GE Digital and Intel hosted a webcast that dove into the future state of IIoT, what it means for the original equipment manufacturing (OEM) industry, and how OEMs can capitalize on the opportunity to increase customer productivity and create new revenue streams.
Check out the highlights here then click below to watch the full webcast.
Preventing one hour of downtime can be worth $250,000 to a semiconductor manufacturer. Predicting when equipment downtime might occur and preventing it improves throughput for the semiconductor manufacturer. This also builds customer satisfaction and a strong relationship with the equipment manufacturer.
80-90% of equipment failures begin randomly, and “just-in-case” service technician staffing and spares inventory for unexpected downtime can be extremely costly. Leveraging condition-based and predictive techniques to detect issues before they fail enables leaner staffing and lower spare parts inventories.
It’s commonly understood that downtime of critical production equipment impacts throughput. What isn’t common knowledge is that degraded performance from supporting equipment-like filters and fans can also have a significant impact on semiconductor quality and yield.
Equipment manufacturers and their customers are looking for Product-as-a-Service business models. These models often include high service level agreements (SLAs) and require new and better approaches to ensure uptime while managing costs.
Security remains a top priority within the semiconductor fabrication plant. GE Digital's industrial portfolio is purpose-built with a high degree of security to protect all data and intellectual property.
See how APM Health, featuring Intel technology, is helping OEMs understand the health and reliability of their equipment.
Learn how to design solutions that achieve interoperability and scalability that meet needs today and in the future. [email protected] research paper.
Optimizing the performance of assets to increase reliability and availability, minimize costs, and reduce operational risks.
Standardize the collection, integration, modeling, and analysis of disparate data into a single, unified view.