Data analytics firm Mariner, launches IIoT sister company Spyglass
Devin JonesAutomation General Data Analytics IIOT
The first solution is Spyglass Connected Factory enabling manufacturers to monitor production-line data in real-time.
Mariner, a Microsoft Gold Partner in Data Analytics, Data Platform, and Cloud Platform, recently launched their sister company, Spyglass Connected Solutions, Inc, an industrial IoT (IIoT) and AI software company that enables manufacturers to reduce unplanned downtime, improve product quality, and balance production.
Spyglass represents the next stage of investment by Mariner as a distinct software-as-a-service company dedicated to providing IoT and AI solutions, powered by the Microsoft cloud.
The first solution is what Mariner calls the Connected Factory, enabling manufacturers to monitor production-line data in real-time.
Mariner states that the Spyglass Connected Factory will be able to overcome two top barriers to IIoT adoption: excessive cost and access to infrastructure. Spyglass Connected Factory is available as a 60-day trial for $9,900 USD that can be deployed in as little as 15 days.
“With the launch of Spyglass, manufacturers now have solutions that can help them jump-start their digital transformation, one factory at a time,” said Philip Morris, CEO and Co-founder of both Mariner and Spyglass. “We believe in the value of starting small, thinking big, and going fast as the path to achieving the most significant return on IIoT or AI investments.”
The connected factory will focus specifically on remote monitoring, Overall Equipment Effectiveness (OEE), and predictive maintenance cycles based on indicators of decreasing performance or pending failure.
Two examples Mariner gives for software in action is a textile manufacturer and a U.S based recycling operator. The former relied on device data from looms to optimize processes, reduce maintenance costs & reduce operational expenses while the latter “uses Raspberry Pi + sensors to monitor line speed to optimize gross margin & help forecast shift labor requirements.”
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