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companies like Witron and Schaefer are able
to build pallets to a planogram (a diagram of
a store’s interior indicating where products
are located). That is, they add cases to pallets
in the reverse order they are needed in the
store aisle. That way, he explains, a worker in
the store can unload from the top down
while moving down the aisle, eliminating
backroom sortation and thus, labor.
In addition, Starovasnik says, the pallets
are built so as to reduce damage and make
the best use of cube in transportation, further reducing those costs. To do that, he
says, the robotic system not only has to
“understand” how to build a square pallet
with various-sized cartons, but it must do
so in a way that accounts for carton weight
(with heavy cases on the bottom) and such
factors as isolating food from hazardous
materials. In addition, as it builds the pallet, the robot must sense any earlier cartons
that might be in its path—it must work in
three dimensions. These are expensive systems, he says, but they provide considerable
savings downstream.
The key to making all this work is data,
says Mike Khodl, vice president of solutions development for systems supplier and
integrator Dematic. “It is absolutely critical
that the data repository—whether an
enterprise resource planning (ERP) system
or a warehouse management or warehouse
control system—has knowledge of every
case, including its physical footprint,
weight, and crushability,” he says. “Those
attributes—and several others—have to be
put into a cubing algorithm driven by software on how to put the pallet together.” He
compares the process to doing a Tetris puzzle at high speed.
WHAT’S AHEAD?
The recent advances in palletizing are just
the forefront of what robots will be able to
do in DCs in the years to come. Case and
piece picking, taking over tasks in hazardous environments, and even packaging
applications are all possibilities.
“One day, we could see a robot mosey on
down into the pick aisle,” Wohlrab muses.
“That could be years from now, but we’re
not all that far away from some things.” He
says, for instance, that Intelligrated is look-
ing at robots that could unload floor-loaded
trucks. That technology could be as close as
12 to 24 months away, he says.
Nonetheless, Wohlrab acknowl-
edges that the technology still has a
ways to go. “We are trying to shoe-
horn in software from the manufac-
turing side,” he says. He adds that
costs need to fall further and that
tools for programming robots need
to improve before robots make
greater inroads into DCs. But he
expects that to happen.
Tymensky speculates that as soft-
ware matures and competition
among robot manufacturers
increases, costs may drop in ways
that make robots feasible for lower-
volume operations.
James Bowes, Peach State’s presi-
dent and CEO, is of the same mind.
“As the technology moves from the
early adopter phase and the control
systems become more dependable
and the cost of labor continues to
grow, the [technology] becomes
much more affordable and attrac-
tive,” he says.
As for what’s ahead, Khodl says,
“If I were to paint a picture of the
future, we would see item-level
picking with robotics come to some
form of reality. I could see robotics
replacing labor in goods-to-person
picking operations.”
To get there will require further
breakthroughs in vision systems,
Bowes says, and will likely apply
first to operations with a high vol-
ume of similar goods. But he
expects the problem will be solved.
The potential for robots in DCs is
limited only by the imagination of
developers and end users. “I can envision robots involved in every step
from picking through cutting a cus-tom-sized carton, packaging, and
labeling,” Tymensky says. “If we get to
the far end of the spectrum, we might
even see them doing gift wrapping.
It’s a repetitive process. The potential
is kind of unlimited.” ;
