Sarah Mellish of Yaskawa Motoman Robotics explains the role of artificial intelligence for robotic order fulfillment:
“Technical innovations in artificial intelligence and robotics have unlocked massive potential for retailers to automate the handling of diverse Stock Keeping Units (SKUs) in the order fulfillment process with human-like flexibility at high speed….”Read More >>
A Neocortex® G2R (Goods to Robot) Cell is easy to deploy, like a collaborative robot. Yet it is uses an industrial Yaskawa Motoman robot with up to twice the capacity and 150% the speed of a collaborative robot (up to 800 items per hour).
How easy to deploy is the Neocortex G2R Cell? The average time for a Cell to be up and running at a customer site is one day. See our fun time-lapse video here. After it is up and running, our team then optimizes the robot and trains your operators. This contrasts with integrating an industrial robot system that may take weeks before being ready to run. The Cell’s deployment efficiency is comparable a collaborative robot’s average setup time, which is about half a day.Read More >>
Note from Bob Ferrari’s Post on Aug 7, 2014 entitled Permanent Shifts in Consumer Shopping Trends Have Supply Chain Implications. He comments on a quote in the article: Online Customer Fulfillment, Retail Supply Chain, Supply Chain Business Process that cites the following from Shopper-Trak: “Online sales have grown more than 15% every quarter for the past two years and are having a big impact on the way many companies are looking at their brick-and-mortar stores…. Rather than networks of distribution centers and fleets supporting individual physical stores, the new emphasis will be on high-volume online fulfillment supported by combinations of fulfillment centers and multi-purpose retail outlets.”Read More >>
Check out this video showing Universal Robotics Neocortex – a patented next-generation machine intelligence software platform. It is guiding a robot to pick up a wide range of parts, providing flexible automation.
Traditionally, flexibility consisted of manually reconfiguring mechanical systems and sensors, and manually re-engineering algorithms to accommodate new parts. Neocortex’s machine learning can automatically handle a wide range of changing parts, reducing the need for manual changeovers. In the past, when a new part was introduced, even if the robot could pick up the new part, the robotic work cell would still require changes in fixturing, sensors, machine vision algorithms, and machine control.
In this video, see Neocortex guide the robot to handle various parts. Also shown is a demonstration of the simple training method for those occasional times an operator needs to teach Neocortex something new – a process that takes less than two minutes.Read More >>