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Engineering Analytics - Data Is The Key To Success

Engineering Analytics - Data Is The Key To Success
May 17, 2017

Co- Authored By: Divya Thiruvenkada Krishnan

For ages, engineering analytics has been a game changer for many industries. The time has come to reinvent engineering research and development analytics once again to carve out a new future for us.

The data gathered is enormous. It is high time to act upon the data – building stable systems with strict measures to improve quality. Engineering analytics is a field which has influenced almost all industry verticals including software, automotive, health care, manufacturing, and aerospace. With analytics comes a huge expanse of data and the need for engineering analytics in various businesses to accumulate large data sets – big data.

Data mining and Data warehousing are now replaced by the term ‘Big Data analytics’. Data mining and Data warehousing have been prevalent for a while, but with data growing at a faster rate from GBs to PBs, analytics with the existing concepts has become fragile and slow. This is where Big Data analytics can be leveraged, in order to effectively action engineering analytics.

Let us try to understand what engineering analytics is in layman terms. Consider a live cricket telecast. In the past, audio-visuals detailed the current match and its forecast. But nowadays, numerous trackers, score comparisons, performance comparisons are being telecasted during a live feed. In such situations where a huge amount of data needs to be analyzed and presented in a more understandable and relevant manner, engineering analytics can be utilized.

Engineering analytics is dependent on various sources of data – machine data, machine learning data, enterprise data, transaction data, and social data among others. Devices/machines/software collect raw data. Engineering analytics is not possible without raw data. Companies can be successful only when they value and understand data. Data analysis falls under two categories – well defined and not-defined. Well defined data, though massive can easily be extracted using engineering analysis through streamlined logging and categorization. The data should be readable, process-able. A not-defined data is of no use, as problem identification is not possible due to cumbersome logging.

Multiple companies have invested in engineering analytics in the later stages of development/post sales. The data collected from the devices/products in the field enables them to identify/isolate the problem, provide proactive fixes, invest learnings in the future products, rectify mistakes, reduce product costs, and reduce time to market. Mostly, engineering analytics is utilized once the product/machine is launched and it reaches the customers. However, this practice is now undergoing changes.

Now, engineering analytics is deployed right from the development stage. Engineering analytics helps in implementing CAE, CAD, 1D, and 3D simulation techniques which aid in constructing complex systems. Building prototypes would otherwise cost much more.

These tools not only aid in creating virtual prototypes but also enables a proof of concept to be built in real world with faster development time.

Real-time monitoring, also known as predictive engineering analytics, is the need of the hour.

This is possible only when data is valued and utilized right from scratch.

HCL Engineering Research and Development Services team (HCL ERS) encompasses engineering, mechanical, electrical, electronic, and hardware teams who work in liaison with the latest trends in engineering analytics right from the development stage. The mechanical experts build virtual prototypes by harnessing tools like CAE, CAD, and simulation.

Several technical papers and patents from HCL are generated towards the engineering analytics tools front and its optimization is absorbed into the projects. Sustenance teams work towards providing numerous cost saving prototype decisions with the help of tools. The try-out from sustenance teams benefits the customer in cost and effort savings and enables faster delivery by running reliability in the prototypes.

HCL ERS has obtained several contracts with leading global giants and Silicon Valley companies for their IT growth. The HCL ERS team is equipped with 21,000+ engineers, helping customers launch over 1000 products over the last ten years.

REFERENCES:

https://en.wikipedia.org/wiki/Predictive_engineering_analytics

https://en.wikipedia.org/wiki/Big_data

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