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 used to animate digital twins, virtual representations of products defined and managed within product lifecycle management-enabling solutions. One aspect of defining a digital twin is determining what operating parameters are of most importance and then including the appropriate sensors and communications technology to gather field data. This data is processed in the context of digital twin definition to develop additional operational insights and suggesting maintenance actions. Furthermore, access to product data such as work instructions will allow technicians to be more effective in performing both predicted maintenance actions, as well as addressing unplanned events.
As mobile devices have grown in capability, field access to product data, including 3D models, has become more common. CIMdata expects significant improvements as mobile applications with product data access leverage audio and visual capabilities for identification and diagnostic use cases. These capabilities will aid in the execution of current maintenance activity as well as predictive maintenance.
Ultimately, algorithms that drive predictive maintenance will evolve to provide an input to an analytics-driven design process. As this capability evolves CIMdata expects the benefits of closing the maintenance loop with product development to provide ever better products that improve operational efficiency and customer satisfaction.
In the session on Predictive Maintenance–Benefits of Closing the Product Lifecycle Loop at CIMdata’s 2019 PLM Market & Industry Forum, CIMdata will show that ultimately, algorithms that drive predictive maintenance will evolve to provide an input to an analytics-driven design process. As this capability evolves CIMdata expects the benefits of closing the maintenance loop with product development to provide ever better products that improve operational efficiency and customer satisfaction.
2019 PLM Market & Industry Forum events will take place in Ann Arbor MI on April 4; Frankfurt, Germany on April 11; Pune, India on April 15; Beijing, China on April 19, and Tokyo, Japan on April 24.
Key takeaways from the discussion on this topic will include:
- Predictive maintenance is a high-value use case that leverages the Internet of Things (IoT) and digital twin solutions to improve profitability and customer satisfaction.
- Capturing the right data of the right quality in the right context is foundational to effective predictions whether using traditional analytics or machine learning technology.
- Beyond just predicting product failures, the data can be used in the actual repairs by leveraging digital twins and augmented reality.
- Transforming maintenance insights into product requirements that improve the customer experience is a high potential opportunity that closes the product lifecycle loop but few, if any, companies have such a solution in full production.
Let us know your thoughts on this topic by sharing them in the comments section of this blog!