OEM : Retail
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth’s most customer-centric company, Earth’s best employer, and Earth’s safest place to work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon.
How Amazon learned to cut its cardboard waste
David Gasperino, an Amazon principal research scientist, led the technical development of PackOpt, which is helping Amazon’s stakeholders to not only minimize the amount of “air” shipped to customers, but also helping Amazon deliver on its Climate Pledge commitment to reaching net-zero carbon emissions across its business by 2040.
“To create an optimal set of boxes, you need to select a small subset of columns to pack all of the shipments, and those columns must lead to the smallest overall box volume when you sum it all up,” explains Gasperino. It is a hard challenge — literally. “This problem belongs to a theoretical class of problems called ‘NP hard’
Amazon Shows Off Impressive New Warehouse Robots
Proteus is our first fully autonomous mobile robot. Historically, it’s been difficult to safely incorporate robotics in the same physical space as people. We believe Proteus will change that while remaining smart, safe, and collaborative.
Proteus autonomously moves through our facilities using advanced safety, perception, and navigation technology developed by Amazon. The robot was built to be automatically directed to perform its work and move around employees—meaning it has no need to be confined to restricted areas. It can operate in a manner that augments simple, safe interaction between technology and people—opening up a broader range of possible uses to help our employees—such as the lifting and movement of GoCarts, the nonautomated, wheeled transports used to move packages through our facilities.
How Amazon robots navigate congestion
“When we first started looking at it, we thought it would take more than 8,000 robots to keep an Amazon fulfillment center running,” Durham said. “There just was not enough room for them all. That’s when we said, ‘Wow, we really have to solve the congestion problem.’ And we have addressed it — we’ve gotten dramatically more efficient.”
While good work allocation and route decisions smooth traffic flow and reduce unnecessary trips, managing the actual movement of robots is also important. To simplify the task, Amazon’s cloud computing service creates the virtual equivalent of a map of a city grid, on which robots can travel ‘north-south’ or ‘east-west’. Once a robot picks up a pod, the computing service creates a route to its final destination.
Amazon launches $1 billion fund to invest in warehouse technologies
Amazon on Thursday launched a $1 billion fund to invest in companies developing supply chain, logistics and fulfillment technologies. The first round of investments will focus on technologies that can speed up deliveries and improve the safety of workers in its warehouses. Start-ups backed by the new fund include Modjoul, a company developing wearable safety technology that issues alerts and recommendations aimed at reducing injuries.
At Amazon Robotics, simulation gains traction
“To develop complex robotic manipulation systems, we need both visual realism and accurate physics,” says Marchese. “There aren’t many simulators that can do both. Moreover, where we can, we need to preserve and exploit structure in the governing equations — this helps us analyze and control the robotic systems we build.”
Drake, an open-source toolbox for modeling and optimizing robots and their control system, brings together several desirable elements for online simulation. The first is a robust multibody dynamics engine optimized for simulating robotic devices. The second is a systems framework that lets Amazon scientists write custom models and compose these into complex systems that represent actual robots. The third is what he calls a “buffet of well-tested solvers” that resolve numerical optimizations at the core of Amazon’s models, sometimes as often as every time step of the simulation. Lastly, is its robust contact solver. It calculates the forces that occur when rigid-body items interact with one another in a simulation.
Amazon Robotics Builds Digital Twins of Warehouses with NVIDIA Omniverse and Isaac Sim
How pioneering deep learning is reducing Amazon’s packaging waste
Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale, and a pioneering combination of natural language processing and computer vision is enabling Amazon to hone in on using the right amount of packaging. These tools have helped Amazon drive change over the past six years, reducing per-shipment packaging weight by 36% and eliminating more than a million tons of packaging, equivalent to more than 2 billion shipping boxes.
“When the model is certain of the best package type for a given product, we allow it to auto-certify it for that pack type,” says Bales. “When the model is less certain, it flags a product and its packaging for testing by a human.” The technology is currently being applied to product lines across North America and Europe, automatically reducing waste at a growing scale.
The evolution of Amazon’s inventory planning system
Forecasting models developed by Amazon’s Supply Chain Optimization Technologies organization 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.
“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.”
A new multi-echelon inventory 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.
In Amazon’s Flagship Fulfillment Center, the Machines Run the Show
More than the physical robots, the stars of Amazon’s facilities are the algorithms—sets of computer instructions designed to solve specific problems. Software determines how many items a facility can handle, where each product is supposed to go, how many people are required for the night shift during the holiday rush, and which truck is best positioned to get a stick of deodorant to a customer on time. “We rely on the software to help us make the right decisions,” says Shobe, BFI4’s general manager.
When managers wanted to figure out how many people they needed at each station to keep up with customer orders, they once used Excel and their gut. Then, starting in about 2014, the company flew spreadsheet jockeys from warehouses around the country to Seattle and put them in a conference room with software engineers, who distilled their work and automated it. The resulting AutoFlow program was clunky at first, spitting out recommendations to put half an employee at one station and half an employee at another, recalls David Glick, a former Amazon logistics executive who supervised initial development of the software. Eventually the system learned that humans can’t be split in half.
Factory Robots! See inside Tesla, Amazon and Audi's operations (supercut)
The history of Amazon’s forecasting algorithm
Historical patterns can be leveraged to make decisions on inventory levels for products with predictable consumption patterns — think household staples like laundry detergent or trash bags. However, most products exhibit a variability in demand due to factors that are beyond Amazon’s control.
Today, Amazon’s forecasting team has drawn on advances in fields like deep learning, image recognition and natural language processing to develop a forecasting model that makes accurate decisions across diverse product categories. Arriving at this unified forecasting model hasn’t been the result of one “eureka” moment. Rather, it has been a decade-plus long journey.
Amazon’s robot arms break ground in safety, technology
Robin, one of the most complex stationary robot arm systems Amazon has ever built, brings many core technologies to new levels and acts as a glimpse into the possibilities of combining vision, package manipulation and machine learning, said Will Harris, principal product manager of the Robin program.
Those technologies can be seen when Robin goes to work. As soft mailers and boxes move down the conveyor line, Robin must break the jumble down into individual items. This is called image segmentation. People do it automatically, but for a long time, robots only saw a solid blob of pixels.
The case of the missing toilet paper: How the coronavirus exposed U.S. supply chain flaws
Before executives at consumer-goods giant Kimberly-Clark rushed to shut their offices on Friday the 13th of March, they convened for one last emergency meeting. Commuting home that final time, Arist Mastorides, president of family care for North America, stopped at his local Walmart, on the edge of Lake Winnebago in Neenah, Wis., to see the emergency firsthand. Mastorides oversees toilet paper brands like Cottonelle and Scott, but that evening he could find none of his own products. “A long gondola shelf that’s completely empty of bathroom and facial tissue, I never in my life thought I would ever see that,” he says. “That’s a very unsettling thing.”
From apple juice to antibiotics: Coronavirus epidemic could cause U.S. shortages
The toll of the ongoing coronavirus epidemic in human life is already devastating enough. But as quarantines continue in China, it looks like the global economic impact of the virus could be incredibly destructive too.
China is a manufacturing superpower, supplying both critical equipment and items of convenience. With some of the country’s citizens unable to report to work and exports curtailed, there are already shortages that have some companies worried.