In the digital transformation realm of manufacturing, Industrial Internet of Things (IIoT) is the number one technology. According to Gartners’
Hype Cycle IoT is somewhere in or just clawing out of the trough of disillusionment. As so often happens, IIoT has too often been seen as a great technology innovation that is looking for a problem. Now that we’re starting to have a better perspective, we see how IIoT can solve real business problems and understand how money can be made.
New Business Models
Many industrial organizations are using IIoT to improve
the health and performance of their existing assets and driving new operational
insights that enable them to boost productivity. Well known examples are
manyfold: reducing downtime of machines, performing asset tracking, increasing
first pass yield and reducing waste to name a few. The business impact of these
initiatives is important. According to a Siemens Mindsphere study, a reported
reduction of downtime by 10% thru predictive analytics on robots.
The value that IIoT can bring above and beyond these
direct business benefits is also becoming better understood. Improving worker
wellbeing, increasing operator safety and growing engagement and motivation —
all increase indirectly revenues. This is also becoming a transformative
ability for the whole organization.
IIoT’s biggest opportunity, however, is connecting things with new business models.
Think about the field maintenance engineer who uses
augmented reality (AR) to diagnose machinery equipped with IIoT devices and who
is guided by a home-based expert using a digital representation. Or, think
about outfitting a machine with built-in sensors to market that machine as a
service rather than a product. A service allows the owner to monitor the
machine remotely and deliver maintenance, repairs and upgrades automatically.
Instead of worrying about the condition of the equipment, it allows clients
(manufacturing companies) to focus on the work at hand.
PTC reports that equipment manufacturers are
digitally transforming their customer relationships through IIoT. They’re moving
beyond selling a machine to providing ongoing value throughout the life of
their product. Digitally transforming service lowers costs and drives more
recurrent service revenue — selling outcomes by shifting to a predictive
A Collaborative Effort Across The Whole Organization
Given the huge amount of assets as well as data points
per asset, a strong convergence between IIoT and AI is needed to ingest all
that data into intelligent decision systems performing analytics. Having
real-time visibility of all the assets allows to monitor, analyze, predict and
prescribe the full production environment. An end-to-end digital representation
(or digital twin) provides a holistic view of not only a full assembly line,
but also a complete manufacturing plant. Not only can you predict when to
perform maintenance, but also to optimize flows in supply chains, etc. The
point is to predict when to take action in the real world. But it also allows
the introduction of new capabilities, new processes, new products and new
business models that you can test out in the virtual world of the digital twin
before moving it into the real world.
These advantages however form the biggest hurdle:
integration across the various parts of the organization, building the right organization
mindset, finding the right talent, creating the right KPIs to measure success,
and the lack of infrastructure to underpin it all. So how do you go about this?
IIoT is a Transformative Effort
IIoT is a profound transformation that touches upon business,
people and technology. Because it is so profound, one needs to think big but
start small. Start with one business problem, with one problem on one piece of
machine equipment, even if the company has multiple large plants. Don’t design
from the top but walk the shop floor and talk to the operators. Determine which
problem is a real pain point for operators; that has a major business impact.
This needs to be driven from a business strategy viewpoint (Gartner).
Technology is not often the challenge, rather the business strategy and the organizational capability are.
Ask the impacted operators to participate in a
multi-disciplinary group to design a solution, if relevant, an IIoT based
solution. As the technology is likely new, ensure that employees and operators
are educated in the new technology in terms of its capabilities and how it can
solve problems. Assess how this new solution will affect your current operation
both asset and people-wise. Define metrics to measure impact of the solution
proving that an IIoT solution is the way to go. These are not just your typical
increases in efficiency and/or productivity or overall impact on workforce. The
learning aspects are even more important in order to build momentum in your
Lean startup and agile techniques need to be taken with a grain of salt too.
One cannot move fast without breaking things in
manufacturing. Agile innovation pilot projects could put sustainable operations
at risk. Therefore, a collaborative business-driven approach is paramount —
because if not, operations experience will eat IoT optimism for lunch.
Once the solution is validated you can replicate it
across other similar assets and start experimentation on other problems. This
scaling and further experimentation needs to continue along the same pattern of
business, people, technology. Obviously architecting a solution needs to
encompass the capability to operate at scale and requires the buy-in of the
Not following this path will likely result in failure:
overspending because tackling a too big project, frustrating employees because
not involved, demotivating business folks as not solving real problems, keeping
data in organizational silos.
IIoT implementations will succeed if they: address a
business problem, directly involve impacted people, are driven by executives
who really understand factory problems and not only look at numbers on their
dashboards, have a mindset of exploring solutions for real problems that not
always have immediate returns and don’t just rely on standard implementation
packages because every project is unique.
About the Author: Thierry Van Landegem is Executive Director of
the IIoT Accelerator at mHUB, the hardtech innovation center based in