Predictive engineering analytics (PEA) is a development approach for the manufacturing industry that helps with the design of complex products (for example, products that include smart systems). It concerns the introduction of new software tools, the integration between those, and a refinement of simulation and testing processes to improve collaboration between analysis teams that handle different applications. This is combined with intelligent reporting and data analytics. The objective is to let simulation drive the design, to predict product behavior rather than to react on issues which may arise, and to install a process that lets design continue after product delivery
Source: https://en.wikipedia.org/wiki/Predictive_engineering_analytics
Exploding data in the engineering space has made Engineering Analytics an imperative across various industries. Engineering Analytics is likely to benefit companies to the tune of $250 billion and this is expected to double to $500 billion by 2017. The current spend on Engineering Analytics product engineering, analytics and system integration is close to $13 billion. Services that enable the planning and deployment of engineering analytic solutions go by as Engineering Analytics services and from a $5.4 billion, it is expected to triple by 2017 to $14.8 billion.
Engineering Analytics are being adopted by OEMs and suppliers in three key areas of their operations: design, manufacture and after-sales support.
In design, all OEM’s have huge clusters of high power computing, which are used in the design process and generate terabytes of data on each simulation on their products. There is a need to visualize and tease out design patterns from this data. This will decrease development time and improves the quality and performance of their products.
As manufacturing systems increasingly network and communicate with each other in an Industrial Internet of Things (IIoT) environment, automatic measurement and quality control at factory level become a reality. Terabytes of manufacturing data produced will have to be analyzed and leveraged for decision making.
The next generation of aircraft will include engines that are permanently connected to a data centre allowing engineers to analyze and monitor the fleets and help diagnose faults, correcting them and preventing them from occurring again.
Source: https://analyticsindiamag.com/decision-making-predictable-can-get/