Is Uber Actually a Space Company? The Discussion at AIAA SciTech

While attending the AIAA SciTech aerospace event in January I was surprised when the discussion turned to Uber as a space company. Seriously? I understand that Uber is revolutionizing the business model for transporting people, but I thought it was purely terrestrial.

Even though this statement was said somewhat tongue in cheek, with a stretch of the imagination you can see how it can be argued that Uber’s business model is predicated on monetizing data — GPS in this case — that is a product of the space industry. From this follows the (somewhat tenuous) proposition that Uber should be considered a part of this industry.

Whether you agree with this argument or not, the underlying message is that, in many cases, disrupting industry requires making use of, and ultimately monetizing data. One of the reasons this is becoming so relevant today is the emergence of the Internet of Things, the Industrial Internet and Industry 4.0, in which the cost models and capabilities of sensors, computation and infrastructure have converged to make connectivity between people and assets more possible than ever before.

Industry leaders and new entrants to the aerospace industry are already seizing this opportunity with business models that are shifting from delivering a product, such as a jet engine, to delivering an outcome, such as flight hours or thrust. This shift passes the challenge of performance optimization and maintenance from the customer to the supplier. And this is where data, or rather actionable intelligence derived from that data, becomes critical.

industrial internet digital twins aiaa scitech 2017To get a better understanding of what this really means in practice, I was fortunate to be able to talk to John Magee, Chief Marketing Officer at GE Digital, about how this concept of transforming data to actionable intelligence through the Industrial Internet is having an impact on GE’s business today and how he sees this transforming the future. You can read the full interview here.

One of the key points John makes is that by collecting in one place all the digital information about an asset, like a specific jet engine, engineers can create a “digital twin” of that asset. This opens a whole new opportunity for performance improvement, maintenance optimization and accelerated new product insertion. According to John:

“The secret sauce to actually achieving innovation is to be able to marry the physics-based models with statistical and machine learning approaches.”

By doing this, GE is already delivering substantial results to their customers.

Those of you who followed the recent launch of ANSYS 18 are aware that we are already supporting our customers as they pivot to the digital twin. Digital twins can help you optimize the operations and maintenance of your critical machines and components by using engineering simulation to predict a possible problem well in advance, and schedule downtime for repair — always less expensive than unplanned downtime. And preventive maintenance can extend the working lifetime of every machine you produce or operate.

Disruptive technologies like digital twins can blur the lines between what we thought were well-established boundaries of particular industries. So, the next time someone tells you a cab company is part of the space industry, maybe they are onto something.

2 thoughts on “Is Uber Actually a Space Company? The Discussion at AIAA SciTech

  1. Interesting post Rob. We have been exhibiting at the Avalon Airshow this week in Australia and Digital Twin is certainly a hot topic in the aerospace industry. I agree the timing is now right for the concept of Digital Twin: made possible by the convergence of multiple trends encompassing big data from pervasive sensors / Iota, data analytics and IoT platforms such as Thingworx, and ongoing advances in hardware (esp. cloud) & software allowing for high-fidelity multiphysics simulation combined with system-level models. Wrap this all up and what do you get? More accurate input data for simulations, and the ability to simulate the history of individual assets, giving us the ability to both design better products but also make smarter decisions about these assets downstream (opening up a whole new breed of engineers – eg. in maintenance scheduling – who can now benefit from tools such as ANSYS).

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