Modern Warehousing Capabilities Mirror the Goals of Industrial Internet of Things

Warehouses and distribution centers are undergoing a quiet revolution in the adoption of advanced technologies. The warehouse is on the trajectory to implement many of the capabilities sought in the vision of the Industrial Internet of Things (IIoT). eCommerce is has been a driver in this sector as companies such as Amazon, Walgreens, and LL Bean are seeking out solutions that are robust, high speed, and have little labor content. The margin pressure in the retail sector is placing incredible demands on operations to reduce labor content, increase production output, and reduce warehousing space. Innovations abound in the warehouse today with the wide use of automated and semi-automated systems used to track, retrieve and store goods. Palletizing robotics, autonomous fork lifts, tracking systems, and scanning tunnels are now the foundation of the current generation of highly automated warehouses. However, underpinning many of these systems is proliferation of machine vision technology which is being utilized as an advanced sensor. Automation in the warehouse and distribution center has come to the forefront in technology adoption as the industry has been catalyzed by the onset of ecommerce. As margins in the business continue to erode, warehousing solutions are driving toward minimal labor handling. This has been underscored by Amazon’s plants to deploy over 10,000 of its KIVA robots in its facilities by the end of 2015.

Warehousing and distribution centers continue to be a hotbed for incorporating the latest innovations in automation technology. A diverse range of technologies have been enablers in these advancements; however, machine vision sensors have underpinned many of the innovations.   Machine vision sensors have been making inroads in warehousing as material handling automation seeks to improve operational effectiveness while also increasing the range of products that can be handled. Automated material handling systems are not only tracking cartons through the maze of conveyance and sortation systems, but automatically determining volume and weight without interrupting the flow of parcels. Machine vision has been an enabling technology to solve these challenging problems as it provides a more robust solution in a world where shapes, weights, and identification are not always easy to determine. Machine vision integrated with robotic systems and high speed automation is making inroads into warehouse on several fronts which include barcode reading, volumetric scanning, package inspection, natural feature based navigation, and 3D guided robotic palletizing. In many instances, machine vision has been the only viable way to solve an automation problem. Vision systems are enabling automated handling systems to be more tolerant of size variations, creating order out of disorder, and proving to be more robust when reading barcodes. The benefit is that fewer packages require manual intervention, shipping container sizes are being minimized leading to a value proposition of Machine Vision that is being buoyed by the increased efficiency in warehousing.   Many of these capabilities are sought after in the IIoT vision of future manufacturing.

Warehousing Automation Rivals the Factory Floor
Large warehouses can rival the complexity of high production factory floors. To manage this complexity, material handling automation architectures have become stratified and connected through well-defined interfaces to facilitate the integration of newer technologies that provide operational improvements. A five layer hierarchical model is often used to define a manageable automation architecture for the integration of business systems, warehouse control systems, distributed automation controllers, tracking systems, and sensors. Automated material handling systems are integrating business systems with conveyance and sortation systems handling up to 300 cartons per minute. This is almost mind numbing considering the variety of package sizes and weights. At the sensor level, machine vision is now being incorporated to provide a wider operational range for many of the machines and tracking systems. Inline product identification allows transport systems make decisions at the latest stage possible. High volume shippers such as UPS and FedEx have used this capability to increase the supply chain visibility in their sortation centers. Just think of how easy it is to reroute a package after you have shipped it. Proliferation of vision based identification systems that are connected over the high speed internet has enabled shippers to use the unique package ID to provide instructions to every piece of handling equipment whether it is a conveyor, diverter or fork lift truck. When you step back a tremendous amount of these achievements resonate with the goals of IIoT.

Sal Spada is a Research Director, Discrete Automation, at ARC Advisory Group. Sal’s focus areas include General Motion Control, Material Handling, Machine Safeguarding, Computer Numerical Controllers, Robotics, and Servo Drives. Sal has been with ARC since 1997. Prior to ARC, Sal worked with EG&G Torque Systems, Boston Digital Corporation, and, most recently, Schneider Automation, all in the areas of CNC and General Motion Control Systems. At his most recent position at Schneider Automation he was Product Marketing Manager for Multi-axis General Motion Control Systems. Sal holds both a B.S. and an M.S. in Electrical Engineering from the University of Massachusetts with a concentration in Adaptive Control Systems. He also holds an M.B.A. from Babson College.