1. Connected Car
Take the automotive industry as an example. Today’s connected cars create and relay vast amounts of performance data from sensors spread throughout the vehicle. This information goes straight to manufacturers or car dealerships, who can then alert drivers of any issues that need servicing before they experience the inconvenience of their car breaking down.
2. Utility Suppliers
In other sectors, companies are turning to predictive maintenance to work smarter internally. Utility suppliers are applying predictive analytics to the big data generated by smart meters so they can detect early warning signs of supply and demand issues on the grid and address them before they lead to outages. Not only does this save them the cost of costly repairs, it also helps them avoid customer dissatisfaction.
In the research field, CERN currently analyzes big data produced by tens of thousands of sensors on the Large Hadron Collider to ensure its particle accelerators are operating at full potential, and if not, that the causes of any fault can be identified and addressed.
4. Manufacturing and Internet of Things
One of the quickest wins for predictive maintenance has been in the manufacturing sector. Manufacturers increasingly collect big data from Internet of Things (IoT) sensors in their factories and products, and have begun to apply algorithms to this data to uncover warnings signs of costly failures before they occur.
GEMÜ, a leading manufacturer of valves and automation components, has deployed technology based on the internet of things (IoT) to monitor the performance of manufacturing processes in order to detect and replace deteriorating components before they fail, improving the efficiency of its manufacturing processes.
There are few industries where predictive analytics doesn’t have the potential to add tremendous value. From manufacturers to governments, retailers to hospitals, organizations run complex processes and make decisions on how to optimize these every day. By analyzing historical, current and relevant external data, many of those decisions can be improved to the benefit of both businesses and their customers.
For instance, the insurance industry will benefit from being able to make more powerful predictive analytics around the likelihood and impact of extreme weather conditions. Supermarkets and their suppliers could improve their operations based on more accurate predictions about crop yield and production. The opportunities are virtually endless.
The Future of Predictive Maintenance
We are still in the early stages of predictive maintenance. Most companies are still focused on collecting a wide range of big data and establishing the correlations between different ways of working and product quality. Once they have enough data, businesses will begin to create increasingly sophisticated models that more accurately predict failure.
To unlock the full potential of predictive maintenance, businesses must become more adept at managing the growing volume of data within their organization and ensuring it is fit for analysis. An analysis might include data from spreadsheets, databases, social media and even photos, so having the right data preparation processes in places will be crucial to combing all this information in a cohesive way.
The potential of big data in maintenance has yet to be fully realized, but there is one prediction we can make for sure: predictive analytics is going to become an increasingly important part of how companies are run.
From data scientists and analysts, who work closely with company data each day, to business leaders exploring new ways to improve the way they work, Oracle has a set of rich integrated solutions for everybody in your organization.
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