Data driven design
Why is data-driven design important? Image: Subbotina Anna/Shutterstock

How to use data-driven design to make your products smarter

23 Oct 2017

Leveraging real-world data from the field can help you make better decisions about your products.

Previously, I have explained the need for and value of manufacturers investing in performance-based analysis. Rather than make assumptions about how your product will perform, you can leverage existing real-world data from the field.

However, another more advanced approach is needed to leverage data from a fleet of smart, connected products. This approach, called data-driven design, is essential for manufacturers who are designing or improving the next generation of smart products.

What is data-driven design?

Data-driven design enables a better understanding of existing products by connecting the design environment to a single product in test or in the field.

It is a must for organisations that have already laid the groundwork to understand their data and are now looking to advance their activities by leveraging the internet of things with smart, connected technology.

It helps you analyse a massive dataset using some degree of data analytics to simplify and correlate the data stream coming from the connected fleet or enterprise system(s).

Whether you use data-driven design to access real-world data from a group of connected products in the field, or other sources such as enterprise systems (eg PLM, ERP, CRM, MES), it can help improve product design, reliability and quality.

More specifically, it can position you to make more informed or even market-driven decisions to ensure that your next generation of products meets customers’ needs even better than today’s generation.

The value of fleet analysis

Because data-driven design is applied to an entire fleet of products, it makes it possible to analyse product data for trends that can impact design on a far-reaching scale. Let’s consider a few scenarios to bring this to life.

Imagine a manufacturer that monitors usage of its product in the field, and realises that some of its components are over-engineered for what it is actually used in, either globally or for a specific geographic area. For example, perhaps a dishwasher’s pump is proving too powerful in the majority of cases. The manufacturer can use this information to improve margins by changing the dishwasher design to include a less powerful, cheaper pump that reduces production costs.

Here’s a real-world scenario in the high-tech realm: HP Enterprise leverages failure information about its servers to drive information learned from the field back into product design. HP takes product failure trends into account as it is designing its next-generation products. By doing so, it has been able to realise a 15pc reduction in annual outage events for customers, and a more than 20pc reduction in average annual customer downtime.

Taking steps to advance to data-driven design

Manufacturers already taking advantage of performance-based analysis can graduate to data-driven design by embracing design for connectivity. With design for connectivity, they design their product from the start to collect needed data. The design accounts for the data that needs to flow from the system, be understood and acted upon. Data-driven design then analyses this data to identify product trends at scale.

Ready to capitalise on real-world data at scale for more efficient product design, better business decisions, improved product performance and more satisfied customers? Discover all the steps you need to take on your digital engineering transformational journey.

A version of this article originally appeared on the PTC website. Part one of this two-part series can be viewed here.

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