The main market opportunities in the predictive analytics and maintenance sector within supply chains revolve around the ...
Moving from reactive to predictive maintenance requires the right tools for use by subject matter experts. As the power industry continues to advance from preventative to predictive maintenance, one ...
Predictive maintenance (PdM) is a process used to monitor equipment during an operation with the purpose to identify any deterioration. It helps to plan maintenance schedules and reduce operational ...
Predictive maintenance is emerging as a necessity for aerospace and defense (A&D) systems. By leveraging advanced analytics to monitor equipment health and anticipate failures, operators can ...
TL;DR: Predictive analytics influences day-to-day decisions once forecasts change behavior rather than sit inside ...
PleoGram Pleo-Gram focuses on real-time energy analytics, automation, and performance monitoring. The platform enables ...
Predictive analytics: the ability to use well documented historical and accurate data to improve future logistics or fleet maintenance operations. Data — especially quality data — is the key to ...
Uptake, a predictive analytics software-as-a-service (SaaS) provider, is teaming with Daimler Truck North America (DTNA) to enable the use of data-as-a-service (DaaS) technology to power Uptake Fleet, ...
Fleet maintenance managers are facing all kinds of roadblocks right now. Labor shortages, supply chain problems, parts inventory price increases and unavailability of used vehicles are all wreaking ...
A new year and decade provide an occasion for predictions regarding the state of analytics in the process industries. After three decades of data generated by digitizing control systems, stored in ...
San Francisco-based UCSF Health tapped Glassbeam to manage predictive maintenance of its medical equipment, the data analytics company confirmed April 11. Glassbeam will apply its CLEAN, or “Clinical ...
Predictive analytics, a branch of advanced analytics, helps forecast future outcomes using historical data, statistical modeling, and machine learning. In industries like vegetation management, it has ...