Analytics: a powerful new tool for engineering
Today’s manufacturing companies are amassing plentiful data without unlocking its full value. In this post, I’ll explore why your data might not be working as hard as it could and reveal how data analytics opens up novel opportunities for improving products and processes.
Valuable insights locked inside
Today’s manufacturing companies are producing a wider variety and greater volume of data than ever before. During product development, for instance, companies capture design data. During testing, their analysis of the mechanical, electrical, structural, thermal and other properties of the product produces more data. During manufacturing, they capture process and machine parameters and more.
And whereas previously companies captured only a small amount of data during the operational life of their products; nowadays, smart and connected products generate data via their embedded electronics and sensors.
Locked with these growing datasets lie invaluable insights that could help manufacturing companies not only create even better products but also create them more rapidly and at a lower cost. But before engineers can unlock the insights captured within the vast volumes of raw data, they must reduce it to a manageable volume.
Furthermore, while companies generate a lot of data, it’s typically not shared outside of departments or combined to provide a complete picture. Departments use that data in isolation. Even the PLM programs that manage the entire product development do not share enough and relevant data outside a specific development phase.
Making data work harder
So, how can companies make their data work harder?
Firstly, by building a comprehensive dataset that provides an end-to-end view of the entire product lifecycle. This comprehensive picture will ease the transition as the product flows from conceptualization to design, testing, manufacturing, and then operational use.
Secondly, manufacturing companies can learn from those sectors that have already established how best to capture, organize and mine large volumes of data. The Technology and Telecoms sectors, for instance, have a great deal of experience in leveraging the powerful methodologies and advanced analytics solutions.
Let’s look at some real-life examples – in the aerospace sector.
When stress engineers analyze an aircraft, the finite element models they use are often only capable of interpreting a subset of the available data. By using advanced analytics, engineers can explore the entire dataset to glean a deeper understanding of how various parts behave. They can even discover structural performance patterns not revealed by classical analyses.
Also, analyzing all available data will help design engineers improve the next generation of the aircraft. The data analyzed by other teams gives them a better understanding of how the aircraft’s components behave while operational data reveals how customers actually use their products, for instance.
Don’t go it alone
Adopting this new approach won’t be easy, but the benefits available will be worth it. As well as technological and methodology changes, maximizing the value from today’s data requires organizational and cultural shifts.
Other sectors have shown the journey is achievable but requires a new way of thinking alongside new tools, time and experience. Expert companies, such as digital leaders and big data companies, can guide them.
After all, manufacturing’s future will be built on data and analytics. Modern ‘design of experiments’ and ‘generative design’ approaches use data analytics, machine learning and optimization simulations. They require a rich and wide variety of data from all phases of the product lifecycle. What’s more, tomorrow’s ‘virtual twin’ product simulators will require huge quantities and a wide variety of data to help them mimic the real world.
As manufacturing enters a new era driven by data, are you making your data work hard enough? Are you ready for a future built on analytics?