“Digital twins are slowly entering mainstream use,” said Benoît Lheureux, research vice president at Gartner last year. “We predicted that by 2022, over two-thirds of companies that have implemented IoT will have deployed at least one digital twin in production.”
In the last few months, manufacturers in the automobile industry have indeed been developing smarter and better-connected products that use machine learning, machine-to-machine communication, and big data to optimize productivity.
As a relatively new approach to automotive design and maintenance, digital twinning entails creating digital replicas of physical products, systems, devices, and processes to bridge the virtual and physical worlds. This connection is established through the generation of real-time data using sensors.
Digital twins play a significant role in driving innovation and performance. Nonetheless, the costs of developing and maintaining digital twins must be driven by both business and economic models, as a Gartner article describes it. “Digital twins are not developed in a vacuum. Both the business concept and model must be tested against an economic architecture – revenue, profits, return on investment (ROI), cost optimization – and a way to measure progress as the products/services are rolling out.”
Tata Consultancy Services recently identified three ways in which digital twins can help with saving costs while rolling out innovative digital services.
Developing products is a long and intricate process. For instance, it takes up to 6 years to design and launch a new car model. There needs to be a seamless transition from the preceding model to the new model. A slight mistake during the process can undermine the brand's value and profitability.
A digital twin helps to integrate data between previous-generation models with the new concept in their digital formats. Twinning also enables seamless communication between product designers, end customers, and other stakeholders. When it comes to product testing, having a digital twin negates the need to wait for performance data gathered during vehicle trials to determine its performance and quality.
When automotive brands introduce new car models, they unsettle their current assembly schedule. Digital twinning can go a long way in minimizing the impact of these disruptions. Before spending money to improve your production capacity, a digital twin allows you to simulate how your schedules will be affected based on the demands and characteristics of the new products.
A well-executed digital twinning strategy helps you optimize process control and resource management. You'll be able to gather and analyze more data in a virtual context, thus enhancing your decision-making capacity. Furthermore, most automotive manufacturing plants face the challenge of skilled labor shortage.
Digital twinning can help you to overcome this challenge by enabling large deployments in workforce training, besides providing real-time guidance on critical tasks such as component design and product assembly. Machine downtime during automotive manufacturing negatively affects productivity. Digital twinning can help mitigate this challenge by helping you to predict when failure will occur on a machine and how to avoid such events.
In the coming years, car ownership will be highly customized. Owners will prefer paying for specific features of a vehicle rather than paying for the entire vehicle up front. Digital twins will provide real-time field insights that highlight particular features that customers prefer and use. This will provide a richer and more customized customer experience.
In today's used-car markets, consumers are always forced to guess the condition of their vehicles. This will soon be a thing of the past since digital twins can help owners to track their vehicles' service history, and when parts need to get changed. Likewise, they'll also be able to track warranty data.
To build a reliable and successful digital twin, several challenges around data like connectivity, security, scalability, availability, role-based access control, access to real-time data and long-term storage must be addressed.
The open source streaming processing platform Apache Kafka is designed to help developers address these challenges. It facilitates the building of digital twins by enabling the scalable and reliable integration of Kafka with other systems. It provides sinks and sources to:
While Kafka is a trending publish-subscribe style API that’s fast, reliable, and scalable, it’s not great for externalization/publication and requires plenty of Java prowess.
Xapix’s core is built on top of Kafka but adds other features like advanced API monitoring, data standardization and allows to seamlessly connect your data even for non-experts with a low code approach. Are you interested in learning more about using Xapix to get started with digital twins? Let's get in touch!