1 Bridging the physical and digital worlds
This chapter covers
- Defining what a digital twin is
- Different levels of digital twin maturity
- What digital twins are good for and when not to consider them
- How digital twins are used across industries
- Considerations when embarking on building a digital twin
Digital twins are virtual representations of physical systems connected to real-world data, enabling them to reflect current conditions and inform predictions about future behavior. The concept builds on decades of simulation and systems modeling, including work at NASA during the Apollo program, although the term itself emerged later, most notably through work in product lifecycle management in the early 2000s. What distinguishes digital twins from these earlier precedents is their persistent, operational data linkage to the physical system they represent, allowing continuous synchronization rather than one-time or periodic simulation.
For years, digital twins remained accessible only to large, well-funded organizations that could afford the scarce, expensive sensors, computing capacity, and advanced analytics required to build them. 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.