In product design it is widely accepted that you learn more through failure. Generally this is because on balance we spend more time on the failing part of a project (the design & development) than we do on the creation element. This is particularly true when you consider innovative and market breakthrough products.
In engineering terms, many of the engineering systems we take for granted today came from attempts to eradicate catastrophic failure (e.g. The Challenger Shuttle, Tacoma Narrows Bridge). Organisations like NASA are able to call on leading scientific, theoretical and advanced engineering resources to identify and correct root cause issues leading to the creation of from clean slate and innovative approaches to better engineering. Engineering systems with the design intent to eradicate failure are vital, they rely heavily on tried and tested processes and masses of learned information and experience.
In the development process, if you know (through a previous experience) a certain design choice will lead to failure, or cannot be economically manufactured, you don’t take it. That’s logical, however, taking into account modern day advances in materials, manufacturing processes and lifelike simulation it could mean that accepting previous truths, an experience of something that doesn’t work, may no longer produce a valid outcome and could eliminate the chance of a new, more advantageous line of development.
Intelligent Engineering can be used to maintain the value of Intellectual Property by developing automated processes that keep information accessible and are able to re-apply previous design questions. Previously correct conclusions can then be challenged against movements of the goal posts (new materials and methods) to be quickly verified by state of the art, life-like, simulation.
The advantage of an Intelligent Engineering solution would be that it is able to maintain the value of that information over time as engineering moves forward and the knowledge of the business grows (New techniques, regulatory and environmental constraints, materials and Disruptive Technology – Additive Manufacturing, The Internet of things and Big Data). Considering the man-hour value of development, keeping information current and being able to re-consider previous product development cul-de-sacs is just one element of best practice.