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5 case studies
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Complex Discrete Manufacturing with ThingWorx Analytics
Complex Discrete Manufacturing with ThingWorx Analytics
ABC faced two major issues when utilizing the data collected. One is the size, complexity, and disparity of the data collected would take lot of man-hours to process and evaluate. Two is real-time data cannot be put into use due to the nature of data science analysis.
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Intelligent Farming with ThingWorx Analytics
Intelligent Farming with ThingWorx Analytics
Z Farms was facing three challenges: costly irrigation systems with water as a limited resource, narrow optimal ranges of soil moisture for growth with difficult maintenance and farm operators could not simply turn on irrigation systems like a faucet.
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DeviceLynk Delivers Customized IIoT Solution
DeviceLynk Delivers Customized IIoT Solution
Previously to working with ThingWorx, DeviceLynk built an IIoT platform but found it lacked scalability. They needed something to capture and handle data from an unlimited amount of devices and customers.
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Reducing the Rate of Readmissions
Reducing the Rate of Readmissions
The client had limited or inefficient integration of its data sources, which made it difficult to see patients through a longitudinal lens. The client was, however, uniquely positioned to leverage the expansive patient data contained within its network of care, and set out to do so in 2012. Specifically, they wanted to improve the outcomes of patients with Ischemic Heart Disease (IHD) through improved care management with goals of reducing readmission rates, better managing patient cholesterol levels, and better managing patient blood pressure. Specifically, the regional healthcare provider was interested in implementing a machine learning platform, that quickly automates complex analytical processes and integrates powerful information into existing applications and portals.
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Springpath Gains Real-Time Intelligence and Improves Operational Efficiencies
Springpath Gains Real-Time Intelligence and Improves Operational Efficiencies
As the HCI market has grown increasingly crowded, Springpath sought to differentiate itself by providing enhanced serviceability to its customers. But it was lacking in its ability to provide fast customer issue resolution for both hardware and software components and in its ability to get meaningful, real-time intelligence from data. Springpath needed to create better operational efficiencies for its support organization. To better service its customers, Springpath would need a solution to resolve customer issues faster while collecting real-time customer data from systems to extract better insights for both internal support teams and end customers alike. Building a solution would require quick ingest and parsing of data – which was at the time often sent in the form of complex log files – and the ability to present it as actionable information. Further, a solution would need to enable support engineers to provide quick issue resolution to minimize downtime. Ideally, Springpath wanted customers to get full visibility into their own systems – and thereby be able to perform timely maintenance tasks – with the goal of maximizing uptime and ensuring optimal system performance. In addition, a solution should integrate with their issue management system, Salesforce.com Service Portal, for easy access for both the in-house support team and end-customers. “Offering world-class Customer Service in the IoT era requires a comprehensive and scalable Analytics platform that is well-integrated with an existing Support workflow,” says Brett Flinchum, VP of Global Customer Success at Springpath.
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