Digital Twin for Product Lifecycle Management (PLM) (Part-1): Bridging the gap through Twinning
Today we are all surrounded by material things - from a coffee machine to start the day, to a bike or car to travel to work, a computer or photocopier at work, work-out equipment at the gym to a smart TV on a smart couch to end the day. When any of these break down you feel helpless.
Over centuries physical products, predominantly mechanical, have been systematically built and used by all. Today’s products are much smarter than previous years. They are packed with electronics and software but we still take them for granted.
Some smart products are able to let you know about their health or even inform their manufacturers about potential maintenance and fixes needed. But most of the time, products once sold do not communicate back to their producers about performance, although the customer might if it breaks down.
Don’t you think product designers want to know if the features really fulfill the end-user’s requirements? How else would a designer be able to improve and innovate better? Wouldn’t it be brilliant if smart products could beam feedback back to designers to help provide impeccable end-user services?
“Digital Twin for PLM” provides the answer by leveraging smart products or connected sensors. With sensors becoming very versatile and affordable, we are able to stream data back about a product’s usage along with information about the external conditions under which a product malfunctioned.
Today digital models carry much more information than we can imagine. It would not be inappropriate to state that a digital twin of the physical product is almost an indistinguishable virtual representation: identical in its geometry, kinematics (1D) and even 3D modelling movement manifesting in the same way to when external factors are applied. To realize “Digital Twin for PLM” we need the following:
- a) Physical products in real space
- b) Virtual products represented in digital space
- c) The connecting information flow between virtual and physical products in a common context.
Single significant difference between managing human twins and product twins are that in the first case human twins tend to learn both good and bad practices between themselves, whereas the latter can be trained to learn constructively in a closed loop product lifecycle.
There’s space for optimism about this technology but it’s early days. Realizing a true Digital Twin would take more than just plumbing of IT systems. Digital continuity within product processes is the first step, followed by establishing an ontology between systems, products and processes. This needs diverse smart minds such as business experts, engineers, IT professionals, data scientists and even mathematicians to come together to help realize twinning successes.
Success in this area would bring many benefits: knowledge capture and best practice, reduced time and cost of product development, improved product services and increased customer confidence, satisfaction, and loyalty. Moving to the advanced level of digital transformation maturity, the product owners will be able to realize regenerative product design or predict product performance using neural-network based analysis and artificial-intelligence. The ultimate application of “Digital Twin for PLM” would come from the use of smart and nanomaterial. When these are used in a product, they are designed to react to corrective input signals remotely to change a product’s physical form and behavior.
Different industries will decide for themselves where the low hanging fruit is for quick success. We will continue to engage on this topic with the industry to respond at pace it demands. Until you hear from us again, happy twinning!