The digital-twin approach can be applied to products, manufacturing processes, or even entire value chains. A digital twin, by contrast, may have one model for each individual product, which is continually updated using data collected during the product’s life cycle. A conventional PLM system uses one digital model to represent each variant of a product. Digital twins combine and build upon existing digital engineering tools, incorporating additional data sources, adding advanced simulation and analytics capabilities, and establishing links to live data generated during the product’s manufacture and use.
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These changing requirements have triggered a transformation in digital product representation and the creation of a new tool: the digital twin. Increasingly, customers are not buying products outright, but paying for the capabilities they provide on a per-use or subscription basis. Many products operate as part of an ecosystem of related products and services.
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Customers expect the performance and functionality of products to improve during their life cycle, enabled by over-the-air software updates or the ability to unlock new features as needed. Advanced, adaptable user interfaces have simplified the operation of complex and sophisticated machines.Įvolving business models are also blurring the boundaries between design and use. Sensors and communications capabilities allow products to offer more features and to respond more effectively to changing operating conditions and user requirements. Product functions are increasingly delivered through a combination of hardware and software. Yet as engineering tools have become more capable, the demands placed upon them have also increased. This article is a collaborative effort by Mickael Brossard, Sebastien Chaigne, Jacomo Corbo, Bernhard Mühlreiter, and Jan Paul Stein, representing views from McKinsey’s Operations Practice.
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