PREVENTIVE ACTION
Big data analysis and correlation is
not always about solving problems.
It can also be an effective tool for
improving current practices. For
example, previously established time standards
may indicate that a
certain number of
order pickers are
needed for a particular shift. But
correlating WMS
data (what needed to be accomplished) with lift
truck telematics
(how long it actually took) over time may
show that the standards in a
labor management system are no
longer accurate, Tenney says.
Integrating data from different
data sources can be useful for predicting the future, too. One I.D.
Systems customer, a large consumer products supplier to a Fortune 10
company, worked backward from
significant repair events to identify
patterns in the types of activities
that occurred prior to those repairs.
“It allows you to say, for example,
that when these four things happen, three months later, this problem happens,” Tenney explains.
Because the customer was able to
identify the common thread among
unrelated events, it is now able to
take action before a major failure
occurs.
Applying big data analysis to lift
truck fleet management is neither
easy nor simple. It also takes time,
since any analysis must consider large quantities of data over a
lengthy period to find and validate
patterns. But as the examples in this
article show, the payoff in terms
of problem solving or prevention
could make it well worth the effort.N
section of a warehouse, Rosenberger notes.
A WMS can be an invaluable source
of information for this type of analysis.
One of Philadelphia Scientific’s customers,
for instance, was experiencing a reduction in the number of picks
per hour. Around the same
time, managers noticed
that drivers were changing batteries more frequently than would have
been expected. Using its
WMS, the company saw
a correlation between
the frequency of battery
changes and reduction
in hourly picks. The problem, it turned out, was that
operators, who were paid by
the piece, wanted to make the quick-est possible change and get back out on
the floor. As a result, some would grab the
closest battery rather than ones that were
fully charged and fully cooled down. The
batteries did not last a full shift, and drivers
lost time in the changing room. After getting rid of the older batteries and putting in
a battery-tracking system, the DC achieved
a 35-percent reduction in battery changes
while order picks per hour quickly rose,
Vanasse relates.
Tenney says some of I.D. Systems’ customers have analyzed fleet telematics and
maintenance data in concert with information from their labor management systems (LMS) and timekeeping modules like
a payroll log to track down productivi-ty-busters. One grocery distributor used
that approach to identify the source of performance variances among lift truck drivers. “Big data can be used very effectively
to identify who’s falling behind, including
looking at what are the four or five attributes that define an operator. Then you
can break that down into what he or she
is good or bad at,” he says. The point is
not to punish, but to “be able to look at
productivity from all viewpoints and angles
within how a job is done.” That analysis
allowed the customer to identify training
program enhancements that helped operators become more effective. Before long,
the grocery distributor increased throughput by 15 percent with the same operators
and vehicles, according to Tenney.
