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5 levels of IOT intelligence
There is a seismic shift under way in the engineering industries. The decreased
cost of sensors, the increased amount of instrumentation on assets and need
for new revenue streams are forcing engineering firms to re-imagine business
models. The fusion of “atoms with bytes” promises to unlock new value
previously unrecognised which generate additional revenue streams predicated
on intelligence generated from the data. As machines increasingly become
nodes in a vast array of industrial network, value is shifting towards the
intelligence which controls machines. Intelligent Platformization of machines
has begun
Keeping in mind this fundamental shift in value from atoms to intelligence,
Flutura has defined 5 levels of maturity to assess the machine intelligence
quotient of an engineering organisation. The highest level of maturity is
"Facebook of machines" with ubiquitous sensor connectivity and the lowest is
an asset which is "unplugged" where the device is offline. As organisations
embark on a journey to intensify the intelligence layer in their IOT offering it
makes sense to map where they are in their current state of maturity.
The 5 levels of machine intelligence with specific illustrative examples are
outlined below
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organisation. A vast
majority of engineering
firms manufacture
assets which fall into
this category. For
example a vast variety
of industrial pumps
still are completely
mechanical devices
with no sensors to
instrument them.
This is the lowest level in
the maturity in the
maturity map. At this
level of maturity, the
device or sensor is
'unplugged' from the
network. There are no
“eyes” to see the state of
the machines at any point
in time.The machine is
offline to engineering 3

This is the next level of
machine intelligence
which exists in the
maturity curve. At this
level of intelligence the
device is connected to
the network. There is
also rudimentary
intelligence exists on the
device to take corrective
healing action. Examples
of assets having edge
intelligence include cars
which can alert the
drivers to basic
conditions which need
intervention. Other
examples include a boiler
which has edge
intelligence to switch
on/switch off valves
based on steam pressure
4

At this stage the device can be remotely
monitored and monitored from a central
command centre network. For example Flutura
was working with an asset service provider who
was monitoring the health of connected
buildings geographically dispersed and
monitored in real time. This requires the ability
of the platform to ingest billions of events from
boilers, chillers, alarms etc. in real time and
make sense of which assets need intervention
from the command centre and which assets are
healthy.
5

This is taking the intimate understanding of
assets to the next level. This involves triangulating
patterns from historical asset data, its ambient
conditions etc. to predict failures, defects etc. At
this stage, there is enough causal knowledge
available to model when the device would break
down and proactively trigger an intervention be it a
field visit or a part replacement.
6

This is the most evolved state of
engineering intelligence where all
assets the organisation has
deployed is connected in real
time seamlessly to field force,
head office engineers and
command centre
observers in real time. Very few
of global engineering firms are at
this level of maturity.
7

As business models evolve driven by
pervasive hyper connectivity of
devices across industries like
Utility, energy, Oil n Gas, Intelligent
building management systems etc,
competitive advantage will shift
towards differentiated value adding
intelligence platforms. Flutura
intends to leverage its Cerebra
Signal Studio Platform
to accelerate signal detection and
deliver value added business
outcomes.
8

www.flutura.com linkedin.com/company/flutura
Blog.fluturasolutions.com @fluturads 9

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5 levels of iot intelligence

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5 levels of iot intelligence