The Digital Twin Opportunity

Half a century ago, NASA’s moon shot landed Neil Armstrong and Buzz Aldrin on the moon and set fire to our imaginations. Technology innovations from the program went on to seed entire industries, including microelectronics, software, and communications, which now form the backbone of our digital century. Another innovation was that NASA built and maintained a physical twin of the spacecraft on the ground so that it could troubleshoot problems without risk to the mission. This proved crucial during the troubled Apollo 13 mission and helped NASA bring the astronauts home safely. This basic concept has now evolved into the use of digital twins, or DTs — still twins, but built and maintained in the digital rather than physical realm. Fundamentally, a DT is a dynamic model of a physical system that enables fast and creative experimentation at very low cost and risk.

DTs have already been used in specialized, complex applications like observing and modeling the operation of an aircraft engine or manufacturing equipment. These initial DT deployments were tactical, mainly for data visualization and product life cycle management. But now, thanks to a confluence of technological advances, DTs are at an intriguing inflection point — transitioning from that specialized, tactical domain to becoming strategic tools with diverse applications.

Leaders now have an inspiring opportunity to harness DTs for today’s moon shot: achieving business success while helping our planet and humanity. They can use DTs to strategize new cross-disciplinary opportunities and drive smart digital transformation. And they can use DTs to achieve aggressive sustainability goals and enhance the health and safety of their employees and communities.

How Digital Twins Are Advancing

What makes a DT special is that it is dynamic — it must always mirror the exact state of the physical system. This requires two key parts to work in tandem: a model describing the behavior of its physical twin, and sensors that provide the real-time “coupling” to the model. For example, a DT created to capture the occupant experience inside a building must reflect the temperature, humidity, air quality, and several other attributes of every room. Buildings, like all physical systems, are notoriously difficult to model because they change over time: Pipes rust, heating coils degrade, and beams weaken. Biological systems such as forests, fields, and humans change even more unpredictably.