Product Designers - are you looking at data as you should?

Published by:
Dipankar Ghosh
23rd July 2017

As companies start thinking about connecting their machines to other machines and larger systems (read IoT!) they are probably focusing more on almost real-time decision making and autonomous action. However, there is also a huge goldmine that lies in the data that gets captured and stored by them and that can help them to better their product design and gain that extra competitive edge. Consider a commercial vehicle propeller shaft manufacturer who wants to study the stresses and strains that the spline and universal joints undergo in real life situation to predict the failure of these joints.

Now, this is a common use case – universally known as predictive maintenance and touted rightly as one of the big benefits of IoT. Based on data collected, patterns can be detected and with help of algorithms a fairly accurate prediction of when failure may happen can be made by the system and an alarm raised for repair/replacement. 

But, the utility of the data does not end there. Once you have long term failure data that correlates with data such as road/terrain condition, time of the year, geographical location, driver behavior, preventive maintenance history and so on, you have critical design inputs on how to improve the design of the shaft joints so that reliability improves, mean time to failure (MTTF) increases and thereby provide better value to the vehicle owner. Perhaps, eventually you may customize the design based on end use conditions to derive more value.

And now imagine what can happen when you obtain the same sort of data from your competitor’s product? Nothing prevents you from studying their product in the market in the same way. You then have a rich picture of how your product fares against theirs and why. 

All of this assumes two things: that you have the necessary IoT system incorporated in your product and system design; and that you start with the objective in mind of gathering data for design improvement and value engineering purposes.

To gain, as I said, that extra edge. Just do it, before your competition does!

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