As a manufacturer, downtime negatively affects your efficiency, which means it also affects your bottom line. Predicting and preventing downtime or equipment failures is crucial to keeping your company running smoothly and productively. Manufacturers are turning to predictive analytics software to keep a close eye on critical infrastructure and prevent downtime. Being able to predict equipment failures and respond with preventive maintenance or a replacement will keep your company running at peak efficiency.
The convergence of multiple technologies and capabilities is enabling the rise of predictive analytics. Internet-connected devices allow the capture of real-time or near-real-time data. At the same time, cloud storage has made it easier and cheaper to store and analyze ever-larger data sets. It used to be that predictive models were built using only backward-looking historic or descriptive data sets. These models could use only a narrow data slice because there was less ability to store data, less power to process it, and certainly less ability to capture it in real time. Now, you can process unlimited data through open-source software utilities like Hadoop. The data can be stored cheaply, which allows data teams to build more robust models with access to an entire data set, rather than a small piece of the set. And that ability increases the chance of capturing more predictive features and attributes, which enables organizations of all types and sizes to use predictive analytics more broadly.
Noble Energy is an independent oil and gas company with a global presence. They began using a big data platform to predict and prevent downtime in their infrastructure. Frank Besch, Noble Energy’s director of business integration, notes that “the infrastructure creates value: money. It leads to sales, and when that capacity is not fully utilized, then there’s value that’s not being captured.” Predictive analytics allows the company to better maintain its hydrocarbon infrastructure. Noble Energy’s next goal for predictive analytics is to use data to improve safety and prevent worker injuries.
Offshore contract driller Rowan Companies used to have no access to a distributed, real-time data architecture. Without access to real-time data, their crews operated with limited capacity to provide remote support. They needed an Internet of Things (IoT) solution that could seamlessly connect sea to shore. Now, Rowan reliably collects real-time data from their industrial systems and instantly streams it. This allows for remote monitoring of certain conditions, and some of them are critical. With predictive analytics and maintenance forecasting, Rowan expects to reduce downtime as well as the number of trips to their rigs for troubleshooting.
Most companies now perform reactive maintenance (fixing equipment when it breaks) or preventive maintenance (a set schedule of maintenance tasks that are usually recommended by the equipment maker). But manufacturers looking to gain an edge in their markets are pursuing predictive maintenance. Using real-time data and predictive modeling, manufacturers can optimize their repair schedules, knowing with increasing certainty when a piece of equipment or infrastructure will degrade or fail. With that knowledge, they can optimize equipment use and reduce costs by performing just-in-time maintenance. This prevents production downtime caused by critical failures, makes the best use of assets, and keeps staff focused on the most critical tasks.
Using predictive analytics software to empower predictive maintenance can improve asset maintenance and internal decision-making, giving manufacturers a competitive edge.
Watch this video to learn about how Noble Energy uses predictive analytics software to maintain their infrastructure and improve worker safety.