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.
Below are some examples of Engineering Analytics services mapped to various industries.
|Aerospace & Aviation||Predictive analytics|
|Rail Transportation||Descriptive Analytics|
|Heavy equipment||Diagnostic analytics|
|Medical devices & Electronics||Data visualization|
|Oil and Gas||Industrial Internet|
|Internet of things|
|Machine to Machine|
|Aero Engines||Engine Health Monitoring|
|Engine Diagnostic Assessment|
|Component Life Assessment|
|Data driven Engine Design|
Engineering Analytics are found to be more pervasive in the Energy industry with a spend of $1.4 billion as well as have the largest addressable market for Engineering Analytics service providers. Some of the key Engineering Analytics services that can be outsourced include:
In the energy industry, predictive modelling and analytics will help in the development of next-gen system planning & operations tools and controls to understand system dynamics, earmark areas prone to fallibility, prevent and mitigate subsequent fall-out events, and support analyses after the incident.
The instability of renewable energy sources owing to factors such as wind speed and solar availability restrict their usage to just about 60-80%. Accurate energy production forecasts of such renewable energy power plants are possible through predictive modelling and analytics. By predicting equipment failure and downtime, it is therefore possible to optimize overall operational efficiencies. Such diagnostic analytics also improve the life of these valuable renewable energy equipment.
In the Oil and Gas industry, predictive modelling and decision optimization is useful across upstream, midstream and downstream operations. In Upstream, predictive modelling enables asset maintenance, optimization in drilling/exploration/production optimization, risk assessment, etc,.
Outcomes of predictive modelling and analytics include better planning of maintenance activity, using insights from intelligent analysis of data and then feeding that back into design and reduced service disruption. All these translate to huge cost savings and reduction and improved operational efficiency.
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