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routes are untenable” because of guidepath infrastructure
limitations.
The market could soon have greater clarity on the nav-
igation question, he said. Today’s warehouse operators
are being squeezed by multiple market forces, including a
DC labor shortage; the challenges of filling small,
multiple-SKU (stock-keeping unit) orders; and
shorter delivery times demanded by e-commerce
customers, he noted. In an effort to address those
pain points, companies are using whatever tech-
nology can produce the quickest results. “There’s
a substantial installed base of AGVs and people
will continue to run them maybe until they go into
the ground,” Christensen said. “But for companies
looking to make a decision today, picking some-
thing with fixed guidance is nine times out of 10
not the right choice.”
AGV vendor and systems integrator Knapp
AG sees many of the same trends playing out,
according to Kevin Reader, the company’s vice president
of business development and marketing. In response, the
company has introduced AGVs whose capabilities extend
far beyond following fixed paths, he noted. For example,
Knapp’s current family of “Open Shuttles” can dynamically
sense obstacles in their path and communicate with other
AGVs, Reader said.
In the end, he said, vehicle choice isn’t just about the best
way to automate a single process. It requires a more holistic
view of the workflow. “You have to look at [vehicles] in the
context of the whole operation, and then calculate the cost
per order or cost per case or cost per tote, depending on your operation,” Reader said.
To that point, he added that regardless of the
type of vehicle you pick, the greatest gains are likely to come from combining the automated equipment with software-based approaches to warehousing distribution. Today’s DCs, he noted, are
poised to start reaping big benefits from tools like
predictive modeling, analytics, big data, actionable
insights, Internet of Things-enabled predictive
maintenance, bottleneck detection, and AI.
EVERY INSTRUMENT PLAYS ITS PART
When it comes to vehicle choice, it may not necessarily be an “either-or” question. Different approaches each
have their own benefits, says Melonee Wise, CEO of AMR
vendor Fetch Robotics, a fast-growing firm that recently landed a deal with industrial heavyweight Honeywell
International Inc. to supply its AMRs for e-commerce DCs.
According to Wise, the fast-growing AMR sector has produced a range of distinct vehicle designs. Some AMRs are
engineered exclusively for order picking, essentially turning
the DC into a virtual AS/RS by providing mobile access to
static inventory. Others support more varied applications,
including tasks associated with processes like forward picking, reverse logistics, and manufacturing.
Given the wide range of potential applications, these
AMRs don’t even compete directly with each other, much
less with existing automated platforms. “Just because we
now have AMRs, do you think AS/RSs are going away? I
don’t,” Wise said.
The key challenge for customers is to pick the right robot-
ic technology for the problem they’re trying to solve, she
said. For example, it would be a waste of resources to dedi-
cate a fast-moving robot to a rack of seldom-needed goods
because the AMR would sit idle much of the time awaiting
a call. “Imagine if Amazon put slow-moving goods in a
case with a Kiva?” Wise asked, referring to the squat orange
robots used in Amazon.com’s DCs to ferry products to
order pickers. “You’d have a really expensive, million-dol-
lar battery-filled paperweight!”
AMRs may have made a flashy debut on the self-driving
vehicle scene in recent months, but AGVs are still the king
of the prom, if popularity is measured by installed base and
total miles driven. Only time will tell whether there’s room
for both types of driverless vehicles in the logistics universe.
But experts agree that they show great promise for solving
some of today’s most intractable logistics challenges, as busi-
ness pressures and new technologies continue to drive the
development of intelligent, flexible self-driving platforms.
