AI-Enabled Solution is a Gamebreaker for Maintenance and More
Data is the great empowerer of the 21st century, and Hewlett Packard Enterprise’s new predictive AI tool gives
manufacturers the ability to not only fix machines before they break, but break open new sources of revenue.
by John Hitch
“Most of our enterprise custom- ers are struggling to even get started with AI,” reveals Beena
Ammanath. As the global vice president of
Artifcial Intelligence for HPE’s Pointnext, her
role is to help transform businesses via digital
solutions, but she says it hasn’t been easy
for most manufacturers.
Apart from the plethora of anecdotal evidence she can rattle off, studies such as Gart-ner’s 2018 CIO Agenda Survey indicate it’s
an uphill battle. A scant 4% of the 3,160 CIOs
surveyed (across 98 countries) have implemented AI. Less than half plan to do so soon.
“Who cares?” you may ask. Factory production is booming without it. You have other
more pressing needs. And who wants to live
in a world run by Skynet?
The problem is that in real life, AI doesn’t
mean a sentient supercomputer; it refers to
the computing programs and algorithms that
can fetch and analyze data among a million
other things to help give your operation an
edge. The other problem is that on this ul-tra-competitive global playing feld, you need
every advantage you can get.
And using AI where appropriate is a potentially game-breaking advantage. It’s not
the beast mode running back that plows
over defenders, but rather a trusty assistant
coach who can break down your plant’s data
like it’s game flm, recommending where to
pivot and when to speed up or slow down.
It’s what the manufacturing heavy hitters’
C-Suite uses to make informed important
decisions, and at the plant management
level, keeps things running smoothly so you
can focus on making things of the highest
HPE DIGITAL PRESCRIPTIVE MAINTENANCE SERVICES
quality for the optimal cost. Instead of putting
out fres daily, you can fgure out how to burn
“Global tech giants are investing heavily in
AI, but the majority of enterprises are strug-
gling both with fnding viable AI use cases
and with building technology environments
that support their AI workloads,” Ammanath
says. “As a result, the gap between leaders
and laggards is widening,”
To counter this, HPE has recently launched
a few new offerings to help factories go from
struggling to thriving. Companies can start
with the Artifcial Intelligence Transformation
Workshop, which identifes and prioritizes use
cases that will convert AI into the insights that
will cut defects, boost production, and provide
real competitive advantage. A more specifc
product, the Digital Prescriptive Maintenance
Services, targets equipment downtime by
predicting, suggesting, and automating fxes
before anything breaks.
While predictive maintenance uses sensors and data to alert you to an overheating
machine, the prescriptive model does that
and more. Ammanath says the Pointnext
solution could then automatically switch
on a cooler to reduce the heat—or shut the
whole thing down. You train the model to
do what is best for the operation; the A.I.
doesn’t go rogue.
While it does all the computing, you look
like the genius. Kaeser Compressors discovered this when the it ran a pilot project with
HPE’s Pointnext solution along with SAP HANA,
an in-memory data platform. Embedded in
air compressor equipment it would sell to
customers, the solution constantly fed data,
BEHIND THE SCENES NEW PRODUCT DEVELOPMENT
such as temperature, vibration, and humidity,
to the predictive analytics system. In real time,
Kaeser would know if a part is likely to fail and
replace them during scheduled maintenance.
This led to a 60% reduction in unscheduled
system downtime, as well as not having to
make an expensive emergency visit. The benefts for the customer are obvious: pneumatic
applications or whatever else they use the
compressors for can go on as usual.
Along with building client trust, Kaeser
can now more effciently manage inventory,
which created an estimated annual savings
of $10 million in break-fx costs. The insight
also allows Kaeser to improve product design
and prevent future device failures.
Finally, it lets companies like Kaeser to sell
their services as a solution.
“We’ve also used this new IIo T infrastructure to launch an air-as-a-service business
model, helping us move costs from CAPEX
to OPEX,” says Kaeser CIO Falko Lameter.
By monitoring usage and analyzing trends the
Kaeser’s Sigma Air Manager, CEMEX, a building
supplies manufacturer, was also able to reduce
compressed air energy usage by 28.5%.
This is the true power of AI that unleash-
es exponential growth by “rethinking new
revenue channels and products,” unlike cus-
tomer service chat bots that may yield some
incremental improvements, Ammanath says.
“That’s where I think the value of AI truly
is,” she says.
It’s a promising technology, one that companies big and small will be using soon.
“The complete industry will go this way in
the next decade,” Ammanath predicts.
But it’s still so new that getting plants to
buy in could be the tougher than programming the AI.
“It’s never about the algorithm,” she says.
“It’s about adoption. Plant workers have been
doing their job for 20 to 25 years. They know
when a machine is going to fail just by in-
stinct. How do you get them to actually trust
a piece of software? When [AI] tells them this
machine is going to go down in a week, they
don’t believe it.”
As they see more and more competitors
leveraging data in new, innovative ways, we
have a hunch they will start believing, too.
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Using HPE’s Pointnext services, Kaeser reduced unscheduled downtime of its compressor
equipment by 60%.