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  • Digital twin has emerged as a critical technology that can be applied in the early stages of product development.
  • It can predict future system performance and can be used to build a digital thread using data from connected smart products.
  • Digital twins reduce the times engineering teams spend on iterating a product, which in turn decreases the overall costs in the product life cycle.




As novices in the design engineering space, it was not unusual for us to be plagued by challenges like disconnected historical product data, lack of boundary conditions, frequently changing global scenarios, fierce competition, and complex product structures. These challenges, alongside the electrification of mechanical products, ever-changing safety and emissions regulations, and time to market, made for a rocky start to our careers as junior engineers. 

A retrospective analysis convinces us that much has remained unchanged despite the global advancements in technology. The chaos engendered by the pandemic, too, has compelled us to reconsider our approach to problem-solving. Now that we've sensed the problem and perceived the ecology around us, how will we act upon it?



The goal of the design and development process is to transition from a traditional knowledge-based model to a data-driven one, resulting in a stronger relationship among modeling methodologies, simulation software, and operational data. But will technology support design engineers and alleviate their pain? 

Trends indicate that the technological disruptions we see in the digital world will impact and alter the laws of engineering, manufacturing, operations, and service. Intelligent and connected products will provide continuous and real-time engineering data while deeper insights into the generated data will aid in the cognitive contextualization of information. By adjusting key performance indicators (KPIs), a product's automated decision-making capabilities will allow it to adapt to a changing environment and remain resilient for optimum performance.




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.


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.


The advantages of digital twin are many for manufacturers. It allows firms to drive strategic and operational decisions at all levels. Digital twins reduce the time to market for new products by lowering the time an enterprise spends on iterating a product and testing it. This in turn decreases the overall costs in the product development life cycle. Besides, the data generated in creating a new product can be used to create value-added services for an enterprise's customers and it can further be contextualized across the firm's value chain. Overall, digital twins enhance an organization's brand value with customers and, in turn, performance expectations and delivery.


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