Animation shows a map of the United States and each of the 8 individual regions that resulted from Amazon's regionalization effort
Amazon's regionalization plan, which resulted in the eight regions seen here, has already proven successful: The percentage of customer orders being fulfilled entirely from FCs within each region has jumped to 76% — and is expected to continue to climb.

Sizing down to scale up: How Amazon reworked its fulfillment network to meet customer demand

The pandemic turbo-charged retail growth — teams of scientists at Amazon forged a path forward to handle the scale.

In 2020, Amazon’s retail fulfillment network in the U.S. expanded at a rapid clip. What followed was a dramatic — and swift — operational pivot.

This is the story of how Amazon’s national network of U.S. fulfillment centers (FCs), intermediate sorting centers, “last mile” delivery hubs, and transportation fleet were successfully restructured into eight largely self-sufficient regional networks, while retaining national coverage. The transformation was dubbed “regionalization.”

Operations research at Amazon
How Amazon’s scientists developed a first-of-its-kind multi-echelon system for inventory buying and placement.

The COVID pandemic was a key factor in two ways. Due to lockdowns or otherwise, people were staying home and ordering more online than ever before.

“Our focus moved from trying to figure out how to make customer deliveries as fast as possible to trying to meet exceptional customer demand by pushing as much volume as we could through our network,” says Adam Baker, Amazon’s vice president of global transportation.

It was in late 2020 that a long-term planning science team led by research director Amitabh Sinha sent up a warning flare: the fast-growing network risked becoming overcomplicated and unwieldy.

“We projected our scenario out to three or four years and took this to Amazon’s leadership with an idea of how to do things differently,” he says. That idea contained the seed that would grow into regionalization.

Joining the dots

The crux of the issue was that Amazon was trying to connect too many physical dots. Its fulfillment infrastructure made sense when it had fewer FCs, because it meant customers across the U.S. could tap into Amazon’s full product range. And with fewer FCs, the trucks carrying the products across the country were fuller, so it was cost-effective.

As the number of FCs and other fulfillment buildings in the U.S. rose sharply, that approach started to look like it might not be the right long-term path. “We would fulfill customer orders from the FCs near them until we couldn’t anymore, and then — okay, it's coming from wherever we still have capacity,” says Russell Allgor, Amazon’s chief scientist for worldwide operations. That “wherever” was the problem.

Operations research at Amazon
The SCOT science team used lessons from the past — and improved existing tools — to contend with “a peak that lasted two years”.

It meant each of Amazon’s FCs was serving not only its locality, but also customer locations all over the U.S. To illustrate the problem, imagine you had to deliver 10,000 products nationwide, quickly, to 100 distant locations, from 10 FCs across the country. You could have each FC dispatch 100 trucks, each carrying 10 items, to each of the locations. That’s 1,000 long-haul trucks and a lot of rubber on the road — clearly an unsustainable idea on all fronts.

Now imagine that you could partition the 100 customer locations into 10 regions of 10 locations apiece, with each region served by a dedicated FC. In this scenario, each region’s FC can dispatch 10 trucks, each carrying 100 packages a piece. That would require just 100 trucks nationwide, driving much shorter distances. That’s faster for customers and more sustainable: a win-win situation. That’s regionalization in a nutshell, and by mid-2021, Amazon threw its full weight behind the idea.

Picking the number

For about a year, Sinha and his team used state-of-the-art network-optimization tools to model and simulate the many potential ways customer orders might flow through a regionalized system, and what effect different configurations would have on delivery speeds and transport costs. There was an enormous number of potential scenarios to explore.

Operations research at Amazon
How the Amazon Logistics Research Science team guides important decisions related to last-mile delivery.

“We were dealing with millions of variables and constraints, and a lot of uncertainty,” says Cristiana Lara, a senior research scientist who worked on estimating the financial impacts of the initiative. “That’s not surprising, because we were completely shifting the paradigm of how we fulfill customer orders.”

A critical early question was how many regions to form. The smaller the regions, the faster the customer deliveries, because Amazon’s inventory would be closer to customers. “In addition to speedy deliveries, the crucial thing was that each region must carry the breadth of selection that customers expect” says Sinha.

The ambitious aim? To have a high proportion of the tens of millions of products offered in the store available to customers within each region, with the rest shipped from further afield only when needed.

Amazon's regionalization map, with 8 regions overlaid over a map of the United States, is seen here
A critical piece of regionalization was using the insights to map out more efficient, shorter routes for orders. As soon as a customer clicks the "buy now" button, Amazon's Adaptive TRansportation OPtimization Service (ATROPS) assigns the optimal route for the purchased item.

With this goal in mind, the collaborators alighted on the number: eight regions. That was as high as they could go without sacrificing speed or requiring excessive inter-regional movement of inventory to meet customer orders, which would defeat the purpose of the exercise.

“We ran extensive, fine-grained analysis for pretty much the entirety of 2022, examining in turn the different aspects of how it would all work,” says Sinha, whose team worked closely with Amazon’s Global Transportation Service (GTS) – which designs, plans, and executes the Amazon Transportation network.

Before long, a timeline was put in place. Come January 18, 2023, the newly minted Northeast and Mid-Atlantic Amazon regions would pioneer this new fulfillment pattern, with the other six regions slated to follow thereafter.

Flipping the switch

A critical piece of regionalization was using the insights supplied to map out more efficient, shorter routes for orders. As soon as a customer clicks the "buy now" button, Amazon's Adaptive TRansportation OPtimization Service (ATROPS) assigns the optimal route for the purchased item. The transportation team devoted the latter part of 2022 to overhauling and testing a completely new set of ATROPS routes designed specifically for this regionalization plan.

Operations research at Amazon
INFORMS talk explores techniques Amazon’s Supply Chain Optimization Technologies organization is testing to fulfill customer orders more efficiently.

On January 18, with the 2022 holiday rush safely in the rearview mirror, it was time to make the leap. The transportation team had contingency plans in place, and colleagues in different global time zones were standing by to offer around-the-clock support if something went wrong.

“We flipped the switch overnight, and immediately started to see the results we were hoping for. It changed faster than any of us expected. It was delightful,” says Nick McCabe, senior manager of GTS network design.

“We had some minor concerns to work through,” Baker adds, “but our delivery speed instantly picked up and our customers saw the benefit in their orders right away.”

Overall, the transition went so well, Amazon brought forward the complete activation of the other regions by a full month.

Rapid results

Regionalization is working. Before the switch, the percentage of customer orders being fulfilled entirely from FCs within what would become each region was 62%. That figure has already surged to 76% — a stunning efficiency gain — and is expected to continue to climb.

Delivery speeds have also picked up, says Sinha, because more goods are travelling shorter distances. And this effect will only strengthen as regionalization continues to take root.

Another quick success of regionalization was how much fuller a subset of Amazon’s trucks has become. Most customer orders leave an FC and are transported to a sorting center, which receives and consolidates customer orders, filling up the trucks that take them to delivery stations for the “last mile” of their journey. Sometimes, for logistical reasons, FCs send trucks directly to delivery stations. Post-regionalization, because there are fewer FCs shipping more packages to each destination, there is greater opportunity to operate these FC-to-delivery station direct trucks, resulting in more efficient delivery routes.

“Suddenly, 70 to 80 per cent of the order volume is not coming from FCs scattered around the country, but from, say, 10 FCs inside the region, so trucks on these short-distance direct lanes are now showing a great fill rate,” says senior applied scientist Semih Atakan, who models how products flow between Amazon’s FCs and delivery stations.

Regionalization has also transformed how the wider national network is managed.

“Before, it was difficult to control the whole network because of our sheer number of trucking lanes,” says Baker. “It was like pushing on a giant spiderweb.” Post-regionalization, he says, that number of lanes reduced markedly, making it much easier to make choices about when and how much to ship between regions.

Scanning the horizon

And this is just the beginning, says research scientist Xiaoyan Si, who is modeling how the fulfillment network might evolve over the next three years.

“Eight regions is our starting point. As we move forward, we will have the opportunity create smaller geographic regions with as much demand per region as we have today,” says Si. “Using the data we have now, we can place future fulfillment buildings more strategically, and we are working with other researchers on the team to design new regions more scientifically.”

Smaller regions will enable Amazon to deliver even faster to customers, while making each region even more efficient in terms of distance travelled, inventory management and truck fill.

Amazon’s Day One culture places great value on horizon scanning, innovation, and risk-taking to deliver customer benefits. The regionalization initiative that sprang from this mindset is a testament not only to the vision and enormous team effort required to pull it off, but also to the flexibility of Amazon’s infrastructure.

Because despite being Amazon’s biggest operational transformation in a decade, it was completely reversible had it misfired. After all, says Si, in what might be the understatement of the year: “When you boil it right down, regionalization is just a software setting.”

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Shape the Future of Cloud Computing Are you a graduate student passionate about Automated Reasoning and its real-world applications? Join our team of innovators and embark on a journey to revolutionize cloud computing through cutting-edge automated reasoning techniques.Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality. As an Applied Science Intern, you'll have the opportunity to work alongside our brilliant scientists and contribute to groundbreaking projects. From distributed proof search and SAT/SMT solvers to program analysis, synthesis, and verification, you'll tackle complex challenges at the intersection of theory and practice, driving innovation and delivering tangible value to our customers. This internship is not just about executing tasks – you'll explore novel approaches to solving intricate automated reasoning problems. You'll dive deep into cutting-edge research, leveraging your expertise to develop innovative solutions. You'll work on deploying your solutions into production, witnessing the real-world impact of your contributions. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment. Join us and be part of a team that is shaping the future of cloud computing through the power of Automated Reasoning. Apply now and unlock your potential! Amazon has positions available for Automated Reasoning Applied Science Internships in, but not limited to, Arlington, VA; Boston, MA; Cupertino, CA; Minneapolis, MN; New York, NY; Portland, OR; Santa Clara, CA; Seattle, WA; Bellevue, WA; Santa Clara, CA; Sunnyvale, CA. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment.
US, WA, Seattle
Unleash Your Potential as an AI Trailblazer At Amazon, we're on a mission to revolutionize the way people discover and access information. Our Applied Science team is at the forefront of this endeavor, pushing the boundaries of recommender systems and information retrieval. We're seeking brilliant minds to join us as interns and contribute to the development of cutting-edge AI solutions that will shape the future of personalized experiences. As an Applied Science Intern focused on Recommender Systems and Information Retrieval in Machine Learning, you'll have the opportunity to work alongside renowned scientists and engineers, tackling complex challenges in areas such as deep learning, natural language processing, and large-scale distributed systems. Your contributions will directly impact the products and services used by millions of Amazon customers worldwide. Imagine a role where you immerse yourself in groundbreaking research, exploring novel machine learning models for product recommendations, personalized search, and information retrieval tasks. You'll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data, fueling the next generation of AI applications. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction, Programming/Scripting Languages, Time Series, Machine Learning, Natural Language Processing, Deep Learning,Neural Networks/GNNs, Large Language Models, Data Structures and Algorithms, Graph Modeling, Collaborative Filtering, Learning to Rank, Recommender Systems In this role, you'll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets, from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management, proposing novel approaches that could further enhance Amazon's machine learning capabilities. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Design, implement, and experimentally evaluate new recommendation and search algorithms using large-scale datasets - Develop scalable data processing pipelines to ingest, clean, and featurize diverse data sources for model training - Conduct research into the latest advancements in recommender systems, information retrieval, and related machine learning domains - Collaborate with cross-functional teams to integrate your innovative solutions into production systems, impacting millions of Amazon customers worldwide - Communicate your findings through captivating presentations, technical documentation, and potential publications, sharing your knowledge with the global AI community
US, WA, Seattle
Do you have a strong science background and want to help build new technologies? Do you have a physics background and want to help build and test superconducting circuits? Would you love to help develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? Join the quantum revolution at Amazon and be part of a team that's pushing the boundaries of what's possible in quantum computing and quantum technologies. As a Research Science Intern focused on Quantum Technologies, you'll have the opportunity to work alongside leading experts in the field, contributing to cutting-edge research and driving innovation in areas such as quantum algorithms, quantum simulation, superconducting qubits, quantum key distribution, and quantum optics. We are looking for builders, innovators, and entrepreneurs who want to bring their ideas to reality and improve the lives of millions of customers. Research interns at Amazon work passionately to apply cutting-edge advances in technology to solve real-world problems. As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using modeling software and programming techniques for complex problems, implement prototypes and work with massive datasets. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Amazon has positions available for Operations Research Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA. Key job responsibilities We are particularly interested in candidates with the following skills: Quantum Algorithms, Quantum Simulators, Superconducting Qubits, Quantum Key Distribution , Optics In this role, you ain hands-on experience in applying cutting-edge analytical techniques to tackle complex business challenges at scale. If you are passionate about using data-driven insights to drive operational excellence, we encourage you to apply. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Conduct research and develop new quantum algorithms to solve complex computational problems - Design and implement quantum simulation models to study the behavior of quantum systems - Investigate the properties and performance of superconducting qubits, a promising platform for building large-scale quantum computers - Explore the application of quantum key distribution protocols for secure communication and data encryption, ensuring the privacy and integrity of sensitive information - Explore the application of quantum optics principles to develop novel quantum sensing and communication technologies