A new model for connected assets

Powered by AI and IoT data, connected and intelligent assets can optimize performance, adapt to changing circumstances, and help ensure continuity.

More and more high-value physical assets, such as manufacturing equipment, gas turbines, and electric utility transformers, are digitally connected. And it’s no wonder. Smart, connected assets feed industries geared toward more efficient use of resources—and reducing costs. Continuously, in real-time, these assets provide data on their current operating conditions, which opens the door to upending the traditional model for operations and maintenance. Organizations that don’t keep pace will have difficulty responding to real-time changes and disruptions to their operating environments.

Even with all their benefits, connected assets can also complicate things: As organizations seek to consume all this data and use it to glean valuable insights, they also need to continue resilient and uninterrupted operations. The software connecting these devices also creates its own set of failure points to be managed, for example, what to do when a sensor “dies.”

Intelligent automation incorporates artificial intelligence  (AI) and other technologies to manage and improve  physical and digital business processes automatically  and continuously.

Reduce downtime and costs

In the next few years, Chief Operating Officers plan to invest heavily in the technologies that are key building blocks of intelligent automation: cloud, advanced analytics, and the Internet of Things (IoT). The top outcome of these investments in digital strategy is improved operational uptime.

Digitization can make assets less expensive to maintain and operate. Mining companies, for example, use autonomous vehicles for certain tasks. Equipment can be remotely monitored—sometimes from halfway across the globe—to check for proper oil pressure or temperature and keep the asset running as it should. Robots working in mines underground can operate with no downtime, and mitigate the safety risks of hazardous conditions such as fire, flood, collapse, or toxic atmospheric contaminants.

Digital twins of high-value assets and their ecosystems  can help drive productivity and predictive maintenance.

Pairing the virtual and physical worlds improves operations

New operating models can use predictive analytics and a “digital twin” version of a physical asset to anticipate how an asset is operating today, when it might fail in the future, and under what conditions. Think of a digital twin as a virtual clone that reflects its physical version’s lifecycle, and facilitates remote monitoring, predictive planning, and proactive management. It's estimated that more than 21 billion connected sensors and endpoints will soon exist for potentially billions of things. A digital twin model helps drive a new level of reliability by performing data analysis on physical assets, which can help drive better and more reliable decisions about equipment.

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Meet the authors

Joe Berti

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, Vice President, IBM Applications Offering Management

Kay Murphy

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, Leader, IBM Global Asset Optimization Services

Terrence O’Hanlon

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, CEO, Publisher - ReliabilityWeb.com, Uptime Magazine, and the Reliability Leadership Institute

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Originally published 04 June 2020