Momenta invests in Shoreline IoT, expanding real-time asset management and machine health to 'Dark assets' - The EE

Momenta invests in Shoreline IoT, expanding real-time asset management and machine health to ‘Dark assets’

San Francisco, United States. 17 August 2022 – Momenta announced an investment in Shoreline IoT, the creator of the off-the-shelf, one-stop asset performance management (APM) solution for industrial condition-based monitoring. This is the seventh investment from Momenta’s AIoT ecosystem fund, powered by Advantech, the world provider in digital industrial solutions.

Asset-intensive industries, such as manufacturing and energy, are plagued by unpredictable operating performance, lost production, and escalating costs. APM solutions unlock machine data and use analytics to improve the reliability and performance of these capital assets. When successfully deployed, they can help improve a company’s bottom line by providing actionable knowledge to stabilise production performance, reduce operating costs and assure optimal return on their assets. APM Solutions are already a multi-billion-dollar market, with the high-end market focused on monitoring the most valuable industrial machines already instrumented and connected. However, there is a significant opportunity at the opposite end of the market.

Most industrial machines are un-monitored or only monitored via manual inspection. They are sometimes referred to as ‘dark assets’ because their conditions are relatively invisible most of the time. The lack of real-time visibility for these dark assets can lead to reliability and maintenance issues, and ultimately to higher expenses and ESG compliance issues for asset operators. Dark assets remain unmonitored because it’s hard to make monitoring them simple, easy, painless, and useful. Shoreline IoT solves that with an easy-to-install, fully integrated APM solution available as a service.

With this financing, Shoreline IoT will scale its connected asset performance management solution. Shoreline IoT’s off-the-shelf software as a service (SaaS) solution is built using multiple services from Amazon Web Services (AWS). The deep insights and predictions enabled via Shoreline IoT’s physics models asset library and self-supervised machine learning do not require historical records and expensive data scientists. Shoreline IoT’s end-to-end IoT – ML offering unlocks deeper insights on equipment health, including advanced root cause analysis to maintain optimal asset performance and reliability. API access enables 3rd party data connectivity to the Shoreline solution from historians, ERP, work order, SCADA, and other systems to deepen insights and provide visibility across asset operational environments.

“Shoreline IoT’s AI/ML APM solution is designed to rapidly scale and provide fast time to value from trial to production,” says Kishore Manghani, CEO and founder of Shoreline IoT. “Domain expertise is built into the solution to bring consumer technology ease-of-use to the industrial sector, eliminating the need for experts, analysts, and data scientists that other solutions require to install the solution, configure assets, and interpret the data. Reliability engineers can now manage more assets with fewer resources.”

“Shoreline IoT impressed me. They created brilliant sensors for dark industrial assets. Next, they delivered superior machine health predictions in real-time from previously unmonitored machines. Lastly, they enabled self-provisioning without experts and made it look easy,” says Michael DolbecMomenta. “Shoreline IoT delivers a potent ‘You can do this yourself’ message to the customer.”

“I am excited to support Shoreline IoT as they continue to scale and enable asset-intensive customers to pursue their digital transformation,” says Weili Dai, co-founder of Marvell Technology Group and Shoreline IoT co-investor.

Jerry O’Gorman, president of Advantech North America, states, “Shoreline IoT’s dedication to the user experience through its sensor and solution as a service approach represents a real break-through in the industrial sector. Otherwise, stranded and dark assets that may be critical to overall process performance are quickly and easily connected to deliver meaningful data and insights without requiring complex intervention from specialists.”

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