/
The triple scale design enables significantly shorter product
gaps with transport speeds of up to 3. 4 m/s (670 ft/min) and
a 50 ms measuring time. EMFR technology and Active Vibration
Compensation delivers superior weighing accuracy.
Visit us at MODEX, Booth 9219.
SHORTER PRODUCT GAPS
SIX SCALES COMBINED IN
ONE WEIGHING UNIT
Catchweigher
HC-FL-T
wipotec-ocs.com
to maximize the efficiency of its fulfillment
process. The information collected at the
evaluation stage feeds all of those efforts,
Kumar says.
Companies should also factor in their
short- and long-term objectives. For example, is e-commerce a small but
growing portion of the business?
How quickly are you expecting to
ramp up e-commerce sales? Also
consider how seasonal peaks affect
your fulfillment process; this is
especially important in developing training programs that can get
temporary employees up to speed
with equipment and processes as
quickly as possible.
“[You need to] understand all those different things as well,” Kumar says.
On an even more fundamental level,
what are the larger, “big picture” goals the
company is trying to achieve? For some
businesses, minimizing operating costs
and/or capital investments is most important. For others, maximizing throughput
capacity while maintaining the best service
level may outweigh the high cost of investing in advanced automated equipment and
systems. And for many companies, doing
all of these things simultaneously may be
the ultimate goal—creating a need to strike
a balance between competing objectives,
Kumar adds.
Gathering and processing data is a key
part of providing analytics solutions,
adds Arnaud Morvan, senior engagement
director for Aera Technology, which uses
machine learning and artificial intelligence
(AI) to develop cognitive automation soft-
ware solutions for supply chain operations.
Aera works with large brands in the con-
sumer packaged goods (CPG), pharmaceu-
tical, and medical-device industries, among
others, and counts Johnson & Johnson,
Merck, and Unilever among its custom-
ers. Morvan says the data-collection phase
consists of gathering information from a
company’s various IT systems—enterprise
resource planning (ERP), warehouse man-
agement software (WMS), and transpor-
tation management software (TMS), for
example—and analyzing it to understand
patterns and business performance. In
Aera’s case, combining analytics, AI, and
process modeling allows the firm to deploy
agement and operations personnel
should answer a series of questions
regarding the type and variety of
orders the company receives (large
retail or wholesale orders, e-com-
merce orders, or a combination),
the size of items being han-
dled (can a human pick
it up?), how those orders
are picked (batch, wave, or
zone; manually, automati-
cally, or some combination
of the two), and how they
are packaged and shipped.
Answers to these questions can rule out many
design options, according
to Kumar.
“We need to collect data and
look at the [customer’s] pro-
file to see what [its] warehouse
should look like,” Kumar explains.
“Understanding the profile will
make you go one way or the other.”
Kumar points to storage require-
ments as an example. A warehouse
that primarily ships pallets of a
particular high-demand item will
require a large storage area to
accommodate the pallets. In com-
parison, a fashion retailer may have
a large inventory that contains a
limited number of each particular
item, requiring a more segment-
ed approach to storage. Each will
require a different combination of
material handling equipment and
technology as well as the develop-
ment of a tailored slotting strategy
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