The science behind the new FBA capacity management system

The new Fulfillment by Amazon system empowers sellers to have more transparency and control over their capacity within Amazon’s fullfilment network by applying market-based principles.

It’s September. You’re a toy seller using Fulfillment by Amazon (FBA) and most of your sales come right before the holidays. However, this summer you overstocked, which degraded the health of your inventory turns, resulting in lower remaining capacity as you near the holiday season. Meanwhile, you just launched a new line of robot assembly kits that are becoming popular with customers.

How can you ensure access to additional space in Amazon fulfillment centers to set yourself up for a great Black Friday and Cyber Monday using FBA? And how does Amazon manage capacity without overbuilding space, potentially incurring costs that do not benefit customers, as well as without underbuilding space and limiting product selection, availability, and fast delivery?

The journey of a package
Brands and resellers seeking to leverage Amazon's science- and technology-driven fulfillment and transportation networks are turning to Fulfillment by Amazon (FBA) to outsource order fulfillment and customer service to Amazon.

The answer lies in an innovative new FBA Capacity management system, driven by Amazon’s Supply Chain Optimization Technologies (SCOT) and Selling Partners Services (SPS) teams. The new system provides most sellers with greater capacity limits, and also greater control to obtain additional capacity when they need it.

FBA is an optional service for sellers to outsource order fulfillment and customer service to Amazon. Since its launch in 2006, FBA has expanded rapidly, with selling partners ranging from family-owned snack makers to multinational high-tech companies.

FBA is an increasingly popular choice for both brands and resellers seeking to leverage Amazon's science- and technology-driven fulfillment and transportation networks. FBA provides sellers with significant cost savings and faster delivery speeds that together provide a great value for customers and sellers. The accelerated delivery speed has led to significant increases in customer demand for products, while the surge in demand, particularly during peak events such as Prime Day, Black Friday, Cyber Monday, and holidays, has created unprecedented demand for FBA capacity.

Amazon needed to find a way to match demand with available capacity while building additional capacity to meet sellers’ needs.

The capacity management challenge

Amazon’s forecasting capabilities — powered by a combination of machine learning, simulation, and optimization modeling — are well documented. Scientists have tackled research questions such as how to manage inventory for peak events and how to best distribute products across the Amazon fulfillment network for the fastest delivery.

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

But when it comes to managing capacity in the FBA program, even Amazon’s predictive mechanisms have limitations. FBA serves a wide breadth of independent sellers who own their inventory and make independent decisions about which products to carry, how much inventory to stock, and what prices to offer.

“There's only so much we can really understand about what sellers’ business plans are,” said Garrett van Ryzin, distinguished scientist at SCOT. “It’s fundamentally limiting to rely solely on our own predictions.”

Additionally, Amazon does not have full visibility into those sellers’ business needs or decision-making processes. This became more relevant as seller usage of FBA network capacity grew significantly within the span of a few years.

“Most of the time our predictive models allocate sufficient capacity to each seller. Yet we observed, in some situations, sellers had insufficient capacity, which impacted their ability to pursue certain growth opportunities. So we set out to design a release valve so sellers can tell us when they need more capacity for products they are confident customers will love, even if we were not predicting that they needed more space based on the data that Amazon had,” said Seamus Browne, a principal product manager on the FBA team.

To manage capacity for FBA, Amazon originally created a system that scores sellers according to how well they manage their inventory. A few weeks in advance of each allocation period, sellers are told how much capacity they will have for the next three months — a number that is based on sellers’ projected sales, and previous inventory performance (everything else equal, this number increases for sellers who use allocated space efficiently).

Our initial system worked well for managing capacity. However, we needed a new approach that could handle sellers’ current and future plans, such as a new product introduction, marketing and advertising campaigns, or other information that only sellers knew about.
Özalp Özer

“Our initial system worked well for managing capacity. However, we needed a new approach that could handle sellers’ current and future plans, such as a new product introduction, marketing and advertising campaigns, or other information that only sellers knew about,” said Özalp Özer, who is leading the FBA Science team in his capacity as director of research science.

Allocating space based on past performance and Amazon’s forecasts meant sellers who had recently underperformed with respect to sales and inventory efficiency might end up having lower leftover capacity to launch potentially popular new products or campaigns. Sellers could submit exception requests for additional capacity; Amazon was getting many such requests each quarter, and each one had to be reviewed manually.

To address that customer pain point, the Amazon FBA team created a new way for sellers to provide a simple, objective measure of how much value they could create if given additional capacity. The new tool applies economic principles to empower sellers to communicate credible needs for additional capacity.

Related content
Why multimodal identification is a crucial step in automating item identification at Amazon scale.

Here is how it works: during each capacity allocation period, sellers can place requests to increase their capacity limit. Those sellers submit a “reservation fee” — the maximum fee they would be willing to pay per cubic foot to reserve additional capacity, along with their desired maximum amount of additional capacity.

Requests are granted objectively, starting with the highest reservation fee per cubic foot until all capacity available under this program has been allocated. When additional capacity is granted, sellers pay no more than their chosen maximum reservation fee. In addition, sellers receive performance credits from the sales they generate using the extra capacity — up to 100% of the reservation fee.

A different approach to capacity management

The FBA team designed the tool to optimize the value for customers who use additional capacity effectively — and not to maximize proceeds for Amazon. In fact, sellers who win more space can earn credits from the extra sales they generate, and these credits can offset the entire amount of the reservation fee.

“The mechanism is designed to reward sellers who use their additional capacity productively to generate sales,” van Ryzin explained. “We are applying economic market design principles to manage supply chain capacity, and it's the first time we've really done it at this scale within FBA.”

This new tool is inspired by earlier economic research on selling securities, where payments are based on the value generated from the asset after it is allocated, but the details posed unique challenges.

“You also need something that is simple for sellers and holds a level of fairness in its decisions,” explained Tolga Seyhan, a principal research scientist on the FBA team.

“We make sure that if you come early and you paid a high fee, you will ultimately get the same fee as someone who paid less in a later round,” said Alexandre Belloni, an Amazon Scholar from Duke University’s Fuqua School of Business who has been leading market-design-related projects. “We want to reward early requests, which helps with our capacity planning and allows sellers to better utilize the additional capacity.”

Related content
How Customer Order and Network Density OptimizeR (CONDOR) has led to improved delivery routes.

Take a theoretical example: Say a seller has a Halloween shop on Amazon and needs space for a new costume. They want 5,000 additional cubic feet (“cubes”) on top of their usual allocation of 10,000 ft3. They place a reservation fee of $6 per cube in an early round of the quarter for a total potential cost of $30,000. Their request is granted, but then the lowest reservation fee granted in subsequent rounds drops to $4 per cube. The new tool reduces the Halloween shop’s reservation fee to $4 per cube, matching the lowest reservation fee granted in the later round. Hence, the seller’s total reservation fee would be a maximum of $20,000.

As is often the case with these capacity reservations, in this theoretical example, the seller correctly forecasts that their new costume would justify the space. That extra space generates $300,000 in incremental sales. Amazon awards a 15% performance credit on the incremental sales, up to a maximum of the $20,000 total commitment. In this case, the seller has earned performance credits of $45,000, which fully offset the entire reservation fee. The seller ends up paying nothing for the additional space, and because they used the space efficiently, their capacity allocations are also likely to increase in the future.

The system was successfully launched for a small number of sellers in early 2022.

Related content
The story of a decade-plus long journey toward a unified forecasting model.

“When we launched the first experiment, we were excited to see that sellers were using this rationally and that it was helping address information asymmetry. Sellers had enough information to confidently forecast both the need and price they’d be willing to pay for additional space. As a result, the vast majority of sellers got their reservation fee back and ended up paying nothing for the additional space,” Belloni said, adding that demand for the program has increased.

“The productivity of the space allocated through this mechanism is higher than what we would get by doing the allocation ourselves, and that benefits everyone: Amazon, our sellers, and our customers,” Özer said. “Building this new system required our scientists to use state-of-the-art tools from optimization, simulation, and economics and work closely with product managers and engineers. Together we innovate on behalf of our customers and sellers around the world.”

Related content

US, WA, Bellevue
We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals.
IN, KA, Bengaluru
Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
GB, London
We are looking for a Senior Economist to work on exciting and challenging business problems related to Amazon Retail’s worldwide product assortment. You will build innovative solutions based on econometrics, machine learning, and experimentation. You will be part of a interdisciplinary team of economists, product managers, engineers, and scientists, and your work will influence finance and business decisions affecting Amazon’s vast product assortment globally. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities * Work on a challenging problem that has the potential to significantly impact Amazon’s business position * Develop econometric models and experiments to measure the customer and financial impact of Amazon’s product assortment * Collaborate with other scientists at Amazon to deliver measurable progress and change * Influence business leaders based on empirical findings
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
EG, Cairo
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK). Please note these are not remote internships.
US, CA, San Diego
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their macroeconomics and forecasting skillsets to solve real world problems. The intern will work in the area of forecasting, developing models to improve the success of new product launches in Private Brands. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis About the team The Amazon Private Brands Intelligence team applies Machine Learning, Statistics and Econometrics/economics to solve high-impact business problems, develop prototypes for Amazon-scale science solutions, and optimize key business functions of Amazon Private Brands and other Amazon orgs. We are an interdisciplinary team, using science and technology and leveraging the strengths of engineers and scientists to build solutions for some of the toughest business problems at Amazon, covering areas such as pricing, discovery, negotiation, forecasting, supply chain and product selection/development.