Big Data & IoT
For example, on a sandwich biscuit
production line, the biscuit sandwiching
machine at the heart of the line should
be able to communicate with the
previous elements of the process,
as well as the ones that come after
it. The mixing, cutting and baking
machines at the very start of the
production process should also be
able to ‘speak’ to the conveyers, the
pile packing sandwich machine, the
cream feed system, lane multiplication
and packaging machines. This level of
communication allows the production
line to be more flexible and cater for a
wider range of biscuit varieties.
Regardless of whether we’re
talking about biscuits, automotive
manufacturing or even smart grids,
IIoT has communication requirements
that go beyond the standard client/
server needs and conventional
thinking. Instead, the nodes act as
peers in a network, each making
decisions and reporting to other nodes.
Besides performing core tasks, the
production system is also connected
to an enterprise level that can
automatically issue alarms, collect and
analyse data and even make predictions
or recommendations based on this
analysis. Of course, this mass influx
of data is not without its challenges.
Big Data flexibility
The increased amount of sensors on
the factory floor facilitates a complex
level of machine communication, having
the potential to improve operational
productivity and efficiency. However,
even greater value can be derived
from unlocking the valuable datasets
an IIoT enabled facility generates.
Research published by Accenture
and General Electric stated that
nearly three quarters of companies
are investing 20 per cent of their
technology budget into Big Data
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analytics. Exploiting data from IIoT
is a key part of the analysis plans of
those organisations.
The introduction of Big Data is
often regarded as a significant financial
investment, but the reality is companies
don’t need to fear this change. In
fact, setting up a Big Data platform to
leverage IIoT can be done without the
need for a large upfront investment and
often starts paying off straight away.
Some organisations may choose
to begin by implementing their
Big Data strategy with a small
proof of concept, to gain insight
of which data combinations are
valuable, before scaling to a broader
enterprise solution. Increasingly,
companies are also turning to cloud
based approaches, as they provide
greater flexibility to scale up and
down as the business requirements
evolve and change.
Regardless of the approach,
organisations must consider exactly how
the data can derive actionable insights
and in turn, benefit the business.
A common language
IIoT will only work if it uses a
compatible language across systems
and industries. To help achieve this
objective, industry giants AT&T,
Cisco, General Electric, IBM and
Intel founded the Industrial Internet
Consortium in 2014. The Consortium
aims to accelerate the development
and adoption of interconnected
machines and intelligent analytics.
As IIoT cuts across all industry
sectors, from manufacturing
to energy, common standards,
harmonised interfaces and
languages are crucial for successful
implementation of the concept.
The consortium hopes to lower the
entry barriers to IIoT by creating a
favourable ecosystem that promotes
collaboration and innovation. The next
step is to facilitate interoperability and
open standards, allowing machines
or systems from different original
equipment manufacturers (OEMs)
to communicate with each other and
with control systems.
The old and the new
Perhaps one of the biggest challenges
when it comes to implementing IIoT on
a larger scale comes from integrating
legacy systems with the latest
generation of smart factory equipment.
Learning new things changes the
structure of the brain and similarly,
in manufacturing, implementing new
automation equipment usually results
in changes across the entire system.
The solution is to use standards based
protocol gateways to integrate legacy
systems in brownfield environments.
This allows organisations to free data
from proprietary constraints and use
it for real time and historical data
collection and analysis.
There is as much risk in sticking
to a single vendor based on current
install base as there is to accepting
these new concepts with multiple new
vendors and interoperability between
intelligent devices. Their concept is
something that we have experienced
greatly within the energy and
infrastructure sector and the concepts
behind IEC61850 and interoperability.
Much like the human brain, the
Industrial Internet of Things is always
changing and there are still a lot of
questions to be answered before we
fully understand its requirements,
implementation and potential. Luckily,
these conversations are taking place
and new ideas are put into practice
every day. The next step is to figure out
an easy way of practically implementing
IIoT innovations in manufacturing
environments across the world.