Digital Twin and Mobel-Based Enterprise Technology; Doing What You Do Better!

To fully utilize the capabilities and advantages of digital twins, it is essential to comprehend their nature and applications throughout the operational cycle. As the name suggests, a digital twin is a virtual version of a physical object or system used to simulate its behavior and better understand its real-life operation.

Product digital twins are the most commonly used among the various types of digital twins. These enable product development teams to create a comprehensive digital replica of a product, even before its physical manifestation. Engineers can develop digital twins of a product's mechanical, electrical, and software aspects and test its performance under various conditions while optimizing its design. Moreover, digital twins facilitate engineering teams in enhancing their products' reliability, manufacturability, testability, and safety by designing them for a specific purpose.

Digital twins can also emulate entire supply chains and shop floors. These twins simulate how complex systems function, providing stakeholders with valuable insights into their efficiency and effectiveness, helping companies to redesign their systems to optimize their performance.

Furthermore, digital twins can replicate a company's service process, offering visibility into equipment performance in the field and forecasting service and maintenance requirements.

Taking Digital Twins Out of the Silo

Many companies are utilizing digital twins due to the benefits they deliver. According to a Deloitte study published in the fall of 2021, 85 percent of respondents were either utilizing digital twins or strategizing around their use in some capacity. However, the study findings indicate that companies derive less value from their efforts than they could. Only 15 percent of respondents reported using digital twins throughout the product lifecycle, with five percent using them on the shop floor and less than five percent using them in service and maintenance.

Even using digital twins in a silo can reduce costs by 20 to 30 percent, but those savings are limited to a specific area. By integrating different types of digital twins as part of a Model-Based Enterprise (MBE), companies can lower total costs by 50 to 70 percent. An MBE enables these savings by removing barriers between aspects of the product development lifecycle and creating all-encompassing views of the processes that drive it. Stakeholders can seamlessly view and share critical data, reduce cycle times, and develop higher-quality products more quickly, improving efficiency within specific areas and throughout the company.

As computing power and 3D printing technologies continue to improve, MBEs will allow for more innovative and cost-effective designs. We have already seen some of what MBEs can accomplish, such as rapidly producing personal protective equipment (PPE), medical detection of cancers, hearing and speech generative AI, and even better fitting prosthetics for children.

Digital Twins: Enhancing Model-based Design with Augmented Reality, Virtual Reality, and Mixed Reality

The idea of "digital twins" originated with NASA. Digital twins were then adopted into the manufacturing industry as a conceptual version of Product Lifecycle Management (PLM). Engineering systems have always used abstraction techniques to model complex problems. But the digital twin furthers this idea by allowing you to model and simulate a situation.

Thus, the digital twin is a virtual model that incorporates all the necessary information about a physical ecosystem to solve a particular problem - typically involving a simulation process. Other industries have adopted the digital twin's paradigm with complex processes, such as construction (built environments) and healthcare.

What is an MBE?

Model-Based Enterprise (MBE) is a manufacturing methodology that uses a digital model of a product as the authoritative information source for all activities in that product's lifecycle. In other words, instead of traditional 2D blueprints or physical prototypes, a 3D digital model is used for design, analysis, production, inspection, etc.

A digital twin is a virtual model of a physical product or process that simulates its behavior in real-time, and it is often integrated with IoT data for improved predictive capabilities.

Here's how MBE and digital twins are related:

1. Common Conceptual Base: MBE and digital twins use digital models to simulate, predict, and optimize real-world processes. In MBE, these processes are mainly manufacturing activities. In contrast, in the case of digital twins, the processes could be anything from the operation of an individual machine to the behavior of an entire city or ecosystem.

2. Data Integration: Both concepts involve integrating various data types into a unified model. This might include CAD data, product and manufacturing information (PMI), and inspection data for MBE. Digital twins could consist of sensor data, operation data, environmental data, etc.

3. Lifecycle View: Both MBE and digital twins aim to provide a comprehensive view of a product or system throughout its entire lifecycle. This lifecycle view can improve efficiency, reduce costs, and enhance product quality or system performance.

4. Enhanced Decision Making: MBE and digital twins can improve decision-making by providing accurate, up-to-date information about a product or system's current state and predicting future behavior. This can support design, maintenance, upgrades, and more decisions.

While MBE and digital twins are distinct concepts, they share many similarities and can be highly complementary. For example, an MBE might utilize digital twins for specific products or processes to optimize manufacturing operations. In contrast, a digital twin of a manufacturing plant could benefit from MBE methods to simulate and optimize production processes.

Guiding the Model-Based Enterprise Transformation

In today's world, it is important to achieve efficiency and cost-effectiveness and contribute positively to society. This can be achieved through an MBE but requires more than just technology. It involves reimagining processes and ensuring that the members of an organization are equipped to use the new system and take advantage of the opportunities it presents. The three crucial aspects of any digital transformation effort:

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require time and patience to see them through. Alignment and management buy-in with a clear mission and product is critical for any organization that wants to derive maximum value from an MBE.

The value an MBE creates is on full display at?The Smart Factory @ Wichita?on Wichita State University's Innovation Campus. Deloitte and Siemens have joined together to showcase the depth and breadth of what is possible with an MBE. Within this facility, Siemens developed the dedicated?eXplore Live space?as 3,000 square feet of hands-on learning opportunities for companies looking to modernize, reshore, localize, or regionalize their operations in North America.?Visitors see demonstrations of how digital solutions, including digital twins, can enable manufacturers to drive new business models, improve product quality, boost productivity, and become more sustainable. The broader factory experience features a fully operational production line and labs that explore factory innovations. In short, it's a space that defines what cutting-edge means for today's manufacturers and establishes the future of manufacturing.

Conclusion

While digital twins are a familiar technology, they still hold unexplored potential for most organizations. Integrating digital twins to create a Master Data Model (MBE) can unlock significant efficiency and cost savings for companies. This is achieved by creating a single source of truth visible across the organization, allowing better products to be made more quickly, data to be shared more easily between teams, and project timelines to be accelerated.

However, it's important to note that digital twins and MBEs are not a quick fix. To fully realize their benefits, companies must ensure that their team members understand the technology and can use it effectively. Additionally, it's crucial to implement the necessary process changes across the organization—failure to do so risks missing out on the potential advantages of these technologies.

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