June 2018 | VehicleServicePros.com 31
to make decisions, it will be challenging to keep
up with the amount of information. But change
will be pervasive.
So how can a fl eet separate the claims from
reality, especially when technology is changing
The truth about data
In its simplest form, data is just values in some
format representing transactions, readings and
observations. Sensors on the vehicle record these
readings and observations, which can be used to
calculate diff erent metrics. For instance, a fuel
consumption sensor can provide metrics such
as gallons per hour, or gallons per mile.
Other examples of data include the GPS location
at a particular time, vehicle speed and
engine temperature. Even work orders and
customer load information are all considered
data. We are now collecting data from a variety
of diff erent sources.
While terms like “Big Data” have been used the
last few years, the information is nothing new. It
is the access to this data, and how it’s used, that
has been evolving.
When it comes to fl eet operations, real-time data
– coming from systems like the CMMS, on-board
sensors, dispatch and order entry, and GPS data –
can help to improve the decision-making process.
Th e operational advantage can be signifi cant, and
in some instances also provide benefi ts to the
A few examples of how real-time data can
improve vehicle maintenance include:
Ability to schedule PMs when they will impact
the delivery cycle the least. Th is helps to
improve demand hours.
Shop scheduling will be more complete and
reliable with fewer disruptions
Catastrophic events will fall dramatically
because some of those events broadcast themselves
from the sensors as soon as there is a
concern (like oil or coolant issues).
Rebuilds will be better scheduled and only the
worn parts will be replaced.
Th e list can go on, but the idea that better information
in the hands of the people who can use it
supports intelligent decision-making.
Data for decision-making
One of the main challenges of utilizing data for
better decision-making is access to and effi cient
analysis of this information.
In an ideal world, all the data generated by the
truck is collected and sent to the cloud for storage.
A cloud-based soft ware program performs
the analytics and provides suggestions for what
to do and how soon to do it.
But in the real world, there is information in
myriad diff erent places or programs, including
everything from the line setting ticket, to the
VMRS codes for all prior repairs, open work orders,
etc. Not to mention factors such as seasonality,
geographic location, topography and weather.
With the data available from many sources, it is
critical to fi nd a way to combine this information
into new ways for a deeper look at what is going
on with the vehicle.
Comparisons between U.S.
military vehicle and commercial
By Tim Bigwood,
Fleets increasingly benefit
from the data produced by
their vehicles to optimize
maintenance plans and
improve uptime. While
vehicles have long produced
this data, it has recently
become more feasible for
fleets to have the hardware,
software or capabilities
to transform the data into
Both commercial fleets and
the U.S. military have a common
goal of maximizing vehicle
uptime, and have looked to
utilizing data for maintenance
procedures in order to achieve
this. However, both industries
have different motivators
and maintenance practices
to meet the common goal of
maximized uptime. For the
U.S. military, vehicle uptime
directly reflects a vehicle’s
mission-ready status. When a
vehicle is not mission-ready,
human lives are at stake.
The U.S. military needs an
extremely high degree of
confidence in a vehicle’s ability
to complete its mission before
dispatching that vehicle.
Whereas the military is mission
driven, fleets in the commercial
industry are business
driven. Vehicle uptime affects
an organization’s ability to
generate revenue and remain
profitable. Additionally, there
is a need to implement maintenance
practices that do
not affect warranty statuses;
also influencing profits.
Noregon’s experience with
both customer types gives
the company an understanding
from different viewpoints.
Fleets are using their vehicle
data to improve maintenance
procedures by learning how
their vehicles are driven.
Access to data detailing driving
tendencies, such as hard
braking, acceleration speeds,
engine load and fuel economy,
as well as environmental
factors such as operating temperatures
and terrain, provide
clarity to the vehicle’s operating
conditions, which helps
optimize maintenance interval
the number of emergency
repairs, extends the life of
components and helps satisfy
needs with warranty claims.
These benefits all directly
reflect the business mindset
of commercial vehicle fleets.
Given the increased risks
associated with unexpected
downtime, the U.S. military
takes additional measures to
implement advanced practices
such as condition-based
maintenance plus (CBM+).
The basic idea of CBM+ is to
follow maintenance practices
determined by algorithms
to predict a failure before
it happens, as opposed
to maintenance based on
intervals or an as-needed
basis. In other words, taking
a proactive versus a reactive
The military is more predictive
in their maintenance
decisions. It is understandably
more difficult to
convince a fleet to replace
a component showing no
signs of failure than it is the
military when an algorithm
predicts a future failure.
For development and
improvement of maintenance
programs, fleets have a
major advantage over the
U.S. military in regards to the
amount of data their vehicles
produce, plus ease of access
to that data. The average
commercial vehicle drives
exponentially more miles
per year, yielding more data
to learn from and enhance
The Noregon data analytics
team often fields requests to
review fleet data and uncover
opportunities for improving
fleet operations. By pairing
vehicle data with maintenance
records, fleets can detect
which vehicle configurations
are producing the best fuel
economy, for example.
Fleets will continue optimizing
by utilizing learnings from
vehicle data. When performing
it makes sense that fleets
may not aim for the same
level of predictive analytics
used by the U.S. military.
As rapidly as vehicles are
changing, what fleets learn
from data today may be
outdated within a year. And,
solving a problem upstream
may have ripple effects, which
may change the problem set.
By routinely analyzing data
and improving maintenance
practices, fleets can be sure to
implement the best possible
programs for their current
» The military is more
predictive in their
Photo courtesy of Noregon