” Bales explains.

The power of customer feedback

To make a prediction about whether a given product could be safely shipped in a particular package type, Bales and his colleagues built a ML model based largely on the text-based data that customers find on the Amazon Store — the item name, description, price, package dimensions, and so on.

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The model was trained on millions of examples of products successfully delivered in various packaging types, and on examples of products that arrived damaged in given packaging types. Amazon has access to almost real-time feedback when a product is not sufficiently protected by its packaging, because customers report it via the Online Returns Center and other forms of feedback, including product reviews.

“Customer feedback is paramount,” says Bales. “It powers all of our statistical testing.”

The model learned that certain keywords were particularly important when making packaging decisions. For example, keywords that indicated that a padded mailer would not be the right packaging included “ceramic”, “grocery”, “mug” and “glass”. These products were better shipped in a box. Keywords that suggested mailers were the right choice included “multipack” and “bag.” Those indicated the product might already have some form of protective packaging.

“The portion of the model that's learning from the Amazon Store has learned really well what the product is, and about its dimensions,” says Bales.

Reducing Amazon’s packaging waste using multimodal deep learning

It’s an important step in the journey, but automatically learning what a product is represents only half the battle. Equally important is how the vendor packaged the product before sending it to a fulfillment center. For example, a ceramic mug may be packaged in clear plastic bag, or in a sturdy box.

To identify product packaging at scale, computer vision needed to be deployed. The ML team already knew that the product images on the Amazon Store weren’t helpful when selecting packaging. For example, a multipack of LED bulbs might be illustrated by a picture of a single, unpacked bulb, suggesting it is fragile, yet the multipack is, in fact, safely packaged by the vendor and doesn’t require additional packaging. It is best shipped in its own container.

Bales’s team addressed this challenge by using Amazon’s own image data. When products are delivered to fulfillment centers, many are sent via conveyor belt through special computer-vision tunnels equipped with cameras that capture images of the products from multiple angles. These tunnels are used for many things, including ascertaining product dimensions and spotting defects.

Prasanth Meiyappan, an Amazon applied scientist, expanded the training of the team’s ML model to include these standardized product images in addition to the text classifiers from the catalog — a multimodal approach.

Our model detects the packaging edges to determine shape, identifies a perforation, a bag around the product, or light shining through a glass bottle.
Prasanth Meiyappan

“Our model detects the packaging edges to determine shape, identifies a perforation, a bag around the product, or light shining through a glass bottle.” Meiyappan explains. But to some extent, how the model makes its judgement about what it detects in images is hard for a human to discern, because the product features identified and weighted by the model tend to be complex.

“The important thing,” Bales notes, “is that the packaging decisions generated by the model are empirically accurate.”

Incorporating both text-based and visual data improved the ML model’s performance by as much as 30%, compared with using text-based data alone. Bales and Meiyappan have produced a position paper describing their work.

“When the model is certain of the best package type for a given product, we allow it to auto-certify it for that pack type,” says Bales. “When the model is less certain, it flags a product and its packaging for testing by a human.” The technology is currently being applied to product lines across North America, Europe, and Japan — automatically reducing waste at a growing scale.

“It’s a triple win,” says Bales. “Reduced waste, increased customer satisfaction, and lower costs.”

Balancing act

To arrive at this triple win, though, the team also had to take on a thorny challenge encountered frequently in the ML domain: class imbalance. In a nutshell, the problem is this: if you want an ML model to learn effectively, you ideally provide it with as many examples of failures as successes, so it can learn to differentiate effectively between the two.

The data used to train the model had many millions of examples of product/package pairings, yet depending on the package type, as little as 1% of those examples were for packages that turned out to be unsuitable in some way for the product within.

The machine learning literature to do with packaging is pretty sparse. Not many people deal with the kind of datasets we are dealing with in the packaging domain.
Prasanth Meiyappan

“Prior to implementing ML, we’ve shipped some product in envelopes and mailers for some time,” says Bales. “So, we had loads of examples of things that were good in mailers, but didn't have a lot of examples of things that were bad in mailers. ML models have problems with this kind of overwhelming imbalance.”

“The machine learning literature to do with packaging is pretty sparse,” Meiyappan says. “Not many people deal with the kind of datasets we are dealing with in the packaging domain. How effective a technique is in dealing with dataset imbalance is both domain and dataset specific.”

Thus the team’s approach to the class imbalance problem was primarily experimental. And of the six approaches they applied — four data based, two algorithm based — the clear winner produced a marked improvement in model accuracy. That was a data-based approach called two-phase learning with random under sampling which focuses the model on the minority class in the first phase of training and then on all of the data in the second. “In our position paper we share that knowledge with the ML community,” says Bales, “so that anyone who encounters a similar problem might choose to try this approach for themselves, to see if it also works in their problem space.”

What’s next

The team said they are eager to expand the use of this tool by training the model to understand all Amazon’s customers languages while also incorporating the unique aspects of fulfilment in each country.

Read the Amazon Sustainability Report

Amazon is committed to building a sustainable business for customers and the planet. Learn more about Amazon's goals, strategies, and policies in the Amazon Sustainability Report.

While Amazon scientists continue to research other ways to utilize machine learning to eliminate waste, the company is also working to reduce packaging waste throughout the e-commerce supply chain. Amazon is, for example, increasingly incentivizing its vendors to create optimized e-commerce packaging for themselves that saves space and materials without compromising product protection.

The company’s Shipment Zero goal is to deliver 50% of shipments with net-zero carbon by 2030, which from a packaging perspective means shipping products without added Amazon packaging or in carbon-neutral packaging. This is part of the Amazon’s wider Climate Pledge — a commitment to reach net-zero carbon by 2040, a decade earlier than the 2050 emissions target of the Paris Agreement.

View from space of a connected network around planet Earth representing the Internet of Things.
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Job summaryAmazon Worldwide Advertising is one of Amazon's fastest growing and most profitable businesses. The Advertising Console Product and Technology team is a group of creative individuals whose vision is to make the Amazon Advertising Console (AAC) the most loved and used tool for all advertisers to market and grow their businesses, brands, and products to a global customer base. The AAC is a collection of federated applications that combine to form the face and brand of Amazon Advertising. ACPT owns the delivery of the software, processes, and tools that allow teams across Amazon to build, support and enhance applications and features that deliver a cohesive advertising experience to all advertisers worldwide. By using our development kit and reusable components, developers can rapidly build features that integrate seamlessly within the suite of advertiser products. We use survey research, data science, machine learning, experimentation, and predictive modeling to understand advertiser dynamics, drive platform optimization, support evidence-based decision making, and help to develop predictive, intelligent features. As the Applied Science Manager on this team, you will: Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity.Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects.Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization.Analyze historical data to identify trends and support optimal decision making.Apply statistical and machine learning knowledge to specific business problems and data.Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.Build decision-making models and propose effective solutions for the business problems you define.Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Job summaryAre you excited about joining a team of scientists building lasting solutions for Amazon customers from the ground up? Our team is using machine learning, and statistical methods to take Amazon’s unique customer obsession culture to another level by designing solutions that change customers behavior when it comes to product search, discovery, and purchase. In order to achieve this, we need scientists who will help us build advanced algorithms that deliver first-rate user experience during customers’ shopping journeys on Amazon, and subsequently make Amazon their default starting point for future shopping journeys. These algorithms will utilize advances in Natural Language Understanding, and Computer Vision to source and understand contents that customers trust, and furnish customers with these contents in a way that is precisely tailored to their individual needs at any stage of their shopping journey. Key job responsibilitiesWe are looking for an Applied Scientist to join our rapidly growing Seattle team. As an Applied Scientist, you are able to use a range of science methodologies in NLP/CV to solve challenging business problems when the solution is unclear. For example, you may lead the development of reinforcement learning models such as MAB to rank content to be shown to customers based on their queries. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as SageMaker, S3, and EC2 with a variety of skillsets in shallow and deep learning ML models, particularly in NLP and CV. You will bring knowledge in many of these domains along with your own specialties and skilset.Major responsibilities:Use statistical and machine learning techniques to create scalable and lasting systems.Analyze and understand large amounts of Amazon’s historical business data for Recommender/Matching algorithmsDesign, develop and evaluate highly innovative models for NLP.Work closely with teams of scientists and software engineers to drive real-time model implementations and new feature creations.Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation.Research and implement novel machine learning and statistical approaches, including NLP and Computer VisionA day in the lifeIn this role, you’ll be utilizing your NLP or CV skills, and creative and critical problem-solving skills to drive new projects from ideation to implementation. Your science expertise will be leveraged to research and deliver often novel solutions to existing problems, explore emerging problems spaces, and create or organize knowledge around them. About the teamOur team puts a high value on your work and personal life happiness. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of you. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to establish your own harmony between your work and personal life.