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6 iot cases
- 2. 6 Families of IOT Use Cases
What are real world examples of IOT Big Data Use Cases impacting
Biz Outcomes ? Flutura keenly read the market signals from Houston,
Tokyo + European markets and more specifically in the energy +
engineering industries we are focussed on . After analyzing the data
accrued over the last 24 months we have distilled it down to 6
practical IOT big data use case families .
We have heard a lot of marketing hype around IOT and Big data.At the
end of the day it boils down to
ONE POWERFUL UNANSWERED QUESTION
What are some of the measurable outcomes impacted by IOT use
cases ?
So here they come ...
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- 3. This is the holy grail of IOT and Big data. While most IOT big data use
cases focus on cost optimisation, there are a couple of interesting use
cases which we encountered which activated new revenue streams by
tweaking the business model in the market place.
For example in the Oil and Gas industry Flutura encountered
organisations creating new revenue streams by monitoring real time
situational awareness of digitised wells as a paid value added service.
Flutura helped a provider of Smart city solutions to monitor 11000
government owned buildings in real time by watching their boiler, chiller,
fire alarm and other ambient data in real time where the pricing model
was per device.
In the deregulated REP markets, Flutura has encountered REPs offering
newvalue added offerings like energy audits and asset refinancing for
commercial industrial customers which are powered by smart meter
based big data products
6 Families of IOT Use Cases
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- 4. Energy consumption is a
very important lever which
drives profitability in the
industrial setup. Surgically
targeting this business
outcome can really make
the business case for IOT
based big data solutions
as we now have the ability
to mine granular state
information and correlate
it to energy outcomes
Another project we executed which
was in the utility industry involved
reducing average peak
power consumed by residential
customers by analysing smart
meter data across millions of
households gathered at 15 minute
intervals
This pattern seems to be repetitive
in multiple industry context
where a single digit change in
energy/fuel efficiency has a 6
figure impact on savings
For example we recently
executed an engagement in
Houston based fleet
provider where we reduced
average fuel consumption by 2
% unlocking 65 million dollars in
savings per year. We did this by
digging deep into signals buried
in operating sensor event
streams and location related
data which was then correlated
to the fuel consumption data.
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- 5. We worked with a leading
Spanish wind power generation
company recently who gave us
data regarding the performance
characteristics of 2 turbines
like main shaft rpm, grid
voltage, slide ring temperature,
reactive power, and rotor
characteristics at second level.
We unleashed Machine learning
algorithms on terabytes of time
series data a Hadoop cluster
and successfully extracted
signals emitted prior to a
turbine breakdown
A US Engineering giant gave us
data from Electrical trip unit
data (48 Samples/Cycle and 4
Cycles prior and post events
along with power data –
voltage, current , frequency )
which was then analysed to
spot significant differences in
trip unit calibration which
compromised asset integrity
“Smelling” asset signals is
again a recurring pattern
encountered across industries
Industrial industries are
asset intensive. For every
minute an asset is down
millions of dollars are
lost. Reducing down time
of certain nodal assets is
a use case which has a
business case. Let’s take
2 examples we executed
in this area to illustrate
this theme
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- 6. In process and discrete
manufacturing industries, it’s
very important to keep
defects below a certain
threshold. A new set of
possibilities is being enabled
from granular data collected
from digital factories. It is
the ability to dig deep into
second level sensor data to
understand specifics of
process states which
increased defect density.
Let’s quickly take an example
of a project we executed for
a US based electrical product
manufacturer.
The product underwent a
variety of operations and at
each step of the operations
sensors were monitoring
viscosity, humidity,
temperature data at second
level and streaming it to
SCADA based Historians. We
were able to spot patterns
using advanced machine
learning techniques on
a Spark based
architecture which resulted
in a 8 % reduction in defect
density for the product
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- 7. As the grid gets increasingly intertwined into all
the assets, security becomes a very important
consideration. Security forensics using granular
event data can help investigators analyse
sequence patterns exhibited prior to an adverse
event happening. These digital signatures can
be codified into a knowledge bank and watched
in real time. From Flutura’s experience this is
still in concept stage and pilots are being
executed to solidify the analytical workflows
which help a IOT security outcome. We will be
happy to share some solid security use cases
in the next 6 months
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- 8. Pricing innovations using analytics is another area where we are
seeing some powerful use cases blossom.
For example, in the energy value chain trading happens in Energy
exchanges and there is a lot of data generated from trades,
competitive pricing information which needs to be mined to
understand volatility patterns and to help time the market. Flutura’s
energy data scientist are helping a major energy trade see those
invisible pricing patterns to optimise millions of dollars by timing
the market well by analysing past correlations
Another use case which is finding momentum is dynamic asset
pricing. Assets which are leased to customers (for example golf
carts are equipped with sensors which record location, topple
events, average cart speed, start, stop events). By analysing the
granular usage statistics , asset leasing companies are able to
have dynamic pricing of assets 8
©Fluturasolutions2015
- 9. We do agree that the marketing machines have been on steroids
advocating IOT and Big Data as the panacea for everything. Industrial
mindsets focus on tangible outcomes and some of the frequent
questions we hear are Are they just fads or is it real ? Where is
the business case for IOT big data in Engineering industries ? What
are the use cases which are real for the Oil n Gas industry ? How can
Retail energy providers and energy trading firms benefit from IOT and
big data ? While a lot has been done on buzz word introduction, we
felt the best way to make the case was to highlight tangible
business outcomes which are tangible and "show them the money"
As a startup we have been blessed as customers trusted us to
unlock possibilities in their machine data and convert them into
dollars. We hope you found the use case taxonomy useful and would
love to hear your experiences from the trenches.
As we say in Flutura
May the big data renaissance awake every industrial
organisation
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