DCs have struggled for years to recruit and retain workers, and as pandemic stresses finally begin to ebb in 2021, that old challenge is returning with a vengeance.
As for the source of the labor shortage, opinions vary. Some cite booming e-commerce demand, others point to an uptick in drug test failures, still others cite a wave of baby boomer retirements. But there’s one thing everyone can agree on: The problem’s not going away anytime soon. That’s led many e-commerce retailers and third-party logistics service providers (3PLs) to invest in robots and automated warehouse systems as a way to supplement their staffs and boost worker productivity.
Although that strategy can be effective, it also raises a new challenge: DC managers are having to re-evaluate how they use a foundational software tool: the labor management system (LMS).
Historically, LMS systems have compared workers’ performance to precisely defined “engineered labor standards,” which allowed managers to identify both their top performers (which they could then reward) and the laggards (those requiring additional coaching or, possibly, reassignment). But today, the definition of a warehouse job seems to change almost monthly, as workers learn to collaborate with cobots, interact with goods-to-person (GTP) systems, staff stations at an automated storage and retrieval system (AS/RS), or dispatch automated mobile robots (AMRs) to distant pick locations.
In response, logistics technology vendors are adjusting their software with an eye toward promoting more efficient interactions between robots and people.
One advocate of that approach is Dan Gilmore, chief marketing officer of Softeon,a Reston, Virginia-based supply chain software vendor. Although tracking workers’ performance is important, he says, an LMS can be much more effective when used “holistically”—that is, to evaluate workers’ performance not just in comparison to their peers, but also to the machines around them.
As for how that might work, consider the example of a case-picking worker who’s feeding an automated sortation system too slowly, preventing the machine from operating at full capacity. A traditional LMS analysis would miss that disconnect and, thus, fail to alert managers to the bottleneck. A more holistic analysis, by contrast, would identify the root cause so that managers could shift more workers to case picking and resolve the problem.
“You need to get your labor balance right at the designed efficiency, whether workers are using a parcel sorter, a goods-to-person system, an automated storage and retrieval system, or something else. You can’t just focus on the automation side as it is,” Gilmore says.
Providers of warehouse robots agree. The most efficient companies manage each warehouse as a whole, instead of focusing on automation and labor separately, says Lior Elazary, co-founder and CEO of warehouse automation specialist InVia Robotics.
“The goal is to find the best way to sort jobs so you have very few resources idle,” whether those resources are robots or people, he says. He adds that while InVia’s software was originally designed to keep its AMR fleet running efficiently, it can also be applied to human workers when LMS data are added into the mix.
“In fact, we now have several customers who are operating just our software, so they can plan how to deploy their people with the same algorithms as our robots,” Elazary says. “Everything has to flow in harmony. We don’t look at it as asking ‘Are you an LMS, a WMS, or a WES?’ You have to look at it more holistically, because it doesn’t all fit into one box.”
Users, too, are finding they get better performance from their DCs by integrating their LMS with other warehouse software, ensuring that all the systems are singing in the same key.
“We’ve gotten more into robotics and automation, but our job is still to help our [workers] be more efficient. They may have changing job functions, but we’re still measuring [their productivity],” says Kevin Stock, senior vice president of engineering at third-party logistics specialist Geodis,which deploys AMRs from Locus Roboticsin its fulfillment operations. “We’re using our LMS to measure job performance and set expectations, but we’re now integrating that with data feeds from robotic functions.”
Among other advantages, this allows for a more nuanced assessment of worker performance, he says. For example, an order picker might travel a different path around the warehouse when accompanied by a robot than when walking the aisles alone, he notes. But Geodis can now track the location of each Locus bot to determine the worker’s new path, which allows it to account for the shift in balance—a reduction in travel time and an increase in picking time—when measuring their productivity.
Likewise, in an operation that uses AMRs in a good-to-person workflow, a worker might not travel at all, but rather stand at a pick-and-pack station or a put wall. The company can still measure the performance of both the person and the automated system, making sure that neither human nor robot is waiting for the other.
“The additional data comes from our robotic systems vendors, because their WCS [warehouse control system] layer is integrated with our systems and is feeding data back in. It’s now a third part of the equation, with a robotic control system integrated with our WMS and our LMS,” Stock says.
Providing an LMS with this type of additional data can open the door to more creative ways of measuring worker productivity, Softeon’s Gilmore says. Instead of comparing individual worker performance with labor standards, employers can look at performance statistics by shift, which avoids the need to hire industrial engineers to conduct timed studies, he says. Under the shift-based approach, the LMS analyzes performance data by calculating the “standard deviation,” a measurement of the amount of change within a set of numbers.
“If a group of workers has a high standard deviation—which looks like a wide bell curve on a graph—then something’s not right. You’ve got to do some digging and figure out what’s going on, because that bell curve should be tight,” Gilmore says. “There’s a gap between the theoretical throughput of the DC as a whole—what you drew up on paper—and what is being observed and actually realized.
“Automation should allow you to get the same throughput with fewer workers,” Gilmore continues. “But how do you maximize and maintain that throughput? This is a form of digitizing a formerly manual process.”
As warehouse operators continue to explore new ways of using their LMS tools, they are unlocking new levels of productivity by ensuring that workers and robots are all singing from the same musical score. Both labor and automated systems are valuable commodities, and that approach helps ensure that neither one sits idle but instead, operates smoothly and harmoniously with its virtual colleagues.