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You lead a large automotive manufacturing unit. With the goal of keeping P&L figures on a positive trajectory, you are responsible for meeting manufacturing targets while keeping automotive asset maintenance costs under control. You are also tasked with keeping your factories operating smoothly with no unplanned outages or downtime.
Did you know that the failure of critical assets continues to be the number one operational risk for leading industrial outfits across the world? A recent study by ARC Advisory Group reveals that the global process industry loses up to $20 billion (almost 5% of annual production) due to unscheduled downtime.
Physical and digital worlds are converging in ways we could only imagine in the past. Learn how the Industrial Internet of Things has transformed and redefined asset-intensive industries such as manufacturing.
With market pressures and competition intensifying at an unprecedented pace, manufacturers are aggressively searching for new growth opportunities. Unfortunately, they’re often forced to work around approximately 800 hours of downtime on a yearly basis—a significant expense in the face of the unrelenting pressure on their top and bottom lines.
Despite growing adoption of the Industrial Internet of Things, many businesses are still losing billions to unplanned asset downtime and equipment failures. Cognitive anomaly detection and prediction can help IIoT companies address this issue. To learn more, join top machine learning and IoT influencer Ronald van Loon and Progress DataRPM Vice President of Product Ruban Phukan for our upcoming webinar.
The Industrial IoT sector is facing maintenance challenges related to their data processes. Traditional methods of anomaly detection aren’t providing the right solutions for every entity, which is why Cognitive Anomaly detection is filling the gaps in predictive maintenance.
The array of connected automotive industrial assets generate mass volumes of operational data. This data can be effectively tapped to map out potential risks using cognitive predictive maintenance, which can convey this insight to automotive industrial asset experts. By applying machine learning to operational data generated by critical factory and automotive assets, OEMs can gain greater insights into these risk-creating factors.
The Industrial Internet of Things (IIoT) presents a huge opportunity for businesses, and it’s only getting bigger. Gartner predicts that by 2020, the number of IIoT-powered devices will reach 20.8 billion and they’ll be producing trillions of gigabytes of data. Cognitive anomaly detection and prediction can help businesses monetize this wealth of data and drive meaningful change throughout their operations.
Downtime for today’s complex businesses means much more than a simple inconvenience. The cost of unplanned interruptions, the impact of unforeseen failures, the effect of unexpected breakdowns—all of this cumulatively means a lot more than factory workers merely being prevented from completing their normal tasks.
Often, organizations are faced with contradictory challenges — increase productivity, but eliminate machine failure…enhance quality, but reduce time to market…predict all potential issues, but quickly enough to take corrective action. A closer look at these challenges usually reveals that business and technology are in opposite corners of the wrestling ring. And however practical both perspectives may be, addressing them simultaneously is incredibly challenging — if not impossible.
Sure, you’ve implemented IIoT on your shop floor. Sure, it’s raking in the data. And sure, you now feel secure, knowing that all your machines are turning in their daily, hourly, and monthly timesheets. But let’s take a minute right there to evaluate exactly how secure you should be feeling.
Digitization has placed immense pressure on businesses and the way they function. Organizations are struggling to cope with the increasingly sophisticated machines they deploy as well as the data they’re generating (in some cases, crossing into exabyte territory).
The Industrial Internet of Things (IIoT) is the next frontier in the digital factory revolution. IIoT brings unprecedented opportunities to create new business models that propel growth by transforming the way humans and machines interact with each other, resulting in lower operational costs, higher machine efficiency and zero unplanned downtime.
Learn how businesses can leverage the IIoT and the data their machines generate to drive better asset performance via predictive maintenance.
Preventative maintenance is only as good as your anomaly detection. With many enterprises only capable of accurately predicting 20% of asset failures, they leave themselves exposed to a wide array of risks that could derail production. Progress DataRPM Cognitive Anomaly Detection and Prediction provides a framework that utilizes machine learning to automate and streamline the way enterprises approach preventative maintenance, promoting greater operational efficiency and productivity.
Financial setbacks stemming from existing asset failures can be significant, even fatalistic, to growing industrial organizations. Fortunately, the power of machine learning combined with the Industrial Internet of Things has provided leading industrial enterprises with pivotal data insights. This enables them to maximize asset performance and create new avenues to increase business value.
Enterprises pay a steep price for errors in their manufacturing process, both in terms of direct costs (like recalls and returns) and indirect costs (like damage to their brand). Learn how one manufacturer that spent millions of dollars annually on pipeline maintenance used DataRPM Cognitive Detection and Prediction to identify anomalies and prevent costly errors before they manifested.
An auto manufacturer struggled to improve the efficiency of operations in a scalable way due to the sheer volume of data that needed to be analyzed. With Progress DataRPM Cognitive Anomaly Detection and Prediction, the client was able to automate the analysis of this data and generate accurate recommendations to drive efficiency.
With margins on the decline, the oil and gas industry relies heavily on smooth production to drive profits. One oil company with a network of equipment that spanned thousands of miles, was spending millions of dollars annually just to keep everything running smoothly. Using DataRPM Cognitive Detection and Prediction, the client was able to optimize maintenance by identifying potential problems before they struck.
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