1 Introduction to digital twins

Digital twins, virtual models of physical systems that continuously synchronize with real-world data to monitor current conditions and predict future behavior, have existed for decades. One of the earliest examples of a digital twin was NASA’s use of simulators of the Apollo lunar and command modules. But until recently, they’ve been largely inaccessible because the sensors, powerful computing capacity, and advanced analytics required to build them have been scarce, expensive, and only accessible to large, well-funded organizations. That’s all changed.

Today, digital twins can be built by almost anybody, with an array of low-cost sensors, high-performance, pay-as-you-go computing, and powerful artificial intelligence and machine learning tools widely available.

1.1 What is a digital twin?

1.2 Technology enabling digital twins

1.2.1 Internet of things (IoT)

1.2.2 Cloud computing

1.2.3 Artificial intelligence (AI) and machine learning (ML)

1.3 Categories of digital twins

1.3.1 Descriptive digital twin

1.3.2 Informative digital twin

1.3.3 Predictive digital twin

1.3.4 Comprehensive digital twin

1.3.5 Autonomous digital twin

1.4 What are digital twins good for?

1.4.1 Accelerating product development

1.4.2 Reducing costs through predictive maintenance

1.4.3 Optimizing performance and operational efficiency

1.4.4 Supporting the full infrastructure asset lifecycle

1.4.5 Training and enablement

1.4.6 Digital twins across industries

1.5 Building your first digital twin: key considerations and challenges

1.5.1 A clear definition of success

1.5.2 Data quality

1.5.3 Skills gap

1.5.4 Build vs. buy

1.6 Summary