How Digital Twins Can Ensure Customer Satisfaction
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You’ve probably heard the phrase “They don’t build them like they used to,” referring to a supposed golden past where products were built to last and even overbuilt rather than skimp on some unknown stress or wear point. Given advances in lean manufacturing, total quality management, Six Sigma, and other techniques that allow the average automobile to easily last well beyond 100,000 miles, it’s possible that the good old days weren’t always as good as we remember.
It’s also possible that techniques such as finite element analysis, which allows designers to eliminate “wasted” materials and optimize for more streamlined shapes and designs, may also make newer products less resilient by due to the limitations of the model used. For example, while engineers may account for a certain amount of wear on one surface over its lifetime, there may be another supposedly wear-free surface that is not and ends up causing problems.
Physical prototypes can help mitigate these issues early on, but they are usually so expensive that only a limited number of physical prototypes can be produced and tested. Additionally, when a design is in the prototyping stage, a significant amount of time and money has already been invested. A better alternative, both from a development standpoint and ongoing customer support throughout the life of a product, is an ideally hyperrealistic digital model known as a “digital twin”.
Predictive customer service improvements
A digital twin is a digital representation of a product in which every aspect of an individual “thing” is known and tracked throughout its lifetime. In the case of a physical model, it starts with manufacturing. Although the tolerance for a certain type of product may be between X.XX and Y.YY, the measurement for that product is known to be exactly Z.ZZ. The individual digital twin based on this product, known as a “digital twin instance”, would know and store all relevant details about a product, even at the atomic level.
After manufacturing, sensor data from the digital twin is aggregated and tracked over time using IoT technology. With this data, one can not only know the current status of a product, but also go back and forth throughout the life of the product for troubleshooting and prediction purposes. Data from a single digital twin instance could also be aggregated with others for further enhancement and prediction information. With this massive cache of data in hand, a vendor could reach out to customers to schedule maintenance in a convenient timeframe, long before problems arise.
Using digital twins to perform this kind of precise preventive maintenance as needed, rather than periodic preventive maintenance performed on an unchanging and necessarily excessive schedule, means that customers are not paying for unnecessary service. At the same time, customers are not required to perform reactive maintenance, which “schedules” itself as failures occur. These monetary savings on maintenance, combined with products that perform consistently, means customers receive the value they expect from a product, with far fewer bad experiences due to equipment failure.
Virtual iteration before the first prototype
Physical prototypes are expensive and testing takes a long time. Even if you have a large budget for prototyping, it is impossible to know how a product will behave in 10, 20 and even 50 years. Accelerated testing techniques may provide an approximation, but ultimately this is all just an educated guess.
Creating a digital twin of a product that does not yet exist means testing and improvements can be done virtually for huge potential benefits. For example, due to the low cost of virtual testing, cars could be tested not only from front and side impacts, but also from any angle, even at different elevations. Improvements can then be made in the virtual world, iterating before a single physical product is made. Add artificial intelligence into the mix, and there’s potential for radical improvement in a design, likely guided by humans but powered by AI.
Virtual simulation of longer-term effects is no different. Capture the constraints and external conditions of a product as part of a digital twin and see the results. If something happens over two or 20 virtual years, it makes little difference to a computer. Computers can create their own time domains, independent of real time. External conditions are applied via simulation, which should probably be thoughtfully applied by a human, or perhaps in the future, by a well-developed AI.
One could consider digital twins as the next step up from 3D models and finite element analysis which have already brought countless benefits to engineers. If a design is made through a virtual iteration, a customer can get the equivalent of a vehicle with several model years behind it, where defects have been ironed out. It is profitable for the producer. And it results in better products. Of course, digital twin concepts can be applied to the manufacturing floor or even to an organization as a whole.
Useful today, potentially amazing future
Consider yourself traveling by jet. You definitely don’t want the engine to fail when you’re at 30,000 feet. But you also don’t want the engine to fail while you’re waiting at the airport, or even on your way to the airport. You want the problem to be “pre-detected” and “pre-fixed” well in advance of your trip. The airline also wants this to keep things running smoothly, and this scenario can be eased with improved tracking of digital twins. If the jet design has already been improved through a virtual digital twin iteration, some of the need for predictive maintenance could be avoided, along with a host of other benefits, such as reduced fuel burn and more customers. satisfied.
The digital twin is already here
Digital twin technology has been available for several years. Each F-35 Lightning II fighter, first fielded in 2015, has its own digital twin. Multimillion-dollar jets are kept at this level, and each Tesla comes with its own digital twin.
These digital twins are used both individually for maintenance and are aggregated to facilitate improvements and better and better service forecasts.
While it may seem far-fetched that computing technology may eventually get to the point where we can make and track elements at the atomic level, as mentioned earlier, consider the state of computing today. For a few dollars you can buy a processing unit that far exceeds what was used to put a man on the moon, in a size that can sit on your finger, that even comes with built-in wireless capabilities (eg. , an ESP32 module).
To imagine what our world will be like in the 2070s, put yourself in the shoes of an engineer transported from 1969 to today. If computer technology continues on the same trajectory we have seen so far, all bets are off as to what computers and digital twins can do.