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Warehouse of the Future: Digitalization and New Technologies Driving Next Generation Warehouse Automation – Part 2 of 2

In part 1 of this 2-part blog series, “Warehouse of the Future: Adopting Automation within the Supply Chain,” we took a deeper dive into today’s automation systems landscape and retrofitting today’s supply chain with automation. Part-2 of the series will go into more detail around the technologies driving next generation warehouse automation and digitalization and next generation capabilities.

Disruptive Technologies

Some of the most exciting technologies driving next generation warehouse automation will be by-products of innovation occurring in other areas. Some technologies to consider include driverless cars, drones, image recognition technologies, the Internet of Things (IoT) and machine learning.

  • Driverless cars –Technologies employed by driverless vehicles have long been employed by automation providers in order to promote safety. Sensors causing vehicles to slow or respond are standard in AGVs today. However, advancements in route optimization, like Google Maps, suggest a future state of continuous optimization in warehouse automation. Advances are likely in WMS that will provide a basic level of route optimization to better direct automated and non-automated vehicles, whereas WCS providers take this a step further with algorithms in three dimensions that optimize travel through storage grids that allow multiple points of entry/exit, but only for fully automated environments.
  • Drones – A variety of uses for drones have been explored in warehousing – as well as in transportation. The most attractive uses of drones in warehousing today focus on inventory verification (e.g. cycle counting), or asset verification (e.g. yard audits). Stability and control mechanisms are the primary concern with drones at this point, though image recognition technology will alleviate most of that concern in the yard and eventually in the warehouse.
  • Image Recognition Technology – Intelligent recognition of photo and video feed is available today, though many of the use cases that will leverage this technology in supply chain are still developing. There is an anticipation that shelf monitoring in stores (as a component of a broader Internet of Things evolution) will drive this evolution, with barcode recognition allowing drone based cycle counting, pallet selection in multi-slot locations, and even robotic arm picking in commingled totes. Investment in this area will likely come across the board from peripheral providers (in addition to augmented reality) as well as from WCS providers who will fully integrate the solution with their robotics.
  • IoT – Widespread recognition of product location in a facility will become more pervasive as Bluetooth location technology, image recognition, and advanced robotics continue to mature. The use of IoT in a warehouse will change the way that inventory management, task management, and vehicle routing are understood.
  • Machine Learning – Finally, advanced analytics and machine learning will continue to evolve, leveraging ever more information available from IoT in order to continuously drive optimization through stock location assignments and vehicle routing recommendations. The roadmap for machine learning will embrace both internal and external factors including weather, forecasts, marketing promotions, and port and road traffic congestion.

Digitalization and Next Generation Capabilities

Many of the challenges currently complicating the adoption of automation suggest areas of focus for the next generation of capabilities that will be brought to market. The WMS market will focus largely on those areas that optimize semi-automated facilities (i.e. where the improvements can increase efficiency of manual, automated, or combined operations), whereas the WES/WCS market will focus on increased efficiencies or speed to market advancements that improve the justification of the automation equipment’s adoption.

  • Dynamic Task Management – System optimization today largely depends on committing plans for inventory and tasks based on what is known at the time the system is generating those tasks. However, real time optimization can, and should, consider a broader set of variables. This will allow the system to determine the optimal pick or storage locations, inventory, and travel path, at the time the work will be performed, considering engineered expected times, shelf life requirements, and appointment times. Parlaying the advancements in individual assignments, interleaved task algorithms will deliver optimal work assignments for multitask instructions, re-calculating sets of individual optimizations into a single, comprehensive one. In automation, this could even be considered while work is in progress as congestion is encountered or as tasks are added or updated, triggering a re-evaluation of potential tasks for a re-optimized solution.
  • Load Building – Transportation optimization tools already perform basic truck and pallet building, some with advanced capabilities around stacking and weight distribution requirements. Increased attention to iterative optimization and asset utilization will continue to drive these capabilities, converging with automated picking capabilities to bring a comprehensive automated picking and loading solution to the marketplace also capable of driving the same results from manual operations.
  • Traffic Management – Currently, XYZ coordinates and equipment speed are considered in best of breed applications when identifying storage locations and dock door recommendations, but step by step information related to travel sequence and directions is needed to better guide driverless vehicles, and these capabilities will likely evolve in WMS. This capability will drive efficiency in manual operations as well, with systemic understanding of congestion avoidance options delivering recommendations to the drivers (or robots) in real time. These additional considerations will also be valuable to machine learning, where insights can be evaluated for facility design improvements, business process changes, or re-warehousing.

Adopting Automation in the Digital Age

The benefits expected from advances in the market to promote automation adoption will not be realized solely through automation. Manual and semi-automated operations also have much to gain. Dynamic task management offers tremendous opportunities in large facilities to increase workforce efficiency, regardless of automation levels. Image recognition technologies will improve pick efficiencies and accuracy, improving service levels to customers at the same time. Traffic management introduces opportunities to drive efficiency and avenues to motivate the workforce through gamification. But, perhaps the greatest promise is inherent in the shift to a real-time optimization paradigm, where systems can recognize and correct inefficiency through machine learning, or even anticipate environmental changes affecting buying habits or material flows, and react to address potential risks of disruption in our increasingly digitized world.

For more information download the Future Series white paper, “Adopting automation in the digital age.”

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