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.

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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.

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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.”

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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.

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“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.”

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We are seeking a Senior Manager, Applied Science to lead the applied science charter for Amazon’s Last-Hundred-Yard automation initiative, developing the algorithms, models, and learning systems that enable safe, reliable, and scalable autonomous delivery from vehicle to customer doorstep. This role owns the scientific direction across perception, localization, prediction, planning, learning-based controls, human-robot interaction (HRI), and data-driven autonomy validation, operating in complex, unstructured real-world environments. The Senior Manager will build and lead a high-performing team of applied scientists, set the technical vision and research-to-production roadmap, and ensure tight integration between science, engineering, simulation, and operations. This leader is responsible for translating ambiguous real-world delivery problems into rigorous modeling approaches, measurable autonomy improvements, and production-ready solutions that scale across cities, terrains, weather conditions, and customer scenarios. Success in this role requires deep expertise in machine learning and robotics, strong people leadership, and the ability to balance long-term scientific innovation with near-term delivery milestones. The Senior Manager will play a critical role in defining how Amazon applies science to unlock autonomous last-mile delivery at scale, while maintaining the highest bars for safety, customer trust, and operational performance. Key job responsibilities Set and own the applied science vision and roadmap for last-hundred-yard automation, spanning perception, localization, prediction, planning, learning-based controls, and HRI. Build, lead, and develop a high-performing applied science organization, including hiring, mentoring, performance management, and technical bar-raising. Drive the end-to-end science lifecycle from problem formulation and data strategy to model development, evaluation, deployment, and iteration in production. Partner closely with autonomy engineering to translate scientific advances into scalable, production-ready autonomy behaviors. Define and own scientific success metrics (e.g., autonomy performance, safety indicators, scenario coverage, intervention reduction) and ensure measurable impact. Lead the development of learning-driven autonomy using real-world data, simulation, and offline/online evaluation frameworks. Establish principled approaches for generalization across environments, including weather, terrain, lighting, customer properties, and interaction scenarios. Drive alignment between real-world operations and simulation, ensuring tight feedback loops for data collection and model validation. Influence safety strategy and validation by defining scientific evidence required for autonomy readiness and scale. Represent applied science in executive reviews, articulating trade-offs, risks, and long-term innovation paths.
US, MA, N.reading
Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Enable unprecedented robustness and reliability, industry-ready - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities As an Applied Science Manager in the Foundations Model team, you will: - Build and lead a team of scientists and developers responsible for foundation model development - Define the right ‘FM recipe’ to reach industry ready solutions - Define the right strategy to ensure fast and efficient development, combining state of the art methods, research and engineering. - Lead Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments - Lead Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Lead Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Lead Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.