A unique advantage of the NextShift system is that it does not require a worker for every robot. This is possible because NextShift robots can independently pick up, transport, and put down totes without human interaction.
In fact, the number of pickers and the number of robots are modeled independently—and the ratio can be tuned to meet the specific balanced goals your company requires for minimizing labor cost, maximizing order fulfillment speed and deriving the maximum ROI for the robot investment.
It’s like having “automation dials” that can be tuned separately to produce a range of travel time, order speed and robot investment combinations.
Example: Flexing Robots to Optimize Throughput or Cost
Consider a case where we have a single line order to be picked from Bay A, a second single line order in Bay B, and a multi-line order to be picked from locations in Bays A, B and C. If each order is handled by a different robot we can minimize individual pick time for all three. That’s a straightforward optimization we use when we have decided the value of optimal order speed justifies an investment in three robots.
If we’d prefer to invest in only two robots, we can opt for less-than-optimal speed for one of the single-line orders by letting Robot 2 pick it up as it works on the multi-line order as well.
Example: Multiple adjacent order picks in one tote
When two orders contain items to be picked from the same Bay, we can use one tote to carry the two orders simultaneously by partitioning the tote into two compartments. This approach emphasizes picker efficiency with fewer totes and moves. Experience has shown that maximum optimization can be achieved when totes are split into two partitions.
Example: Splitting a large order across multiple totes
Let’s say an order comprises 32 lines to be picked from 32 locations. If all 32 picks can fit in a single tote, we could handle the order with one AMR. But the picking process can be speeded up dramatically by logically clustering items stocked in the same location and spreading the picking task across multiple robots. If, say, four totes are each dedicated to eight locations, it cuts pick time by a factor of four. After picking to four totes, the NextShift Shipper App scans the totes and consolidates the order.
Example: Multiple robots moving a tote through a multiple pick
An especially powerful NextShift advantage is the ability to have multiple robots participate in a single order, moving the order tote around the warehouse floor as needed, but never waiting idly for pickers to execute their picks.
For instance, when an order is received that needs to be picked from two Bays, three AMRs might be assigned to facilitate the pick. First, Robot 1 picks up an empty tote and delivers it to the first item’s location, then moves off to accomplish another transport task. Meanwhile, a picker scans and places two items from into Tote 1, then moves on to perform other picks. At this point the NextShift fleet control system sends Robot 2 to move the tote to the location of the next item in the order, drops off the tote, then proceeds to do other work. A picker adds more items to the tote. After the second picker is done, Fleet Control sends a Robot 3 to collect the tote holding the completed order for transportation to the pack/ship station.
NextShift optimization dynamically clusters AMRs in areas to reduce travel time and idle time, not in predetermined, fixed zones. Fleet Control automatically dispatches the optimal robot available each time the tote is ready to be moved.
Factors Used to Calculate ROI
- What is the order volume and the order profile? (mix of multi-line and single-line and order size)
- What is the SKU profile? (SKU diversity warrants the use of autonomous robots)
- How important is order processing speed? (impact on customer satisfaction and retention)
- How important is reducing labor costs associated with fulfilling orders?
- What is the cost of fulfillment labor? (base salary, benefits, availability of workers, cost to hire workers, cost to train workers, cost of worker retention) Look out over the next 5 years.
- How many workers are required for the existing process?
- How many shifts does the fulfillment center run? (Because robots can work 24×7, each robot reduces labor across all shifts)
- Pick face distance – how long and wide is the picking area? Larger areas have more opportunity for travel time reduction.
- Packing and Shipping distances. What is the distance to shipping/packing to deliver the order when picking is complete? Longer packing/shipping distances provide more opportunity for the robot to eliminate travel.