KloudGin is now providing the only single cloud offering that encompasses a closed loop, AI-based Predictive Maintenance solution with seamless workflows.
SUNNYVALE, CA, UNITED STATES, November 9, 2021 /EINPresswire.com/ — KloudGin is now providing the industry’s only single cloud offering that encompasses a closed loop, AI-based Predictive Maintenance solution with seamless workflows. This facilitates work-order creation, scheduling and execution, which helps prevent asset failure and potential outages.
KloudGin, the leading provider of cloud-based field service and asset management solutions, is announcing the availability of its new Predictive Maintenance (PdM) application. KloudGin’s single cloud solution, which now includes PdM, is the first of its kind to allow operations to combine the power of artificial intelligence (AI) and IoT-enabled enterprise asset tracking. Further, this application will have the capability to remotely troubleshoot the status of an asset, so that field service technicians can be immediately deployed prior to failure.
KloudGin’s new predictive maintenance capability was designed to support utilities, manufacturing and commercial operations to maximize asset efficiency, reduce maintenance costs and increase workforce engagement for asset manufacturers, owners, and operators.
Unlike other PdM applications, KloudGin’s solution seamlessly ties operational Enterprise Asset Management and Field Service Management together with AI-based machine learning models, allowing operational managers to receive real-time updates on work execution, as well as on-going connected customer communications from the technicians.
“The new KloudGin PdM application is a milestone for the industry. KloudGin PdM allows utilities, manufacturing and commercial operations to predict failure, provide potential outage alerts, and give a manager automatic visibility on a technician’s availability, schedule and route work, all based on skills, location, and priority,” said Vikas Bansal, KloudGin CTO and Co-Founder. “Utilities and asset-dependent operations can immediately benefit from our closed loop approach: Monitor / Analyze / Predict / Act / Learn, which creates a built-in response to condition-based maintenance (CBM) to better align resources and actual asset performance. This will reduce unplanned downtime, by detecting problems sooner, avoiding service on assets that are performing well, and supporting technicians with better data and recommendations.”
Predictive Maintenance helps reduce operational costs and increases productivity.
Predictive maintenance lets operations estimate time-to-failure of a machine, and create the optimum schedule for equipment maintenance. It can also identify what parts need to be fixed, predict future failure, and track regular problems in complex machinery.
“IDC research shows that 65% of utility, transportation, and telecommunications organizations worldwide expect that incorporating circular economy principles will profoundly impact their operations including design processes, waste reuse, and retiring assets.” says Juliana Beauvais, research manager in IDC’s enterprise applications practice. “Predictive maintenance supports a utility’s decisions on when to repair or replace equipment. Without digitizing or standardizing enterprise asset management, a utility continues to waste time and costs on unplanned asset downtimes, and unanticipated equipment failures.”
KloudGin’s new Predictive Maintenance app provides:
– Real-time condition monitoring: Service and engineering managers can review the internal data collected by various pieces of equipment, to check if there are any changes in performance.
– Performance analytics: Technicians can analyze trends in equipment performance, to determine when to order new pieces, or to arrange preventive maintenance.
– Automated notifications and alerts: The KloudGin PdM application will send automated reminders to managers so they stay informed on condition-based monitoring and scheduled maintenance.
– Maintenance scheduling: Allows operations to automatically schedule maintenance services based on performance and equipment usage.
– Work Order management: Asset-dependent operations can ensure all scheduled maintenance is performed in the correct order, to cut down on downtime and streamline actionable repairs
– Supply Chains: Operations will be able to manage spare parts more effectively based on what is needed for upcoming maintenance, and to ensure ideal asset performance throughout their lifecycle.
Source: EIN Presswire