Autonomous mobile robots, or AMRs, are the latest trend in warehouse automation. Ecommerce companies, traditional retailers, and 3PLs view AMRs as a key technology to rein in the high labor costs associated with ecommerce fulfillment.
Scores of start-up robotics companies are applying autonomous driving technologies in a variety of robotic solutions for the warehouse and distribution market. Other companies are developing robotic arms and/or grippers of various types for picking products. And some companies are creating robots that can both pick and transport goods in a DC. Although the technology may be taking off, the solution is still early in development and may not offer a strong ROI for all DC operations. However, there are ways to maximize AMR investments by utilizing software solutions to optimize the coordination and communication amongst human workers and bots.
Building on Kiva’s (Amazon Robotics) legacy, today’s goods-to-person autonomous mobile robots take two forms: some that take cartons from shelves to picking stations and others that carry shelves to pickers. These solutions are typically paired with dedicated pick-pack stations. They are most applicable to ecommerce picking operations with very large SKU counts and small order sizes.
The latest twist on autonomous mobile robots for each picking is robot-to-goods AMRs, or swarming robots. In this approach, numerous AMRs travel among workers in pick zones. The robots carry totes to pick locations and meet workers in the picking area. The human workers pick and place the items in totes based on instructions on a tablet mounted to the deck of the robot. The AMRs may incorporate scanners and put-to-light technology.
The robot-to-goods approach is being used primarily for each picking in ecommerce fulfillment centers. Unlike goods-to-person AMRs, the robot-to-goods model does not completely eliminate human travel.
Several start-up companies are offering robots that convey products over long travel distances between DC areas. These conveyance robots offer DCs a more flexible and cost-effective alternative to conveyors or traditional AGVs.
Conveyance AMRs are an ideal complement to systems and technologies that maximize conventional picking processes within picking zones – like Lucas Work Execution with Dynamic Work Optimization.
Traditional automation systems are sized to handle maximum throughput which may only be reached for a few weeks per year. By contrast, warehouse robotic solutions can be scaled up or down by adding or subtracting robots and/or pick-pack stations.
AMR’s have free reign to travel throughout a facility, so they can often be deployed in existing facilities without changing racking or the DC layout. If your layout changes after deploying robots, the robots can adapt without moving any fixed infrastructure. Likewise, with conveyance robots you can define new drop off or induction points through software changes and the robots will adapt.
For our calculation we will summarize the costs of a goods-to-person or robot-to-goods picking solution with 10 workers.
We are assuming a ratio of 3 or 4 robots per human picker. That is the robot:worker ratio suggested by robotics companies to achieve maximum productivity and throughput. Therefore we are utilizing 30-40 robots for our 10 workers.
Based on published reports, the cost per AMR is approximately $30,000. (Note that this cost does not include implementation costs for WMS integration, development, on-site testing and deployment, and training.)
Therefore, our upfront cost for 30 AMRs will be $900,000 (30 X $30,000) and for 40 AMRs it will be roughly $1,200,000 (40 x $30,000).
In addition to the upfront capital cost of AMRs, they also include an annual maintenance fee of 20%. Thus, if we implemented 30 robots at $30,000 a piece we would be paying $180,000 ($900,000 x 0.2) and at 40 it would be $240,000 ($1,200,000 x 0.2).
For purposes of our ROI calculations, we compared the costs for installing AMRs to the labor costs saved by using robots. We estimated average annual labor costs per worker of $35,000 (roughly $17.50/hour including benefits). We used these costs to estimate the time to achieve a 100% return on investment in DCs achieving 2X greater productivity with robots than without.
Calculating the robotic solution over 5 years including both our annual maintenance and capital cost comes to $1,800,000 ($900,000 + ($180,000 x 5)) for 30 bots and $2,400,000 ($1,200,000 + ($240,000 x 5)) for 40 bots.
Our ROI calculations for 2X productivity (a 100% increase in pick rates/hour compared to picking without robots) assumes the same level of production with 20 full time pickers and no robots compared to 10 workers with robots. Working without our additional 10 pickers saves us $1,750,000 across 5 years (($35,000 x 10) x 5). In that scenario it would take more than five years for a DC to earn a 100 percent ROI with a 3:1 [AMR:Picker] ratio and eight years with a 4:1 ratio.
The table summarizes our ROI calculation for a typical AMR solution. These figures assume there are no other costs associated with deploying AMRs, including new staff to maintain the new equipment.
Dynamic Work Optimization dynamically manages priority, maximizes pick density, and optimizes travel paths in picking and other travel-intensive warehouse processes. Using order, inventory, and location information from WMS and other systems, DWO applies real-time optimization algorithms to create work assignments. DWO then uses a virtual model of your facility to define an optimal pick sequence or travel path. Unlike rule-based batch and pick-path strategies, AI-based optimization technology dramatically increases pick density, reduces travel and also coordinates the movement amongst robot and human workers. Below are two great use cases for utilizing DWO to coordinate humans alongside robots instead of traditional/manual automation. Utilizing DWO alongside mobile robots helps more than double productivity by cutting travel time in half.
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 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.
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.n a revamped warehouse, conveyors are eliminated allowing more storage space and extra bays in each aisle. 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 DWO.
In this scenario, robots are performing the long distance travel by carrying orders between zones, packing areas and sorters while humans 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.
Watch our brief overview of AMRs and how Lucas can optimize the coordination and communication of humans and robots in your warehouse or DC. If you’re interested in learning more, click the button below to get a demo.