I N SI G H T S
into P R E D I C T I V E M A I N T E N A N C E
Hindsight May be 20/20, but Predicting the Future is Better
A Closer Look at Predictive Maintenance
Today, predictive maintenance is a concept that is
generating interest and building momentum, yet it still
raises a number of questions. Specifically, what is predictive
maintenance? How can it benefit your organization?
How can you get started?
Predictive maintenance, also known as predictive
asset maintenance or predictive analytics, is the idea
of capital equipment makers using embedded sensors
combined with connectivity, data communications,
advanced analytics, and remote diagnostics to predict
equipment breakdowns before they occur. The practice
uses very advanced statistical and data-mining
techniques to analyze large volumes of data to best
predict potential failures down the road.
Yet predictive maintenance is much more than just the
idea of capitalizing on device connectivity, which is the
basis for the Internet of Things (IoT) and the related
Industrial Internet of Things (IIoT) trends. IoT and IIoT
imply that value can be created from machine-driven big
data in an industrial or manufacturing setting through
device communications. Predictive maintenance takes
this idea a big step further by using available data to
predict equipment failures and take action before they
can occur. This helps avoid adverse—possibly
catastrophic—effects to safety, productivity, and profits.
Warning: Danger Ahead
To picture predictive maintenance in action, think of a
supplier of capital equipment products in the manufacturing, automation, or material handling industry
who embeds a broadband sensor in one of its products.
This sensor collects data related to the performance
of the product and the surrounding environment and
provides valuable trend information. But, hidden in this
vast amount of data—even a single sensor can provide
mountains of data—can be clues that something is
wrong. This is true even if the product is operating at
a normal level of quality and throughput.
Predictive maintenance is the perfect approach to finding
these hidden clues. In this case, the analytics uses
knowledge of the underlying physics of the specific
manufacturing process as well as the overall trend data
to identify a potential future failure. Then, when supervisors receive a notification related to a pending failure,
they can schedule maintenance or parts replacement
at the most convenient time, such as the end of the
next shift, to minimize costly downtime.
Advanced Notice, Advanced Benefits
The benefits of predictive maintenance are significant—
and compelling. For example, predictive maintenance
can drastically reduce machinery failure rates, increase
uptime, and maximize production. It can also decrease
service costs and lead to higher customer satisfaction.
Predictive maintenance also lets capital equipment
suppliers better manage the fleet of equipment they
have installed at their customers’ manufacturing sites
and operational facilities. Additionally, collected data
can be used to identify and prioritize future product
enhancements, critical for increasing market share and
revenue.
As attractive as these benefits may be, many capital
equipment manufacturers have not yet embraced predictive
maintenance. According to a recent survey by Deloitte
and MHI, only 16% of respondents indicated that they
are using this practice today, or plan to use it over the
next one or two years for the purpose of predicting
machine breakdowns for preventive maintenance.1
With so much at stake—and seemingly so much to
gain—why aren’t more capital equipment suppliers
leveraging predictive maintenance?
Making the Case
It’s an interesting question, especially when capital
equipment makers stand to gain a substantial competitive
advantage with this type of innovation. As the value of
the asset they are selling and the impact of that asset’s
failure both increase, so too does the rationale for
investing in predictive maintenance.
Consider the case of jet engines, which are very expensive
to build and have an extremely high impact in the
case of failure. Manufacturers of jet engines are using
predictive maintenance to minimize engine downtime
caused by parts failure or to improve efficiency and
performance. For example, GE uses sensors to collect
jet engine data to perform short bursts of maintenance
before larger issues can occur and create downtime.
This means that over an entire lifecycle, the engine
spends less time in repairs, which has led to significant
productivity gains for GE employees.2 Market and industry
factors are driving the return on investment of predictive maintenance down to less expensive products.
Today, warehouses and manufacturing plants are
becoming smaller, closer to consumers, and more
automated to better respond to consumer demand for
customized products. Yet as automation increases, the
number of human workers decreases, a trend that requires
even more automated decision-making, especially
when those decisions are related to machine maintenance.
Finally, recent advances in data tools, technologies, and
services further enable the concept of predictive maintenance. The cost of extracting value from big data—
once considered prohibitive—is decreasing every day.
Increasingly powerful data tools and analysis techniques
are now readily available, and are being deployed by
sophisticated users familiar with advanced statistical
methods as a means to create business insight. All of
this is evidence that the time is right for capital equipment
suppliers to develop predictive maintenance functionality
that will result in increased market share and keep the
competition at bay.
Where Do We Go from Here?
Yet the question remains: how do capital equipment
makers proceed with predictive maintenance? Stay
tuned for the next article in this two-part series, which
will describe how companies can implement specific
programs to make predictive maintenance a reality.
Sources:
1 Deloitte and MHI, “The 2015 MHI Annual Industry Report: Supply Chain Innovation—Making the Impossible Possible,” p. 26, 2015.
2 Computing, “The Future Is Here Today: How GE Is Using the Internet of Things, Big Data and Robotics to Power its Business,” March 12, 2015.
About the Author – Tom Mariano
As Executive Vice President and General Manager, Tom provides strategic oversight and technical expertise
to Foliage’s Industrial Equipment practice. He has over 25 years of experience in software development,
engineering and marketing management. Tom’s focus during his career includes semiconductor and automotive
manufacturing automation; robotics and material handling for warehouse and distribution solutions. He holds
a Master of Science in Robotics and Control Systems from MIT, and a Bachelor of Science in Mechanical Engineering
from Northeastern University.
Have questions about predictive maintenance? Contact Tom at tmariano@foliage.com
About Foliage
Foliage, part of the Altran Group, is a global product development company partnering with clients to address
the business and technical challenges inherent in developing, manufacturing and supporting complex,
connected systems. Providing a full complement of technology consulting and engineering services, Foliage
ensures clients deliver innovative solutions to market while reducing total cost of ownership over the lifecycle
of their products. Visit foliage.com
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