In the article IoT And Big Data At Caterpillar: How Predictive Maintenance Saves Millions Of Dollars, the author examines an interesting case of the company Caterpillar saving significant amounts of money using big data and the IoT. The best part of this case study is that Caterpillar is seeing a very quick ROI on their big data investment, which is not something that can be said for most companies. As a Caterpillar manager put it, you don’t have to look for a “grand slam” with big data; sometimes you just need multiple smaller applications of big data to experience significant savings. In this instance, gathering as much data as possible seems to be the best approach, and utilizing experts in the processes and in the data to analyze and understand the insights gleaned helps realize real value.
Caterpillar utilized big data on in its Marine Division, mainly to analyze fuel consumption for its customers as it most affects the bottom line. Sensors on the ship monitored everything from generators, engines, GPS, refrigeration, and fuel meters, and Caterpillar utilized Pentaho’s data and analytics platform. Insights gained have been a correlation between fuel meters and amount of power used by the refrigeration containers, and also that running more generators at lower power instead of maxing out a few was more efficient. The cost savings here added up to $650,000+/year. Another insight was to the optimization of a ship’s hull cleaning schedule. Through the collection of data of cleaned and uncleaned ships, the data showed that cleanings should be performed once every 6 months instead of once every two years. The savings associated with this optimization was $400,000/ship.
In the grand scheme of big data, predictive maintenance analytics seems to be the most powerful tool consistently being used. With data being generated just about anywhere you could imagine via the IIoT, understanding trends becomes easier and easier. Interestingly and contrary to previous articles, Caterpillar believes that you can’t collect too much data. They point out how data storage is very cheap. In the words of a Caterpillar manager, you can’t see “relationships about relationships” in the data if you don’t collect it. Although a more is better approach is definitely not what some companies have ascribed to, it seems to be working well for Caterpillar’s marine division as they continue to pull value of out of big data and analytics.
Quick returns on big data investments seems to be rare, so do you think that companies just aren’t utilizing the big data correctly?
Do you believe in the more is better approach with regard to collecting data?
Do you believe Caterpillar is more likely to invest in big data projects in other parts of their company due to the success in the marine division?