Digital Twins and Manufacturing: Benefits, Uses, and Implementation

Technology is revolutionizing factories and changing how shop floors operate. As the manufacturing sector picks up steam and shakes off the post-pandemic hangover, industry leaders are looking for ways to manage either existing or potential spikes in demand. Digital twins technology is a way forward-thinking manufacturers are already implementing solutions to meet demand and enhance operations.

This resource defines what a digital twin is, considers use cases and benefits in a factory setting, and provides suggestions for ways to implement digital twin technology to improve productivity, cut costs, and accelerate innovation.

What Is a Digital Twin?

As the term implies, a digital twin is a virtual copy of a physical system.

Digital twins utilize real-time data by integrating into IoT-connected devices, augmented reality, and other smart factory databanks. They use this information to create both targeted and system-wide digital replicas depending on the situation. This can be at the scale of an individual product or asset, a factory, or an entire end-to-end ecosystem.

Digital twins function as a “what if” digital alternative to a physical asset or system. This makes it easier to test, experiment, analyze, and explore potential improvements in a factory or warehouse setting. It removes the need to invest in physical testing or risk resources while providing deeper insights that lead to more informed and confident decisions.

The Benefits of Digital Twins in a Manufacturing Setting

Using a digital twin can help with a variety of manufacturing activities. One example is accelerating R&D by improving the design of parts, the products they belong to, and even entire manufacturing systems.

A digital twin can streamline manufacturing activity across an ecosystem, as well. It can do this by optimizing internal processes and predictive maintenance. It can also help with external considerations by giving leaders insights into potential improvements for scheduling, routing, distribution, and supply chains. In the post-pandemic world, the ability to stay ahead of market fluctuations and maintain an agile response to unexpected developments is also a key factor in ongoing success.

All of these activities lead to greater productivity, more efficient processes, and lower costs across a manufacturer’s operation. They avoid unnecessary investments in developing solutions while giving manufacturers insights that lead to stronger decision-making.

What Do Digital Twins Look Like in Manufacturing?

While digital twin software can integrate into a variety of business scenarios, there are several unique applications of digital twins in a manufacturing setting:

• Optimization of Design and Layout: Factories consist of complex, nuanced networks and systems. Digital twins provide a detailed virtual visual of a facility’s design and layout. This can guide initial construction as well as identify bottlenecks, production restraints, and other inconsistencies or risks in existing operations.
• Simulations of Processes and Production Lines: Digital twins provide insights into how materials flow through a factory. This allows for a comprehensive analysis of entire production lines and their individual elements.
• Predictive Maintenance: Digital twins make it possible to simulate maintenance needs across a factory floor. This helps avoid unnecessary TCO expenses, like excessive operational downtime due to unexpected breakdowns or repairs.
• Quality Control and Assurance: Digital twins empower factory teams to create more consistent, reliable systems. This can come through standardized training, improved processes, and catching bottlenecks early.
• Supply Chain Management: Post-pandemic, leaders are concerned about stubborn inflation and rising material costs. Digital twins counteract this by pinpointing inefficiencies up and down a supply chain.

The multi-faceted interactions and systems within each factory make them an ideal area to integrate factory digital twins.

How to Implement Digital Twin Software in Manufacturing

If you are aware of the value of virtual replicas in improving and innovating but you aren’t sure how to start the adoption process, you aren’t alone. 86% of senior executives in the industrial sector are aware of the applicability of digital twins within their operations. However, just half that number have begun the process of implementing digital twins in their organization.

The first step in moving toward digital twin technology is setting the stage to get the most from the software once it is integrated into your system. Along with awareness of the technology’s capabilities, you must review your system for fragmented or cryptic data. This must be updated, organized, and optimized to align a digital twin program with your MES (manufacturing execution systems) and MOM (manufacturing operations management) processes.

Investing in full data compatibility creates a smoother integration process. It also improves the likelihood of digital twin adoption across your organization and makes it easier to scale in the future.

Talent considerations are also a factor. Consider your in-house personnel and their ability to build proof of concept digital twin models for your specific needs, transition these to real production environments, monitor outcomes, make improvements, and so on. If there are talent gaps, consider third-party solutions to support the digital twin adoption process.

Once you have the data and team in place, you can define objectives, identify which components of your system you wish to virtually replicate, select design tools, and establish data sources. You will then be ready to design, connect, and test the model.

Improving Factory Output With Digital Twin Technology

Digital twin technology is still in its infancy, and its application in factory settings is just beginning to generate positive results. The future of the technology will continue to hinge on the development of new artificial intelligence capabilities, as well. As these advance, they will create higher-functioning language models, which will, in turn, increase the capabilities of digital twin software to create more nuanced analyses of more intricate systems.

As factory leaders consider the potential and create timelines to implement digital twin technology, they must consider partners that understand the current and future implications of digital twin technology. 

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