Using this approach, one aerospace and defense player has cut the time required to develop advanced products by 30 to 40 percent. As a further benefit, the configurator helped the team reduce the time taken to reach agreement on changes by 20 percent, thus accelerating time to market.ĭigital twins are even being used to replicate systems in complex mission scenarios. Applying the approach to select customer-facing components has allowed the company to optimize costs and customer value simultaneously, improving the contribution margin of those parts by 5 to 10 percent. Using the configurator within cross-functional development teams has helped the OEM to reallocate 5 to 15 percent of a new vehicle’s material costs to the attributes that drive the most customer value. Digital twins in practiceĬompanies in many different industries are already capturing real value by applying digital twins to product development, manufacturing, and through-life support (exhibit). 1 Infinium MarketsandMarkets MarkNTel Advisors Meticulous Market Research Mordor Intelligence SBIS Technavio, last accessed April 2020. Current estimates indicate that the market for digital twins in Europe alone will be around €7 billion by 2025, with an annual growth rate of 30 to 45 percent. Digital-twin technology is becoming a significant industry. Digital twins can also be a critical enabler of new revenue streams, such as remote maintenance and support offerings and “as a service” business models.īased on the experience of companies that have already adopted the approach, we estimate that digital-twin technologies can drive a revenue increase of up to 10 percent, accelerate time to market by as much as 50 percent, and improve product quality by up to 25 percent. They can aid design optimization, reduce costs and time to market, and accelerate the organization’s response to new customer needs. In this article, we will focus on their application to products, specifically to product design.ĭigital twins offer multiple potential benefits for product-based companies and users. 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. 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. 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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