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IoT And Big Data At Caterpillar: How Predictive Maintenance Saves Millions Of Dollars

7 February, 2017

IoT And Big Data At Caterpillar: How Predictive Maintenance Saves Millions Of Dollars

When it comes to big data and Internet of Things (IoT) initiatives most companies are still in the design or early adoption phases which make it hard to get a solid return on investment (ROI) figures.  So it’s refreshing to share a story of an organization delivering real-world ROI for their customers by vastly ramping up their data collection and predictive maintenance analytics.

The Marine Division of Caterpillar serves fleet operators of tug boats and shipping vessels for whom fuel usage often drives the bottom line. James Stascavage, intelligence technology manager at Caterpillar Marine, served with the US Navy for 28 years before joining ESRG, which was acquired by Caterpillar in 2015. He shared “Often times when people look at data, they’re looking for the ‘grand slam’ – one thing that’s going to save them tens or hundreds of thousands of dollars. In reality, it’s the small improvements that can add up to big dollar savings across many vessels,” he told me.

As companies set out on data-driven transformations, they want a blueprint to uncover a ‘north star’ insight – one golden discovery that will guide them towards growth. Stascavage shared many “north star” discoveries customers have made using Caterpillar’s Asset Intelligence platform, which is built on Pentaho’s data integration and analytics platform.

Providing ROI on Big Data Investment 

Shipboard sensors monitor everything from generators, to engines, GPS, air conditioning systems and fuel meters. In one example, Caterpillar is able to identify that fuel meter readings are correlated with the amount of power used by refrigerated containers.  This data can now be used to determine optimum operating parameters, by simply modifying power output from the generators.

By doing multivariate predictive maintenance analysis in Pentaho, the customer discovered that running more generators at lower power was a more efficient approach than maxing out a few.

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