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Items filtered by date: January 2019 - CIMdata
The theme for CIMdata’s 2019 PLM Market & Industry Forum series is “Augmented Intelligence: Applications Across the Product Lifecycle.” CIMdata believes that the work done to date on this topic barely scratches the surface of what is possible. With this in mind let’s consider how closing the maintenance loop with product development will lead to ever better products that improve operational efficiency and customer satisfaction. Predictive maintenance is an area receiving significant attention because of the operational efficiency it can provide. IoT and Industrial IoT (IIoT) are improving access to field data, which is key to predictive maintenance. Considerable effort is being expended using traditional analytics and reporting tools to help equipment operators be more productive. As more sensors are added and data access improves, advances in machine learning are improving equipment condition predictions, enabling more efficient operation. Beyond predicting maintenance needs and parts replacement, the field data can also be
Published in PLM
The theme for CIMdata’s 2019 PLM Market & Industry Forum series is “Augmented Intelligence: Applications Across the Product Lifecycle.” CIMdata believes that the work done to date on this topic barely scratches the surface of what is possible. With this in mind let’s take a look at how an augmented intelligence capability should be developed for product lifecycle management (PLM) as part of the enterprise digital transformation journey. CIMdata defines PLM as a strategic business approach that involves orchestrating the creation, maintenance, and reuse of intellectual assets, resulting from the innovative efforts of manufacturers, suppliers, and startups. These intellectual assets often engender large volumes of heterogenous data, leading to the challenge of storage, communication, analysis, reporting, as well as, extracting intelligence from the data. Due to increased connectivity, driven largely by the Internet of Things (IoT), between the products and the enterprise systems used to develop them, the data volumes
Published in PLM
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