Mantenimiento predictivo de maquinaria en Fabricación - Seguridad Mania.com - España y América Latina

Participe en este webinar titulado "How the Cloud Can Deliver the Benefits of Predictive Maintenance Sooner" (Cómo beneficiarse mediante el Mantenimiento Predictivo trabajando en la nube), patrocinado por Epicor e IBM. En el mismo se discutirá la reducción de la dependendia de IT, integración de datos, y especialistas en minería de datos, mediante un mantenimiento predictivo, consiguiendo así beneficios más rápido

 

Asset-intensive industries are becoming increasingly instrumented and connected, motivated by the immediate need to improve efficiency or via interest in the potential of the Internet of Things. These trends enable capture and analysis of volumes of detailed operational and performance data.

 

However, transformation of these data into meaningful insights that help operations, manufacturing, production, maintenance, and procurement to make well-informed decisions remains a challenge. Provisioning the IT environment, identification of meaningful asset performance data, development of accurate predictive models, and delivery of asset health reports in real time have depended upon combined and coordinated efforts of IT, data scientists and business intelligence professionals.

 

Cloud technologies, pre-built analytics, industry- and asset- predictive models, and customizable user interfaces significantly improve the ability for organizations to operationalize the inherent value of asset performance data. Via real-time reporting, lines of business can use this information to assess asset health -- historical, current and future -- to make better decisions to proactively manage critical assets to help improve asset availability, reduce maintenance costs, and improve throughput.

 

Participate in this webinar to learn how:

 

The cloud facilitates adoption of predictive maintenance to realize benefits sooner by reducing dependence on IT, data integration, and data mining specialists.

Customizable predictive models can identify pending asset degradation or failure, identify likely reasons for failure, and recommend optimum maintenance schedules.

Operations, production and maintenance users with high manufacturing knowledge but low analytic knowledge and experience can gain real-time insight into asset health.

Integration with EAM solutions, such as IBM Maximo, can accelerate time to repair.

Predictive capabilities complement current asset management practices to further improve asset availability, increase throughput, minimize unplanned outages, and reduce maintenance costs.