FreeMove in a Collaborative Palletizing Case Study—Part 2
By Alberto Moel, Vice President Strategy and Partnerships, Veo Robotics
In our previous blog post, in collaboration with Advanced Robotics for Manufacturing, we presented a case study of the Veo FreeMoveTM system in a palletizing application. We focused mainly on the capital expenditures and commissioning metrics required to get four different palletizing solutions up and running—a fully manual palletizer, a fully robotic one, an approach using Power and Force Limited (PFL) robots, and one using the Veo FreeMoveTM system.
We concluded (surprise!) that the Veo solution is 40% less expensive to install than the other automated solutions while retaining a short process cycle time, a much shorter payback time, and quicker design, development, and implementation times.
But how do the economics of an operational Veo FreeMoveTM palletizer stack up? In particular, what kind of flexibility does the Veo approach enable, and how much is that flexibility actually “worth” to the end-user?
Ein Gedankenexperiment1
Let’s start with a thought experiment in which robotic palletizer solutions are able to work faultlessly across the cycle—a reach, I know, but stay with me—never a dropped box, a badly placed pick, a failed case erector spilling shampoo all over the palletizer, or a missed bumper step. In this fantasy there is no need for a fault recovery process, so the operating statistics would look like those modelled in Figure 1.
Figure 1. Operating statistics for faultless palletizer operation.
What does Figure 1 tell us?
- Labor costs are highest for the manual palletizer and account for the majority of its cost structure. The fully automated palletizer has no labor costs (it never fails, hence nothing to clean up!), while the FreeMoveTM and PFL solutions have some labor costs since part of the palletizing application (the bumper placing step) is done by humans.
- Due to lower capital expenditures, the Veo solution has lower depreciation than the fully automated solution (assuming a 10-year useful life of the robots and associated automation). The PFL solution takes a hit here, as it requires doubling down on robots and automation given its lower throughput and payload.
- Space utilization (as captured by rent and utilities) is lowest for the Veo solution and highest for the PFL approach.
- Of the three robotic palletizers, the Veo system retains the lowest cycle time (and corresponding highest throughput) because it uses automation (for pick and place) and humans (for the fiddly bumper placing step) selectively.
So all-in, if the palletizer never fails, using the Veo system provides cost savings over alternatives, while maintaining the highest performance, reducing the per-pallet cost from $0.81 to $0.65, and stacking an average of 115 pallets per shift.
OK, back to reality
The assumption that the palletizing step is always flawless and fault-free is, well, flawed. Dropped boxes, improperly erected boxes, bad bumper steps, failed pallet wrapping, and bad case placements are all facts of life in a palletizing cell.
Of course, increasing the fault rate from our zero-assumption Gedankenexperiment and accounting for recovery time adds costs to the palletizer, which continues to depreciate when not palletizing, so the per-pallet cost increases. If a fault occurs, the palletizer must be stopped and some aggrieved human has to step into the idle cell and fix the fault. And if the palletizer is caged and only accessible through a lock-out/tag-out mechanism—as it would need to be in a fully automated system—fixing the fault takes much longer and requires two humans—one to open and close the cage and another to correct the fault. This human labor also has cost implications.
Different factories have different levels of efficiency and attention to detail during fault recovery. So more generally, the impact of a faulty palletizer will be driven by two factors: how often faults happen and how quickly faults are recovered. Figure 2 compares the costs of fault recovery for the fully automated solution and the Veo FreeMoveTM approach, assuming (sensibly) that the fully automated palletizer is caged, while the Veo FreeMoveTM solution is easily accessible.
Figure 2. Per-pallet costs of fault recovery.
The blue line corresponds to the per-pallet cost of the fully robotic solution assuming a 10-minute fault recovery2 as a function of the number of faults per hour. The green line shows the same for the Veo solution assuming a one-minute fault recovery time.
As we can see, the impact of increasing faults is especially serious when recovery times are long, which is the case for fully automated, caged palletizers. As the number of faults per hour increases, so too do the robotic palletizer’s per-pallet costs. The Veo solution also sees some cost impact as the number of faults per hour increases, but it’s much more modest. Because the Veo approach is flexible and allows humans to work closely with the automated palletizer, it enables much quicker fault recovery (and fewer faults in the first place) and, therefore, reduces per-pallet costs relative to the fully automated solution.
But wait, there’s more!
In the model above, we’re looking purely at the incremental costs of fault recovery on the per-pallet economics. But every time the palletizer is down, it’s very likely that the rest of the production line is also stopped. The palletizer, when it faults, becomes a bottleneck for the rest of the system. If this is the case, the overall factory throughput is impacted and, depending on what you’re making, we could be talking big money.
As a proxy for how “big” that money could be, let’s consider the throughput of pallets per shift for a short recovery time (one minute) and a long recovery time (10 minutes) as a function of the number of faults per hour, as shown in Figure 3.
Figure 3. Pallet throughput as a function of fault frequency and recovery time.
Since both the fully automated palletizer and the Veo palletizer are “driven” off the same robot palletizer arm, their throughput charts look the same. The difference is that the Veo system’s flexibility allows for fault recoveries on the shorter end of the chart (closer to the one minute line) because the robot is not caged and humans can quickly and safely step in to correct faults without unduly impacting the rest of the production line. This means that although the number of pallets per shift declines as the number of faults per hour increases, the lost throughput is not as dramatic.
If we’re considering the fully automated palletizer, though, which has longer fault recovery times, the decline in number of pallets per shift is quite steep. What is the value of this lost throughput? Let’s assume that each pallet has (say) 48 detergent cases each containing four big detergent bottles, so 192 bottles per pallet. And let’s assume a price of $15 per bottle, so that’s $2,880 per pallet. With a 10% contribution margin, we’re looking at around $300 of lost profit per pallet per shift. If faults stop the rest of the line and cost us time so that we’re making (say) five fewer pallets per shift, that’s a hefty $1,500 of lost profit per shift, or almost $5,000 per day. Reducing the frequency and duration of faults certainly looks like a priority.
A flexible, collaborative robotic system like Veo FreeMoveTM not only materially reduces the costs of frequent, time-consuming fault recoveries common to fully automated solutions, but also offers an opportunity to enhance overall plant throughput and capacity. Like we discussed in Part 1 of this case study, the Veo solution enables humans and robots to each do the things they’re best at, which means the FreeMoveTM palletizer solution allows palletizing to happen more efficiently. So in sum (at a high level), Veo FreeMoveTM requires lower costs and time to implement, reduces the impact of faults (both on per-pallet costs and throughput volume), and increases productivity. Of the four palletizing solutions analyzed, FreeMoveTM is by far the most economical.
But wait, there’s even more: Veo’s hybrid system is flexible, and that means the palletizing process can easily be reconfigured to account for changes in the items that need to be palletized. We’ll explore this benefit next. To be continued!
1 Obligatory big word per blog post
2 These 10 frantic minutes consist of two people working together to stop the system, find the person with the key (maybe they’re in a different building!), open the cage door, reset the fault, exit the cage, verify that no one is in the cage, lock the door, write the fault up in the logbook, and then restart the system.