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
Level-1: UNPLUGGED
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 the engineering
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
Level-2: EDGE INTELLIGENCE
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
Level-3: REMOTE MONITORING
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.
Level-4: PREDICTIVE
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.
Level-5 : "FACEBOOK OF MACHINES"
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.
Closing thoughts
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.
