A recent research report performed by ABI Research concluded there will be more than 4 million commercial robots installed in 50,000 warehouses by 2025. Ecommerce orders will continue to put stress on warehouse operators, causing them to invest in both extra labor and automation. As robots find their place in the DC, warehouse operators will face new challenges in coordinating and optimizing the work of humans and their robotic coworkers.
First Generation DC Robots
Currently, Autonomous mobile robots (AMRs) are highly dependent on human workers to handle products. Although some technology companies are developing fully autonomous picking robots that can travel and pick product, those robots are much further from wide commercial adoption than AMRs.
Kiva, which was acquired by Amazon in 2013, was a pioneer of autonomous mobile robots for goods to man picking. The Kiva robots rely on people to unbox items and put them in bins on shelves that are later delivered to workers who pick products. Newer AMRs from companies like Locus Robotics can also travel to pick locations where they meet workers who pick and place items on the robots (robot-to-goods picking, as opposed to goods-to-man).
The symbiotic relationship between workers and robots has given rise to a new term, collaborative robots or cobots. Cobots complement pickers and other warehouse staff, making people more productive and thereby reducing the number of workers needed to ship a given volume of orders. Kiva robots didn’t eliminate people from handling products, but they have helped Amazon rapidly grow its distribution network and add tens of thousands of new DC workers.
Robots Create New Warehouse Execution Challenges
Cobots will force DCs to change how they organize and coordinate the work performed by humans and their robotic helpers. Using AMRs as take-away or transport systems will eliminate some worker travel, but warehouses will still need to optimize pick rates and minimize worker travel within picking areas.
In short, even DCs using robots will need work execution software for manual process optimization through intelligent work batching, real-time pick-path optimization and other forms of software smarts. Likewise, human order pickers will have to be closely coordinated with AMRs to ensure your DC is fully utilizing its robots and getting products sorted, packed, and shipped on time.
Coordination of workers and AMRs is even more challenging in a robot-to-goods scenario where workers interact with AMRs at pick locations. This represents an all-new kind of optimization challenge that requires warehouse execution software that directs workers and robots. Today, most robotics systems do not direct workers; pickers read their pick instructions on the screen of the robot but they are otherwise unconnected from the robot control software.
This new warehouse execution challenge requires software solutions that can optimize processes and direct the activities of robots and people, while providing flexibility, scalability, and efficiency. These solutions meld work execution for manual activities with robot control systems. Most importantly, these solutions will strike a balance across a number of operational objectives, not just reduced labor costs. Lucas Dynamic Work Optimization (DWO) reduces travel for human workers through intelligent batching and path optimization, but it also coordinates movement amongst robot and human workers.
Using DWO to Optimize the Coordination and Communication of Humans & Robots
There are two great use cases for utilizing DWO to coordinate humans alongside robots instead of traditional/manual automation.
Conveyors can move product quickly and efficiently to reduce travel for pickers in the warehouse. However, conveyors can restrict movement from zone to zone and limits the ability to pick and pass orders throughout the warehouse. Pickers will have to walk around the conveyors in order to go from Zone A to Zone B.
In a revamped warehouse, conveyors are eliminated allowing more storage space and extra bays in each aisle (see video below). The extra space and elimination of the conveyors as barriers allows for passing of orders from zone to zone, increasing the ability for batching and utilizing Dynamic Work Optimization.
In this scenario, robots are performing the long distance travel and carrying orders from zones to the packing locations. Robots are used for the long travel between zones, packing areas and sorters. The robots do not go down the aisles, but only pick up carts and transfer them as opposed to conveyors moving product. Humans will push the wheeled carts down the aisle to each location as determined by DWO. In summary, Lucas will batch orders together for the pickers and then leverage robots for the takeaway which is heavy on travel.
Coordination of Robots and Humans in Aisles
Secondly, DWO can coordinate picking with robots in zones/aisles. Currently AMR companies have humans read a robot screen to know where their next pick will be. However, the Lucas system will voice direct the picker and allow them to confirm the pick via voice, scanning or screens. Coordination and communication amongst humans and robots will add significant value to warehouses and distribution centers.
The Lucas system will direct the picker through the warehouse while simultaneously positioning the robots to be at the pick locations. Robots will be waiting for the picker to quickly make their selection and move on to the next location, thus eliminating any unnecessary waiting time for humans at pick locations. In addition, time is reduced at the pick location because pickers will not be scanning in to the robots as they normally do. Pickers will simply speak a check string or scan the item to confirm the correct order with Jennifer. In a scenario with multiple robots, each robot will be able to receive the pick and then stage itself at the next location so the picker is not slowed to the speed of the robot.
If the system detects sufficient demand or if labor is made available, then the system will direct another worker into the zone. As this happens Lucas will split up work amongst workers and robots in the most efficient way possible to reduce travel between the picks. The robots may reposition themselves to meet the picker where they are headed.