Understanding Asset Optimization in Manufacturing

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Understanding Asset Optimization in Manufacturing
Mission-critical assets in manufacturing setups
can make or break an entire value chain. An
unmitigated asset breakdown or productivity
decline can halt production for hours, or even
days, resulting in huge revenue losses and
unsafe work environments. The situation becomes
even more complex for industries with
distributed assets. To maintain assets in
optimal condition and run the production process
without disruptions, dedicated maintenance teams
have to be deployed in various locations.
Furthermore, investments are required in
carrying spare-parts inventory and establishing
strategic service contracts with Original
Equipment Manufacturers (OEMs). While
conventionally, these practices have been
considered inevitable, a marked shift is
happening towards predictive analytics and
responsive maintenance solutions that can
optimize asset performance. This article will
cover what exactly asset optimization is, why is
it important for manufacturing industries, and
how predictive maintenance solutions empower
maintenance and operation teams to achieve
it. What is Asset Optimization? Optimization
essentially means making something as effective,
functional, reliable, and productive as
possible. Asset optimization means optimizing the
way that an asset is utilized and deriving
maximum value from it. It also entails driving
efficiency and reliability objectives by
improving the Remaining Useful Life (RUL) of an
asset and enhancing the Overall Equipment
Effectiveness (OEE). Asset Optimization depends
on leveraging data-driven intelligence and
predictive analytics to achieve business
objectives and add to the bottom line.
IoT-enabled technologies can be deployed to
monitor asset conditions and analyze real-time
data to determine maintenance needs.
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Asset Optimization through Predictive
Maintenance Predictive Maintenance solutions can
help plant maintenance teams overcome the various
challenges in asset performance management and
ensure asset optimization across the plant. With
a predictive approach, maintenance teams monitor
asset conditions remotely with the help of
cloud-enabled technologies. Vibration analysis,
acoustics, thermography, oil analysis, and other
remote condition monitoring techniques are
deployed to track asset conditions while they
operate as per schedule. The machine health
data is centrally collected and analyzed with the
help of Industrial IoT (IIoT) technologies and
accessible through responsive dashboards to
concerned stakeholders. Since maintenance has to
be strategized based on predictive insights, edge
diagnostics, and advanced analytics are used to
determine which asset is performing non-optimally
and in need of attention. Such a focused
approach to asset performance management has
several benefits
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