DC Velocity - September 2014

A big data analysis might look at information sources that are related but traditionally are examined independently. For example, lift truck, battery, and charger performance usually are reviewed separately. But a big data analysis that treats them as “a holistic system” will allow fleet managers to see patterns that would not be apparent otherwise, says Harold Vanasse, vice president of sales and marketing for Philadelphia Scientific, a provider of battery management technologies. Some of his customers match their battery usage and handling data with lift truck manufacturers’ data collection and analysis systems, such as InfoLink from Crown Equipment and i Warehouse from Raymond, Vanasse says. “They may look at changes in run times and utilization of batteries with our system, then look at the fleet’s performance. They can then match up the activity of a truck [powered by] a particular battery with that battery’s performance” to find out whether one is affecting the other, he explains.

Or, like the humidity example above, it may involve analyzing data sources that appear to be unrelated. Another example: An analysis of a Raymond customer’s maintenance and repair data showed that some trucks were suffering damage to drive wheels and tires, while others were not. A look at the damaged trucks’ daily activities found that they all had been driving over a malfunctioning dock plate. The DC’s managers were aware of the faulty plate and had planned to replace it when the next year’s facility maintenance budget was released. But because building maintenance and fleet maintenance had separate budgets, nobody knew until it was revealed by the analysis that driving over the dock plate was directly responsible for some $1,000 a month in truck repairs, Rosenberger says. Immediately replacing the dock plate would be more cost-effective than waiting for the following year’s budget to kick in.

In that particular case, the customer was
able to track down the problem because it
assigned drivers and trucks to specific dock
areas. But a company that does not fol-
low that approach could use information
from its warehouse management system
This type of analysis requires help
from technology. Although spread-
sheets and basic databases are useful
in collecting and sorting fleet oper-
ating and maintenance data, it can
be a cumbersome, slow process to
enter data from different sources,
sort it, visually identify patterns,
and then figure out the correla-
tions. Fleet and battery manage-
ment, maintenance tracking, and
asset tracking software—not just
those mentioned in this article but
also the many other programs that
are on the market—are designed to
gather, compare, and analyze data
from multiple sources. A big data
analysis requires a certain degree
of technological sophistication, so
fleet managers shouldn’t be reluc-
tant to ask for help. The lift truck
manufacturer, the software provid-
er, and in some cases, an outside
data management consultant or
an in-house systems analyst can
assist with identifying which data
are relevant, determining how best
to “harvest” it, and then conducting
an analysis.