Davra Storms MQ
Although it sounds like the sheer definition of a cool band name, a digital twin is actually a crucial business asset for companies that use the IoT. What you might not know is that failing to use them properly could render your job infinitely harder.
Why do your connected applications need these tools so direly? If industry visionaries like Gartner repeatedly cite digital twins as critical IoT performance enhancers, then there must be something to them. Of course, your organization doesn’t thrive on opinions alone. You need more concrete evidence.
Are digital twinning dev workflows going to help your applications save money and establish your firm as a major Internet of Things presence? Here’s why it shouldn’t take much convincing to get your entire team on board with the digital twin practice.
What Is a Digital Twin?
A digital twin is a simulation that attempts to replicate a real-world environment. For instance, bulk freight shippers, cruise operators and other maritime enterprises use twinning to gauge the performance of massive vessels before investing millions in laying down keels or making voyages. By simulating their assets in virtual space, enterprises can:
• Gain more operating data about routine and abnormal situations
• Confirm that components won’t interfere with each other during normal procedures
• Analyze how configuration differences and operating choices translate to key performance indicators
• Reduce lag between project conceptualization and implementation
• Run trials of loss events, such as disasters, to learn how your systems might respond to the real thing
• Identify the most viable strategies for real-life pilot tests
The key distinction between digital twins and other traditional simulations is that twins continuously work to replicate the original. As opposed to making informed estimates of how faux versions should run, they combine actual operating data with hardware models to expose core functional ties. By using actual IoT sensor data streams, they let you try new things without wondering whether your practice runs will prove true to life.
Simulate Every Operation Before You Buy
One of the coolest aspects of digital twinning is that there are no bounds to what you can test. You can create a twin for any asset, product or process. For instance, your healthcare facility might use smart bandages to gather data from individual inpatients and stay ahead of their prognoses.
The programmatic nature of digital twins imposes few constraints on your virtual trials. As long as you can model something mathematically, you can create a simulacrum that helps you master its nuances. Even better, modern programming frameworks make it simple to scale up. For instance, why use a lone model’s performance to extrapolate how your delivery drivers might behave when you can run a digital reconstruction of the entire fleet?
Could Digital Twins Give Your IoT Enhanced Business Impact?
Picture the following scenario:
You’re running a company that makes custom prosthetic limbs. To improve consistency and regulatory compliance, you’ve cut humans out of most aspects of the production — leaving it up to your smart factory instead.
Before creating your first batch of consumer-ready medical devices, you’ll probably want to build and test a few sample products. Each prosthetic that you fabricate costs money, however, so you’d rather not subject yourself to the grueling process of trial and error.
Digital twins come in handy because you can iterate and tweak tests as many times as it takes to get optimal, actionable results. In this case, your prosthetic factory might experiment with a range of designs and materials to lower product costs, reduce weight or provide other attractive features. Instead of merely guessing whether your business machinery will survive the transition to a new operating model, you can take its twin for a virtual test drive.
Why Enterprises Without Digital Twins Struggle With the IoT
In systems theory, emergence describes what happens when complicated frameworks exhibit behaviors and properties that their components lack. As different parts interact, they may produce completely unexpected outcomes. While this phenomenon yields some exciting computer science riddles, it doesn’t always bode well for organizations that have stakeholders breathing down their collective necks.
The Unexpected Isn’t Exactly Good Business
Emergence is especially problematic in the Internet of Things. Factors such as strict real-time data transmission requirements, network service quality fluctuations and poor silo management practices can easily combine to give birth to less-than-ideal behaviors in critical systems. Although there’s certainly an advantage to the flexibility and scalability with which emergent behaviors respond to typical IoT stressors, you shouldn’t count on things to work themselves out magically. For instance, you wouldn’t want to wait until a major storm to discover that your flying crop-monitoring drones tended to flock dangerously close together.
Predicting Impossibly Complex Behaviors
Every enterprise IoT implementation is unique. This reality significantly increases the unpredictability of delivering and maintaining business applications.
Digital twins hold the tantalizing answers to many of these utterly confounding problems. Want to change how your company’s autonomous delivery vehicles route themselves to lower collision risks in transit? Simulation frameworks let you run thousands of tests and use the data they generate to retrain the algorithms in your actual drones. Curious how well your shipping and receiving IoT architecture might fare after more facilities start vying for network resources? Digital twinning abstracts complex systems of systems, making it simpler to put the kibosh on surprises.
Integrate Twins Into Your IoT Lifecycle
As IoT assets go through their lifecycles, engaging in proactive state modeling does more than merely help you predict outcomes. The practice can also explain outliers and expose trends, such as why certain parts wear down faster than others. Because they’re living, evolving models, twins help you maintain an accurate image of the best- and worst-case operating states. They also retain their relevance when confronted with changing constraints.
Digital twinning is a prime example of an easy-to-overlook preparatory step that shells out dividends in droves later. For instance, digital twins make perfect venues for preconfiguring advanced IoT elements, such as service quality monitoring routines and connectivity analysis algorithms. By planning and testing such integrations beforehand, you can dramatically minimize lead times. What’s more, all the subscriber needs to do is enable the features that you’ve already programmed in.
Hold a Clearer Mirror up to Your IIoT
Digital twinning doesn’t work without comprehensive support systems. For accuracy’s sake, you need a system that securely exposes as much data as possible and helps you sanitize it for your simulation’s consumption.
Digital twins demand continuous updates. It ultimately doesn’t matter whether you’re trying to learn from past failures or draw insights from an exemplary stage of the product lifecycle before implementing a new manufacturing strategy. No matter what you’re up to, your distributed computing backbone must be tough enough to simulate, improve and deploy apps in record time.
Can you afford to leave your stakeholders waiting for viable IoT solutions? With the Davra platform, you never have to because creating digital twins is as easy as building architectures that you once thought impossible. Learn how a healthy IoT ecosystem looks before betting your future on one. Talk to our team about getting your app tested and running in mere weeks.
Brian McGlynn, Davra, COO
Davra Storms MQ
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