Digital Transformation Issue 22
David Telford, Vice President of Innovation
Here we are at the end of the year already. This year has flown by. Too much to read and listen to and write, as well as doing the day-to-day things that work and life demand. Have you ever wished there were two of you? I do, sometimes, but then I think about another me getting into as much trouble as the original me, and I kind of think that would be a bad thing to wish upon the world.
Still, having a way to get more done, or to at least quickly sort out the things that should be done from the pile of things that want to get done would be a great start. I have usually been pretty good at looking at the 50 ways to get from A to B and then quickly reduce the choices to 2 or 3 that will work with the least amount effort. If I could capture that in a digital assistant, that would be slick. Digital Dave.
Tying this back to Gartner’s concept of “ContinuousNext,” the concept of the Digital Twin is becoming very popular. A Digital Twin is, unfortunately, not a robotic version oneself, but rather a digital representation of a physical thing that can be used to monitor that thing digitally and apply different types of stress and usage models to optimize it. Once upon a time, back when the world was young, I was working the injection molding industry and helped install a piece of software that would simulate how the molding process works so we could train press operators without the risk of giant globs of hot plastic flying through the air as they learned. That was a sort of Digital Twin although it was strictly for training and not for process optimization. Today’s version is more about applying IoT and Machine Learning to simulate real world conditions for actual process improvement.
As an example, Willow (www.willowinc.com) uses digital twins to model real buildings and uses an army of IoT sensors throughout the building to build a representation of the building in real-time. Using this they can model different aspects of the building like temperature, air quality, and other aspects of the internal environment to predict maintenance requirements and energy consumption. Imagine using a similar type of modeling to simulate your supply chain to plan for spikes in demand for your products, or the impact of losing a supplier during a hurricane or other natural disaster, or the best way to reroute your deliveries around high traffic scenarios.
Digital Twins take the best of IoT, analytics, and Machine Learning, and enable your planners to predict and avoid challenges to getting your products to your customers. At the same time, you can grab a real-time view of your assets and help plan preventative maintenance to keep things operating at top performance. You can then work with your suppliers and customers to build a network of Digital Twins whereby your supplier’s twin feeds information to yours, which you process and in turn send to your customers. Cross-industry twins require a good bit of planning and coordination within your own organization as well as the extended ecosystem of suppliers and customers. Everyone needs to agree on data ownership, governance, and flows, and be ready to adapt as things progress and members of the network update their technologies. If done correctly the results can be transformative.
This month I want to introduce a podcast channel from The Mission podcast group that has a number of very enlightening episodes. IT Visionaries brings CIOs from some of the biggest, and sometimes surprising, companies to talk about innovation. Great stuff.
Meet the Author