This picture is an overhead shot inside an Amazon center, workers can be seen moving amidst hundreds of boxes which sit on conveyor belts and carts, in the upper left foreground, a yellow railing extends into the distance.
When faced with the need to evolve Amazon’s supply chain to meet customer needs, a team of scientists, developers, and other professionals worked together to create an inventory planning system that would help Amazon fulfill its delivery promises.
F4D Studios

The evolution of Amazon’s inventory planning system

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

For every order placed on the Amazon Store, mathematical models developed by Amazon’s Supply Chain Optimization Technologies organization (SCOT) work behind the scenes to ensure that product inventories are best positioned to fulfill the order. 

Forecasting models developed by SCOT predict the demand for every product. Buying systems determine the right level of product to purchase from different suppliers, while large-scale placement systems determine the optimal location for products across the hundreds of facilities belonging to Amazon’s global fulfillment network.

“With hundreds of millions of products sold across multiple geographies, developing automated models to make inventory planning decisions at Amazon scale is one of the most challenging and rewarding parts of our work,” said Deepak Bhatia, vice president of Supply Chain Optimization Technologies at Amazon.

We made the decision to redesign Amazon’s supply chain systems from the ground up.
Deepak Bhatia

In the first half of the past decade, Amazon transitioned from a largely manual supply chain management system to an automated one. However, when faced with the need to evolve Amazon’s supply chain to meet customer needs, and the introduction of same day delivery services like Prime Now, the team moved to replace that system with a new one that would better help Amazon fulfill delivery promises made to customers.

“As far back as 2016, we were able to see that the automated system we had at the time wouldn’t help us meet the ever-growing expectations of our customers,” Bhatia recalled. “As a result, we made the decision to redesign Amazon’s supply chain systems from the ground up.”

A global company catering to local needs

“In 2016, Amazon’s supply chain network was designed for scenarios where inventory from any fulfillment center could be shipped to any customer to meet a two-day promise,” said Salal Humair, senior principal research scientist at Amazon who has been with the company for seven years.

This design was inadequate for the new world in which Amazon was operating; one shaped by what Humair calls the “globalization-localization imperative.” Amazon’s expansion included an increasing number of international locations — at the time, the company had 175 fulfillment centers serving customers in 185 countries around the world.

“Meeting the needs of our customer base meant that we needed to serve those customers in multiple geographies,” Humair said.

As Amazon continued to expand internationally, the company also launched one-day and same day delivery windows in local regions for services like Amazon Prime and Amazon Prime Now.

“We quickly realized that in addition to serving customers around the globe, we also had to pivot from functioning as a national network to a local one, where we could position inventory close to our customers,” Humair says.

A row of five profile photos shows, left to right, Deepak Bhatia, vice president of Supply Chain Optimization Technologies at Amazon; Salal Humair, senior principal research scientist; Alp Muharremoglu, a senior principal scientist; Jeff Maurer, a vice president; and Yan Xia, principal applied scientist.
Left to right, Deepak Bhatia, vice president of Supply Chain Optimization Technologies at Amazon; Salal Humair, senior principal research scientist; Alp Muharremoglu, a senior principal scientist; Jeff Maurer, a vice president in SCOT; and Yan Xia, principal applied scientist, were among those instrumental in migrating Amazon to the multi-echelon system.

In addition to the ‘globalization-localization imperative,’ the growing complexity of Amazon’s supply chain network further complicated matters. To meet the increased customer demand for a diverse variety of shipping speeds, Amazon’s fulfillment network was expanding to include an increasing number of building types and sizes: from fulfillment centers (for everyday products) and non-sortable fulfillment centers (for larger items), to smaller fulfillment centers catering to same-day orders, and distribution centers that supplied products to downstream fulfillment centers. The network was increasingly becoming layered, and fulfillment centers in one layer (or echelon) were acting as suppliers to other layers.

“We had to reimagine every aspect of our system to account for this increasing number of echelons,” Humair said.

The science behind multi-echelon inventory planning

The sheer scale of Amazons operations posed a significant challenge from a scientific perspective. Amazon Store orders are fulfilled through complex dynamic optimization processes — where a real-time order assignment system can choose to fulfill an order from the optimal fulfillment center that can meet the customer promise. This real-time order assignment makes inventory planning an incredibly complex problem to solve.

Other inventory-related dependencies further complicate matters: the same pool of inventory is frequently used to serve demand for orders with different shipping speeds. Consider a box of diapers: it can be used to fulfill an order for a two-day Prime delivery. It can also be used to ease the life of harried parents who have placed an order on Prime Now, and need diapers for their baby delivered in a two-hour window.

Amazon’s scientists also have to contend with a high degree of uncertainty. Customer demand for products cannot be perfectly predicted even with the most advanced machine learning models. In addition, lead times from vendors are subject to natural variation due to manufacturing capacity, transportation times, weather, etc., adding another layer of uncertainty.

This required building a custom solution, one that relies on sound scientific principles and rigor, and borrowing ideas from academic literature as building blocks, but with ground-breaking in-house invention.
Alp Muharremoglu

Humair notes that the scale of Amazon’s operations, the complexity of the network, and the uncertainties associated with the company’s dynamic ordering system make it impossible to even write down a closed-form objective function for the optimization problem the team was trying to solve.

While multi-echelon inventory optimization is a well-researched field, the bulk of literature focused on single-product models, proposed solutions for much simpler networks, or used greatly simplified assumptions for replenishing inventory.

“There is a large body of academic literature on multi-echelon inventory management, and papers typically focus on one or two main aspects of the problem,” noted Alp Muharremoglu, a senior principal scientist in SCOT who spent 15 years as a faculty member at Columbia University and the University of Texas at Dallas. “Amazon’s scale and complexity meant no existing solution was a perfect fit. This required building a custom solution, one that relies on sound scientific principles and rigor, and borrowing ideas from academic literature as building blocks, but with ground-breaking in-house invention to push the boundaries of academic research. It is a thrill to see multi-echelon inventory theory truly in action in such a large scale and dynamic supply chain.”

As a result, the system developed by SCOT (a project whose roots stretch back to 2016) is a significant break from the past. The heart of the model is a multi-product, multi-fulfillment center, capacity-constrained model for optimizing inventory levels for multiple delivery speeds, under a dynamic fulfillment policy. The framework then uses a Lagrangian-type decomposition framework to control and optimize inventory levels across Amazon’s network in near real-time.

Broadly speaking, decomposition is a mathematical technique that breaks a large, complex problem up into smaller and simpler ones. Each of these problems is then solved in parallel or sequentially. The Lagrangian method of decomposition factors complicated constraints into the solution, while providing a ‘cost’ for violating these constraints. This cost makes the problem easier to solve by providing an upper bound to the maximization problem, which is critical when planning for inventory levels at Amazon’s scale. 

“We computed opportunity costs for storage and flows at every fulfillment center,” Humair said. “Using Lagrangean decomposition, we then used these costs to calculate the related inventory positions at these locations. Crucially, we incorporated a stochastic dynamic fulfillment policy in a scalable optimization model, allowing Amazon to calculate inventory levels not at just one location, but at every layer in our fulfillment network.”

Mobilizing the organization

While creating the new multi-echelon system was an imposing scientific challenge, it also represented a significant organizational accomplishment, one that required collaboration across multiple teams.

“Moving multi-echelon from concept to implementation was one of the most difficult organizational challenges we’ve worked through; we had many potential implementations that looked radically different in terms of model capabilities, interfaces, engineering challenges, and long-term implications for how our teams would interact with each other,” said Jeff Maurer, a SCOT vice president who has been instrumental in rolling out the automation of Amazon’s supply chain and oversaw the roll out of the multi-echelon system.

“This was also a case where there wasn’t a great way to decide between them without building and exploring one or more approaches in production. Ultimately, that’s what we did — we picked the best options we could identify, built them out, learned from them, then repeated that process. We learned things by experimenting with real production implementations that we could never have learned from simplified models or simulations alone, given the complexity of the real-world dynamics of our supply chain. But it was hard on the teams — it wasn’t always obvious that the systems the teams were iterating on were the best path, given the high directional ambiguity.”

Packages moving through a fulfillment center

“Sometimes, the only way to make a massive change is to realize that you have no option but to make that change,” said Yan Xia, principal applied scientist at Amazon. Humair noted that Xia played “a pivotal role” over the four years it took the company to migrate to the new multi-echelon system.

Xia recalled that teams within SCOT were keenly aware of the limitations of the existing system.  However, there was skepticism that the multi-echelon system was the right solution.

“The skepticism was understandable,” Xia said. “It’s one thing to have a big idea. But you also have to be able to present the benefits of your idea in a coherent way.”

Xia gave an example of how he helped convince members from the buying and placement teams about the benefits of the new model.

“One team decides optimal suppliers to source products from, while another team makes decisions on where these products should be placed,” Xia explained. “I was able to show them how the two functions would essentially be unified in the multi-echelon system. Sure, it would change how they worked on a day-to-day basis — but it would do so in a way that made their lives simpler.”

To help ensure that resources were made available for the development of the multi-echelon system, Xia also focused on driving alignment among leaders in SCOT. He developed a simulation based on real-world data. The results clearly demonstrated that the proposed solution for inventory forecasting, buying, and placement would result in a steep decline in shipping costs, which in turn would allow Amazon to keep prices lower for customers.

Teams involved in multi-echelon planning discussions were galvanized after seeing the results of the simulation.

“Everyone bought into the vision,” Xia said. “We began to collaborate in near real-time. If we ran into a problem, we didn’t wait around for a weekly sprint meeting. We just got together in a room, or stood next to a whiteboard and solved it.”

Xia said that this was also when things began to get more complex. 

“An awareness of the complexity of the existing setup began to dawn on us,” says Xia. “We began to realize how every component in the system had multiple dependencies. For example, the buying platforms were tightly integrated with older legacy systems – we now had to factor these dependencies into our solutions.”

Solving a multi-item, multi-echelon with stochastic demand and lead-time and aggregated capacity constraints and differentiated customer service levels. That sort of thing is just unheard of in the academia and the industry.
Deepak Bhatia

The team iterated on the multi-echelon solution in a sequence of three in-production experiments (or labs) that spanned 2018 to 2020. The first lab incorporated components of the new system coupled with the old platform. It was a resounding success in terms of reducing costs, even while fulfilling orders associated with higher shipping speeds. The team moved on to testing the subsequent version of the multi-echelon system in the second lab. 

“That wasn’t nearly as good,” Xia recalled. “Most things didn’t work as expected.”

However, the team was encouraged by leadership to keep going. This wasn’t SCOT’s first attempt at taking on big and ambitious projects. The organization had taken three years to deploy the first automated supply chain management system where they overcame various challenges.

“Sure, the failure of the second lab was demotivating,” Xia says. “But we knew from experience that this failure was only to be expected. It was part of the process.”

The team fixed the bugs, and moved on to testing new features in the third lab. These included critical system capabilities, such the ability to model order cut-off times for deliveries within a particular time window.

The system went live in 2020, and over the past year, the multi-echelon system has had a large and statistically significant impact in positioning products closer to customers.

“On a personal level, I am incredibly proud of our team. Having worked in the area of multi-echelon inventory optimization before I joined Amazon, I have a deep appreciation of how difficult it was,” Bhatia noted. “There is a strong sense of pride for the work the team is doing — such as solving a multi-item, multi-echelon with stochastic demand and lead-time and aggregated capacity constraints and differentiated customer service levels. That sort of thing is just unheard of in academia and industry. This is why I find it gratifying to work as a scientist and a leader at Amazon. It gives me a lot of pride, and none of this could have been achieved without the people and the culture we have.”

Related content

US, WA, Seattle
Are you excited about building high-performance robotic systems that can perceive, learn, and act intelligently alongside humans? The Robotics AI team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.The Amazon Robotics team is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. Come join us!
US, VA, Arlington
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. As Director for PXT Central Science Technology, you will be responsible for leading multiple teams through rapidly evolving complex demands and define, develop, deliver and execute on our science roadmap and vision. You will provide thought leadership to scientists and engineers to invent and implement scalable machine learning recommendations and data driven algorithms supporting flexible UI frameworks. You will manage and be responsible for delivering some of our most strategic technical initiatives. You will design, develop and operate new, highly scalable software systems that support Amazon’s efforts to be Earth’s Best Employer and have a significant impact on Amazon’s commitment to our employees and communities where we both serve and employ 1.3 million Amazonians. As Director of Applied Science, you will be part of the larger technical leadership community at Amazon. This community forms the backbone of the company, plays a critical role in the broad business planning, works closely with senior executives to develop business targets and resource requirements, influences our long-term technical and business strategy, helps hire and develop engineering leaders and developers, and ultimately enables us to deliver engineering innovations.This role is posted for Arlington, VA, but we are flexible on location at many of our offices in the US and Canada.
US, VA, Arlington
Employer: Amazon.com Services LLCPosition: Data Scientist IILocation: Arlington, VAMultiple Positions Available1. Manage and execute entire projects or components of large projects from start to finish including data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.2. Oversee the development and implementation of data integration and analytic strategies to support population health initiatives.3. Leverage big data to explore and introduce areas of analytics and technologies.4. Analyze data to identify opportunities to impact populations.5. Perform advanced integrated comprehensive reporting, consultative, and analytical expertise to provide healthcare cost and utilization data and translate findings into actionable information for internal and external stakeholders.6. Oversee the collection of data, ensuring timelines are met, data is accurate and within established format.7. Act as a data and technical resource and escalation point for data issues, ensuring they are brought to resolution.8. Serve as the subject matter expert on health care benefits data modeling, system architecture, data governance, and business intelligence tools. #0000
US, TX, Dallas
Employer: Amazon.com Services LLCPosition: Data Scientist II (multiple positions available)Location: Dallas, TX Multiple Positions Available:1. Assist customers to deliver Machine Learning (ML) and Deep Learning (DL) projects from beginning to end, by aggregating data, exploring data, building and validating predictive models, and deploying completed models to deliver business impact to the organization;2. Apply understanding of the customer’s business need and guide them to a solution using AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances;3. Use Deep Learning frameworks like MXNet, PyTorch, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our customers build DL models;4. Research, design, implement and evaluate novel computer vision algorithms and ML/DL algorithms;5. Work with data architects and engineers to analyze, extract, normalize, and label relevant data;6. Work with DevOps engineers to help customers operationalize models after they are built;7. Assist customers with identifying model drift and retraining models;8. Research and implement novel ML and DL approaches, including using FPGA;9. Develop computer vision and machine learning methods and algorithms to address real-world customer use-cases; and10. Design and run experiments, research new algorithms, and work closely with engineers to put algorithms and models into practice to help solve customers' most challenging problems.11. Approximately 15% domestic and international travel required.12. Telecommuting benefits are available.#0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Manager III, Data ScienceLocation: Bellevue, WashingtonPosition Responsibilities:Manage a team of data scientists working to build large-scale, technical solutions to increase effectiveness of Amazon Fulfillment systems. Define key business goals and map them to the success of technical solutions. Aggregate, analyze and model data from multiple sources to inform business decisions. Manage and quantify improvement in the customer experience resulting from research outcomes. Develop and manage a long-term research vision and portfolio of research initiatives, with algorithms and models that to be integrated in production systems. Hire and mentor junior scientists.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, VA, Arlington
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist IILocation: Arlington, VirginiaPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, IL, Chicago
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist ILocation: Chicago, IllinoisPosition Responsibilities:Build the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. Tackle cutting-edge, complex problems such as predicting the optimal location for new Amazon stores by bringing together numerous data assets, and using best-in-class modeling solutions to extract the most information out of them. Work with business stakeholders, software development engineers, and other data scientists across multiple teams to develop innovative solutions at massive scale.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Bellevue
How do you design and provide right incentives for millions of sellers that inbound and ship billions of customer orders? How do you measure sellers' response to /causal impacts of capacity control policies we implemented at Amazon using the state-of-the-art econometric techniques? How do you optimize Amazon’s third-party supply chain using new ideas never implemented at this scale to benefit millions of customers worldwide? How do you design and evaluate seller assistance to drive their success? If these type of questions get your mind racing, we want to hear from you.Supply Chain Optimization Technologies (SCOT) optimizes Amazon’s global supply chain end to end and build systems to deliver billions of products to our customers’ doorsteps faster every year while saving hundreds of millions of dollars using economics, operational research, machine learning, and scalable distributed software on the Cloud. Fulfillment by Amazon (FBA) is an Amazon service for our marketplace third party sellers, where our sellers leverage our world-class facilities and provide customers Prime delivery promise on all their goods.We are looking for the next outstanding economist to join our interdisciplinary team of data scientists, research scientists, applied scientists, economists. The ideal candidate combines econometric acumen with strong business judgment. You have versatile modeling skills and are comfortable extracting insights from observational and experimental data. You translate insights into action through proofs-of-concept and partnerships with engineers and data scientists to productionize. You are excited to learn from and alongside seasoned analysts, scientists, engineers, and business leaders. You are an excellent communicator and effectively translate business ideas and technical findings into business action (and customer delight).Key job responsibilitiesProvide data-driven guidance and recommendations on strategic questions facing the FBA leadershipDesign and implement V0 models and experiments to kickstart new initiatives, thinking, and drive system-level changes across AmazonHelp build a long-term research agenda to understand, break down, and tackle the most stubborn and ambiguous business challengesInfluence business leaders and work closely with other scientists at Amazon to deliver measurable progress and change
US, WA, Seattle
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve the employee and manager experience at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science!The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.We are seeking a senior Applied Scientist with expertise in more than one or more of the following areas: machine learning, natural language processing, computational linguistics, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling. In this role, you will lead and support research efforts within all aspects of the employee lifecycle: from candidate identification to recruiting, to onboarding and talent management, to leadership and development, to finally retention and brand advocacy upon exit.The ideal candidate should have strong problem-solving skills, excellent business acumen, the ability to work independently and collaboratively, and have an expertise in both science and engineering. The ideal candidate is not methods-driven, but driven by the research question at hand; in other words, they will select the appropriate method for the problem, rather than searching for questions to answer with a preferred method. The candidate will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders).About the teamWe are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.
US, WA, Bellevue
Job summaryThe Global Supply Chain-ACES organization aims to raise the bar on Amazon’s customer experience by delivering holistic solutions for Global Customer Fulfillment that facilitate the effective and efficient movement of product through our supply chain. We develop strategies, processes, material handling and technology solutions, reporting and other mechanisms, which are simple, technology enabled, globally scalable, and locally relevant. We achieve this through cross-functional partnerships, listening to the needs of our customers and prioritizing initiatives to deliver maximum impact across the value chain. Within the organization, our Quality team balances tactical operation with operations partners with global engagement on programs to deliver improved inventory accuracy in our network. The organization is looking for an experienced Principal Research Scientist to partner with senior leadership to develop long term strategic solutions. As a Principal Scientist, they will lead critical initiatives for Global Supply Chain, leveraging complex data analysis and visualization to:a. Collaborate with business teams to define data requirements and processes;b. Automate data pipelines;c. Design, develop, and maintain scalable (automated) reports and dashboards that track progress towards plans;d. Define, track and report program success metrics.e. Serve as a technical science lead on our most demanding, cross-functional projects.