Today there are AMR’s that can handle almost all styles of picking. However, there are different providers that specialize in different AMR’s. Some of these workflows include:

To get the best blend of robotics in your DC, you may need different AMR’s from different providers to satisfy every process. This can lead to integration complexities. 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.

This is where utilizing an integration partner, like smart software and mobile technologies to orchestrate and optimize these processes can help you get the most out of robots and reduce time and e­ffort for human co-workers. This software based approach in most cases does not require any new automation systems or changes to warehouse layouts or storage systems.

Most of the robot types we introduced above can be integrated into your warehouse operations regardless of your operating software. In most cases, a WMS follows basic logic and location sequence pick paths in allocating work. But it is not looking at batch and path optimization without robots. So if you’re going to start looking at how best to orchestrate the work of humans and robots, chances are you probably don’t have the systems or the logic in place.

To improve decision making and task execution in your system involving humans and robots, it’s essential to implement an additional layer that synchronizes all three components under one controlling entity. Typically, a WMS (Warehouse Management System) lacks this capability.

WMS usually follows a fixed location sequence, using a snake pick path around the facility, which is not optimal. Even if there are some optimized paths within the WMS, they are often hardcoded and inflexible. For a truly dynamic approach, a solution that allows intuitive real-time adjustments is required.

AMRs optimize for robots, not people. There are two common processes with AMR’s – follow-the-robot/cobot styles, or zone picking. Cobot picking means that a human follows the robot, more often than not through a WMS allocation of tasks. Zone picking keeps the humans in a fixed zone. This reduces human travel, but means that pickers need to be allocated to the correct zones and ready for AMR’s. It can be quite static. While a WMS, or an AMR company might claim that they do optimization of travel, again, this is something that is hard coded and not dynamic. There are actually three different aspects of optimization that make it all truly dynamic – work prioritization, batch optimization and finding the most efficient pick path.

Before any system can get into creating efficient work, we must first understand that warehouses have priorities via certain types of orders, service levels, important customers or carrier cut off times. And these priorities often shift rapidly throughout the day. Before being fed orders, a best case scenario system should consider these priorities on a just in time basis, to increase pick density and create better batches, so as new orders come into the system, they are automatically slotted and prioritized appropriately. Warehouse leads and supervisors should have full control over changing priority for their orders too, because the system should operate on demand. In a best practice scenario, when a user asks for work, they always get optimally prioritized work.

As previously explored, zone picking can do this, but cobot picking is unlikely to do so. To be truly optimized, you need to harmonize the travel of both robots and humans. This reduces travel for humans, but doesn’t tie them to a zone. They dynamically travel to the most optimized pick and meet the AMR there.

To explain, cobots don’t necessarily reduce any human travel, because their concept generally involves a human traveling around with the robot at the same time. Obviously, if the robot goes in a more optimized path, then the human is going in a more optimized path as well. But the whole concept of a cobot is that you’ve got one human and one robot tied together.

The second option of zone based picking does reduce human travel, but it’s very rigid. In that scenario, you have humans in zones, so they might have an aisle or section that is allocated to them, but they will stay completely rigid within that zone. The whole concept of the solution is based upon the humans being there, when they’re supposed to, and a robot coming around into a zone, the human picking and loading to the robot, and the robot going on into a different zone. But if they need to go for a break, and no one replaces them in that zone, the whole solution can start to fall apart. Also, it’s harder to balance your workforce well in that scenario, because there might be one day where one zone has 50% of the picks and then the next day it has 1% picks. So you might have some people waiting around not picking while you might have someone else picking completely flat out. You still need a layer of orchestration that can reduce worker time at the pick face.

WMS and AMR companies lack true orchestration. To achieve genuine optimization, you must incorporate system software and AI to provide the necessary intelligence. The focus of optimization should be on minimizing the human pick path. However, AMR and WMS systems generally overlook this aspect and simply concentrate on the next easiest pick where a robot is already present in the same aisle. True orchestration and optimization do not follow a rigid zone-based picking approach; instead, they employ a hybrid model where the system dynamically makes decisions at various points to reduce human travel. This flexibility and dynamic decision-making are the critical factors that set true orchestration and optimization apart.

In an AMR-supported picking workflow, for example, a worker can avoid a lot of unnecessary walking by picking items to a tote on an AMR, directing the AMR to a conveyor system to unload, and then triggering another robot to move into place for the worker to continue picking. Interfaces with the robots and workers can be directed by voice. Similar to conventional voice picking systems, workers confirm their work using voice, scan, RFID, or robot mounted screens or lights.

In conclusion, the use of autonomous mobile robots (AMRs) in warehouses holds great potential for various benefits. However, in order to fully optimize and coordinate the tasks of humans and robots, it is important to address labor challenges by incorporating robots into multiple workflows, dynamically allocating workers and robots to maximize productivity, and improving ergonomics and safety. Despite common misconceptions about the implementation of human-robot collaboration in the warehouse, there are solutions available for effectively orchestrating and optimizing the work of people and robots.

With a strong analytical background and experience through his degree in Economics, he understands the nuances that drive design, implementation and delivery of financially successful solutions for customers. He utilizes his unique expertise in a variety of industries including telecommunications, medical devices and e-commerce fulfilment to bring significant value to customers.

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