Robots Boost Capability for Supply Chain Scalability
Technology Revolutionizes Manufacturing And Warehousing Operations
By Chris Caldwell, Product Manager at Yaskawa America Inc. – Motoman Robotics Division
Considered by some to be the backbone of the global economy, the distribution and logistics market continues to grow, revolutionizing manufacturing and warehousing operations. To accommodate the 64% of supply chain leaders accelerating investment in advanced technology and to help them deal with on-going challenges in high-mix, low-volume production environments, automation suppliers and integrators are fast-tracking flexible solutions that successfully.
From retrofitting current operations to creating new facilities for Distributed Manufacturing Systems (DMS), much of this robotic adoption is the result of expanded vision capability. Speed enhancements for the acquisition of images and recognition of parts for 2D machine vision have expanded robot functionality to diverse markets, while more robust software algorithms have provided robots with 3D vision capability to sense and handle a broader mix of parts.
Innovative cameras and sensors are also being mixed with computational computing speed and artificial intelligence (AI) to expand robot utilization to extremely unstructured environments where there is limited knowledge of object size, shape or orientation. From upstream induction processes to final trailer loading, the latter concept fosters intelligent robot control systems to scale operations.
Thanks to the many advances in robot perception, manipulation and mobility, industrial robots are more capable than ever, boosting automation confidence for facilitating a variety of key tasks:
Parcel induction and sortation
The mass flow of product from an induction piece – where objects are accumulated in a chaotic manner, often in a bin – demands substantial speed and accuracy. To keep competitive, product mixes for flats, rounds, cases, etc. typically require the well-handled movement of 1,500 to greater than 2,000 parcels/parts per hour. Doing this usually dictates the use of a streamlined, high-speed robotic arm placed near a bin or chute that has a 3D vision system attached above it to provide the robot accurate information for object location, as well as path planning and validation.
Other solution accommodations often made for this application include bar code scanning with a label-up object orientation and single file placement for downstream processes such as edge referencing, metering of minimum gap between objects, reorientation for induction to downstream equipment, inversion for scanning/reading of data, and rejection for weight, damage, no-reads, etc.
Generally applicable to the parcel segment, the process for last-mile sortation greatly benefits from robotic automation – as efficient and accurate placement of objects into a final tote or bag for on-time delivery is of the utmost importance. To do this, small parcels and flats are usually picked from large totes or bins and placed into smaller bags (and, sometimes, sorted by zip code or transportation company). An extended-arm robot equipped with a feature-rich 3D vision hardware/software solution can expertly perform this task, especially for deep totes.
Popular for eCommerce, put wall sortation is another area that is being addressed with highly consistent, agile robots. Similar to last-mile sortation – where products can be inducted via infeed conveyance or by mass flow diversion to a gravity chute – the process uses a robot with 3D vision capability to scan the objects and perform any necessary path planning, placing objects into the appropriately assigned bins (often by zip code or route) on the put wall. A human operator then discharges the object into a full chute to a poly bag for sealing, before it is transitioned to last-mile sortation for final delivery preparation.
Key to staying profitable among fierce competition is the ability to prepare, package and deliver/return orders in a timely way – especially where same-day delivery is concerned. Helping to process orders, the use of robotic automation is facilitating zero-error delivery in the shortest amount of time possible.
Ideal for processing multiple, diverse stock keeping units (SKUs) in a single tote, the goods-to-robot method of micro-fulfillment is well-suited for sorting applications, reaching in excess of 900 picks per hour. 3D vision quickly locates objects within the bin, and frequently, these systems work in conjunction with automated storage and retrieval systems.
With respect to online order fulfillment, picking is widely used to pick a single item or SKU from a master tote/location to get goods to the robot, then on to the consumer. The main idea is to efficiently transfer ordered products from the tote to the appropriate shipping items with the least number of human touches as possible. A fast, handling robot with a flexible gripper and 3D sensor technology expertly achieves this, optimizing order accuracy and on-time delivery.
As a subset of the fulfillment process, kitting – or the assembly of assorted products or “kits”– is going a long way to bolster supply chain resiliency, smoothing peak season or high-growth product bottlenecks. Typically combining a robust robot with a high-resolution 3D vision system and AI technology, today’s cutting-edge kitting systems learn how to quickly identify and pick up items in varying size, weight and shape out of bulk storage to singulate them.
A tertiary stage of order fulfillment, robotic palletizing efficiently performs this back-breaking task with ease. Simple systems with no robotic vision are extensively used for case or bag palletizing. Offline programming platforms for fast and easy creation of palletizing patterns for virtually any mix of SKUs greatly facilitate this, enabling faster workcell deployment for even the most complex patterns. While the palletizing of homogenous layers of any single SKU is quite common, manufacturers and distributors of all sizes are turning to mixed-case palletizing to accommodate demand-driven loads.
Capable of handling and stacking multiple SKUs within a layer – where dissimilar packages such as boxes, bottles, polybags and more are used – mixed-case palletizing is made possible through enhancements in robotic vision, machine learning and AI with software intelligence. Product characteristics drive the pallet build algorithms which correlate data like sequence and orientation of the packages in relation to the robot and gripper coordinates. Extremely intelligent, the software helps place large, heavy products on the bottom of pallets, while small, lightweight objects are placed on top of loads.
Overall, the use of intuitive tools and pallet pattern generation software creates perceptive and adaptable robotic systems for user-friendly palletizing that calculates the optimum number of unit loads (or pallets) that can be created from the pick sequence of an order fulfillment.
Another application gaining widespread favor, depalletizing is especially beneficial for cross dock facilities. Frequently tilted at varying angles, pallets can now be accurately and efficiently depalletized via highly capable robots. Whether to feed a stocking system or something else, six-axis, extended reach robots with 3D vision capability and intuitive software are ideal for this application.
Thanks to LiDAR sensors for light detection and range, as well as fast image processing and wireless bandwidth, highly adaptable autonomous mobile robots (AMRs) are also meeting various production needs for dynamic factory and distribution environments. These mobile platforms can be equipped with robots, vision systems and custom end-of-arm tooling (EOAT) to autonomously maneuver through a facility and perform assigned tasks such as picking, sorting and on-demand material transport with ease and safety.
Viewed by some as a competitive edge game-changer, the ability to stack sorted packages in an organized manner for delivery or facility-to-facility distribution is extremely helpful. A mobile robotic platform, capable of gaining complete access inside the truck for retrieval or placement of packages, is ideal. Proper EOAT, a capable vision system, and a suitable conveyor system can work with the robot to quickly, accurately, and ergonomically load and unload packages.
Transform the future with adaptive technology
With 39% of retail sales projected to be filled via eCommerce by 2030, the utilization of robotic automation will continue to grow. Similarly, buy-in and subsequent funding for flexible robots, including easy-to-program collaborative robots, will facilitate the many processes required to maintain fluid supply chain movement.
The use of enterprise resource planning (ERP) or asset management platforms for more informed decision making throughout the extended enterprise will also help navigate future struggles, enabling companies to move forward with a clear strategy for a stronger return on investment.
About the Author
Chris Caldwell is a product manager at Yaskawa America Inc. – Motoman Robotics Division. Learn more at www.motoman.com.