Accelerating the
Industrial Internet of Things

Ditch the old, welcome the new - Making the most from your industrial assets with cognitive predictive maintenance

Published on 07/06/2017 | Operations

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Anita Raj

Head of Growth Hacking . DataRPM


The Industrial Internet (IIoT) is radically changing the way manufacturing organizations function and create value for their customers.  According to McKinsey, the economic impact generated by IIoT which incorporates machine learning and big data technology would be $11.1 trillion by 2025.

Today, IIoT is being leveraged to enhance results - from operational performance to asset maintenance processes to the customer experience. The focus has clearly moved from merely getting things done to achieving those same things with a lesser investment of time, effort and money.

A key challenge which production centric industries face is to ensure that they take good care of their assets. Throughout the asset lifecycle, efforts are focused on extracting maximum value from equipments by slashing down unplanned downtime, predicting failures before they occur and toning down maintenance and repair costs.

What further complicates matters is that every asset is a part of this complex ecosystem where each of their roles are interdependent and equally disproportionate as well. While some assets contribute to greater uptime, few others may not be that business critical 24/7. Organizations therefore not only need a clear asset performance management (APM) strategy which clearly lays out each assets’ scope but also needs a viable plan to derive maximum value from high-stake assets.

Moving from the old to the new

The answer is quite simple, yet the possibility is not fully explored. To start obtaining ultimate value from your assets you need a shift from traditional preventive data science techniques to cognitive predictive maintenance and machine learning powered with automation.  

Moreover, why?

The ultimate objective of traditional asset management is to help reduce asset lifecycle costs, but sadly preventive maintenance schedules don’t do a great job in avoiding asset failures.  Since minimizing unexpected outages, managing asset risks and maintaining assets before failure strikes are critical goals for asset-intensive industries, an automated approach to predictive maintenance helps big time.

Firstly, traditional data science is limited by human scale and analyzing machine data manually may not provide razor sharp insights about the asset data. A machine first approach charged with automated machine learning will help overcome this challenge.

Secondly, typical asset data patterns are extremely volatile and change too often. Most traditional data science models get obsolete even before they are productionized. This is why a cognitive approach will help with lightening fast speed in picking up flickering data patterns.  

Thirdly, asset data is complex due to its veracity, velocity, volume and variety all of which are quite impossible to comprehend with a manual approach. Data Scientists need the supreme weapon of cognitive predictive maintenance to differentiate the true signals hidden in the data scattered over millions of data points across various sensors. This again is only possible with an automated approach to data science

A Digital Industrial in the making

The IIoT is all set to transform traditional industries with a complete new data-rich digital avatar. However, so far only 5% of organizations have succeeded in this overhaul.   By obtaining high value from their existing assets, the remaining 95% can make most of this digital wave and enter into the league of extraordinary champions.


This article was originally posted on LinkedIn.

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