Assembly Line

The evolution of Amazon’s inventory planning system


Topics: demand planning, operations research, E-commerce, glocalization

Organizations: Amazon

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.

Read more at Amazon Science

Why Robots Can’t Sew Your T-Shirt


Author: Harris Quinn

Topics: Glocalization, reshoring

Vertical: Apparel, Textiles

Organizations: SoftWear Automation, Sewbo

But sewing has been notoriously difficult to automate, because textiles bunch and stretch as they’re worked with. Human hands are adept at keeping fabric organized as it passes through a sewing machine. Robots typically are not deft enough to handle the task.

SoftWear’s robots overcame those hurdles. They can make a T-shirt. But making them as cheaply as human workers do in places like China or Guatemala, where workers earn a fraction of what they might make in the US, will be a challenge, says Sheng Lu, a professor of fashion and apparel studies at the University of Delaware.

SoftWear calls its robotic systems Sewbots. They are basically elaborate work tables that pair sewing machines with complex sensors. The company zealously guards the details of how they work, but here are the basics: Fabric is cut into pieces that will become parts of the shirt: the front, the back, and the sleeves. Those pieces are loaded into a work line where, instead of a person pushing the fabric through a sewing machine, a complicated vacuum system stretches and moves the material. Cameras track the threads in each panel, allowing the system to make adjustments while the garment is being constructed.

Read more at WIRED

Going Glocal: What is Glocality and How Can Cloud Manufacturing Help?


Topics: cloud manufacturing, Glocalization

Organizations: Kreatize

Glocalization is a combination of the words “globalization” and “localization.” It is used to describe a service or product that is made and distributed globally, but adjusted to better accommodate the customer in a local market. For manufacturing, it is when global hardware companies produce locally to better meet demand.

Read more at Kreatize Blog