THE CHANGING ROLE OF DIGITAL TWIN IN PRODUCT DEVELOPMENT
While several new digital technologies and platforms are emerging in the engineering ecosystem, the digital twin has enormous potential for resolving industrial difficulties. Though it is underutilized, according to Research and Markets, digital twin will be commonly used in internet of things platforms.
In the early 2000s, digital twins emerged as a theoretical model for real-time monitoring. Many researchers and academics in the manufacturing industry adopted the buzzword to sell their existing products and services. Over the previous half-decade, the industry has demonstrated a paradigm shift toward the digital twin approach, emphasizing its practical applications and ability to create value through real-time monitoring, modeling, and forecasting.
However, the rapidly changing post-pandemic world has fueled a fundamental question on digital twins: Can the technology go beyond the realm of physical products? Increasingly, manufacturers have realized that digital twins can be applied to the early stage of product development. Therefore, digitally connected organizations are hunting for new opportunities to create digital twins beyond the spheres of manufacturing and services.
IMPACT OF DIGITAL TWINS ACROSS THE ENTERPRISE
Recent discussions on design and development have explored moving the process away from a conventional, knowledge-based model to a data-driven development one – based on digital thread and digital twins. Before a real-world asset is developed, many digital twin scavengers create solutions that combine virtual machine data with 3D simulations and analytical models to identify ergonomics and forecast product behavior in various environments. A European automotive company witnessed a 30% reduction in its engine development thanks to the adoption of digital technologies.
Digital twin capabilities in product design, development, and validation can be conceptualized at the unit, sub-system, and systems level in the model-based systems engineering (MBSE) environment. Listed below are the various ways digital twins help manufacturers develop new products:
• Create new products and identify use cases through what-if scenarios or designs of experiment (DOE).
• Predict mechanical and structural breakdowns and build multivariate time series models using advanced analytics during product development.
• Run what-if scenarios to design optimized parts or products.
• Use predictive analytics to identify hidden patterns and build a sophisticated knowledge repository that predicts future system performance.
• Enable decision-making and build a digital thread using previously siloed data from smart and connected products.
• Allow for robust decision-making using artificial intelligence (AI), machine learning (ML), and advanced analytics.