“Digital twin” is one of the recent buzzwords in the manufacturing industry. Recently, Gartner named digital twin among the top 10 strategic technology trends for 2019 and beyond, considering its potential to understand, predict, and optimize industrial performance. Today, the digital twin has become one of the key criteria to measure your ROI in technology initiatives.
The Significance of Digital Twin in Manufacturing
A digital twin is the virtual representation of an actual product that can be used to validate how the product performs in the real world. The digital twin can receive continuous, real-time data through IoT sensors, which makes it capable of virtually monitoring the representation.
Among various applications of IoT in the manufacturing industry, digital twins contribute to improving situational awareness. Besides, by evaluating your product’s performance using a virtual replica, you can identify problems. Further, you can even experiment with innovations, test future scenarios, and simulate the results before implementing any changes to your actual product. The approach will enhance your product’s performance and proactively anticipate maintenance faults.
How Are Digital Twins Disrupting the Manufacturing Sector?
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Improve Cost Efficiency
When you implement a change in your operational environment, all your dispersed and connected machinery should be able to scale with the new situation. Experimenting with your real-world machinery can take a toll on your expenditure or even affect your existing operations. If you do not get a chance to test your solution’s ability to resolve a defect in your machinery, you will end up spending a lot of money.
By implementing a digital twin, any new operational change or issue resolution can be tested in a virtual environment. The virtual model will respond to you exactly the way it should, in a real-world scenario. This will help you gauge the viability of your new strategy while bringing down your costs phenomenally. The digital twin model enables you to ensure that only the best-fit solution is implemented in your manufacturing business rather than experimenting with multiple options.
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Enhance Product Design and Usability
Manufacturers are under constant pressure to make their products appealing to the masses. To meet the rising user demands, manufacturers have to redefine their products by altering the usability, design, look and feel, and the product’s response to end-user scenarios. But executing a design change on the actual product is not a wise idea as it can lead to unwanted overheads.
It’s possible to virtually test the design changes or customizations on the digital twin variant, which is an accurate representation of the original product. Deep data-driven modeling techniques allow you to analyze how the new design will integrate and respond to various end-user scenarios. This will reduce your time-to-market.
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Support Quality Assurance
The digital twin is a boon to manufacturers when it comes to quality assurance. It enables you to prevent quality issues from entering mainstream production. Using a mixture of data analytics and machine learning, digital twins can identify gaps in a product’s quality and point out the source of the issue. You can be sure that only the reliable product variant is going to your customers’ hands.
Digital twins can also predict future scenarios where quality issues may pop up and ensure that the stakeholders are always alerted to take necessary actions.
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Augment Staff Collaboration
The digital twin facilitates collaboration among different departments such as industrial designers, machine experts, process control associates, technicians, technology consultants, data scientists, market experts, and so on. You can trial the ideas suggested by everyone on the digital twin model and visualize the real-world results. In the final version of your product, you can include the best recommendations that will ensure a high rate of market acceptance.
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Accelerate New Product Launch and R&D
In manufacturing segments like automotive, it takes years to launch a new product. A lot of research and development effort goes into the design and production processes before the product is assembled in the production line of a factory.
If digital twins are used in the R&D process, the effort to design and test a product for the market conditions can be minimized. You can run intelligent machine learning techniques on the actual product and market data to derive insights on viable designs, geographical market propositions, potential target audience, and marketing campaign possibilities. This ensures successful market competence for new launches and accelerates your speed-to-market.
Digital Twinning Use Cases from the Real-World
Many key players in the industry are applying digital twin tech.
Manufacturing of next-generation autonomous vehicles is an exciting area where the digital twin is employed to evaluate and ensure the success of long-term autonomous transportation.
Tesla Motors, for instance, creates a digital twin of every car it sells. According to them, digital twin tech is one of their continuous sources of manufacturing insight.
GE has developed a digital twin model to augment their customers’ digital industrial transformation journeys. The company also reports significant savings in maintenance costs and a considerable increase in customers’ reliability.
Chevron is launching digital twin tech for its oil fields and refineries, expecting to save millions of dollars in maintenance costs.
Conclusion
The success of a digital twin journey depends on the ability of the manufacturers to demonstrate increasing enterprise value over time. These winning stories from industry leaders underscore why manufacturers must make use of innovations like digital twins. The most significant benefits are realized through innovations in product, process, and business model. Undoubtedly, digital twin tech is paving the way for disruptive innovation in manufacturing.
