A digital twin in manufacturing is a digital representation of an actual system. In other words, it is an encapsulated software object or model replicating a distinct real thing, procedure, organization, individual, or other abstraction.
The global market for digital twins is expected to expand from roughly €16.42 billion in 2024 to €240.11 billion by 2032. Manufacturing is the fastest-growing sector in this market. The main manufacturing digital twin trends involve AI-enhanced simulation, industrial metaverse integration, AR/VR collaboration, and more. In manufacturing, companies implement AI-driven representations within broader Industry 4.0 and “industrial metaverse” initiatives to create 3D models of cars and equipment, facilitate remote collaboration, optimize and customize design, and test processes and features before deployment, overall enhancing efficiency.
In this article, Qubit Labs, with vast experience in building dedicated teams for companies engaged in digital twin manufacturing, explains the role of a digital twin for manufacturing, outlines the main trends and core technologies behind digital twins, emphasizes their advantages, and provides proven strategies for implementing flexible and scalable digital twins.
Key Takeaways:
- In its simple context, a digital twin technology for manufacturing is a digital version of existing or upcoming products that may replicate every feature of their real-world equivalents.
- Digital twins transform factory decision-making, allowing innovative companies using this advancing technology to increase productivity and reduce expenses.
- Digital twins enable real-time production process monitoring and control in smart product development with the help of diverse technologies, such as IoT, AI/ML, cloud computing, and more.
What Is a Digital Twin in Manufacturing?
A digital twin in manufacturing is a virtual representation of certain physical assets, like goods, manufacturing equipment, and/or entire factories, through IoT and AI. A technology consultant and advisor, Daniel Araya, stated, “A digital twin is more than a blueprint or schematic since it incorporates a set of executive controls together with a real-time simulation of system dynamics.” Adopting digital twins will help businesses make better decisions regarding the quality of the final product and manufacturing processes, increase productivity, and obtain profound insights into company operations.
Key features of a digital twin in construction and manufacturing, according to ResearchGate, are as follows:
- Accurate digital modelling
This characteristic ensures that a virtual model comprehensively replicates all physical qualities of a real-life system. - Analytics support feature
This virtual replica of a physical product can gather much data over time to examine trends. - Real-time update
All data collected is two-way synchronized and updated in real time, ensuring effective optimization and comprehensive process diagnostics to identify possible problems early. - Control
Digital twin technology allows for independent responses and adjustments to the physical system using analysis insights.
Digital twins in the manufacturing industry have several alternatives. However, they have particular distinctions. Here is a brief overview of digital twin modeling and other relevant options.
| Technology | Real-World Modelling | Continuous Optimization | Real-Time Data Integration | Analytics | Limitations | Use Cases |
|---|---|---|---|---|---|---|
| Digital Twin | Yes | Yes | Yes | Yes |
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| Simulation | Yes | No | No | Yes |
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| Virtual Reality | Yes | No | No | No |
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| Artificial Intelligence | Yes | Yes | Yes | Yes |
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| 3D | Yes | No | No | No |
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| Metaverse | Yes | No | Partial | No |
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Core Technologies Behind Digital Twins
Digital twin smart manufacturing is combined with cutting-edge technologies to provide maximum efficiency and comprehensive optimization. Internet of Things (IoT) sensors that provide real-time data are at the core of digital twin solutions for smart manufacturing. Then, this data is processed with cloud technologies, analyzed and predicted with AI/ML, and visualized with AR/VR technologies.
IoT
IoT offers a connection to information in the real world through chips, scanners, and intelligently connected devices. These IoT devices are attached to components in the physical environment and track all relevant data. Then they transmit these real-world metrics in real time to accurately model digital twins and track all necessary indicators.
Possible challenges:
- IoT devices may be prone to cyberattacks.
- Data integration may be challenging.
Artificial Intelligence (AI) and Machine Learning (ML)
Artificial intelligence and machine learning algorithms analyze the volumes of digital twin data, automate analysis processes, and create realistic predictions. This information improves processes and equipment and builds effective development strategies.
Possible challenges:
- Data privacy and security issues may arise.
- The computational demands of complex AI and ML algorithms require a strong IT foundation.
Virtual Reality (VR)
This technology allows you to create an interactive experience that ensures a deeper understanding of a physical world and interaction with different subjects. It will enable you to understand production processes and product functionality better, analyze them thoroughly, and find opportunities for improvement.
Possible challenges:
- VR hardware and software usability can pose a problem.
- There can be network challenges and communication issues.
Cloud Computing
It may be challenging for traditional IT infrastructure to process massive volumes of data. In this case, cloud computing offers the necessary performance, scalability, and storage opportunities to support efficient operation. Besides, this technology for digital twin applications in manufacturing is cost-effective, as it provides the possibility of paying only for the resources used and eliminates the need to invest in expensive physical equipment.
Possible challenges:
- There can be data integration issues.
- The poor Internet connection can disrupt regular operation.
Edge Computing
Digital twin technology manufacturing and edge computing can be combined to assist manufacturers in reducing risk, enhancing security, optimizing data, and speeding up response times. Edge computing technology transfers data quickly as the data storage unit is situated locally. Besides, it ensures constant real-time environmental monitoring to prevent any hazards.
Possible challenges:
- There can still be network latency.
- Operational management can be challenging.
How Digital Twins Work in a Smart Factory
Smart factories leverage cutting-edge technologies like the Internet of Things, Artificial Intelligence, Machine Learning, and cloud computing to optimize production processes, increase efficiency, and easily adapt to fluctuating market demands. The digital twin manufacturing process is as follows:
- Sensors embedded in the physical environment collect the necessary information about production efficiency, temperature, and other indicators and transmit it to the digital twins.
- The digital twins process the information to create an accurate virtual model of the physical environment or a specific object. This enables simulations to test various parameters.
- Professionals simulate different scenarios, testing product performance and functionality. This allows them to optimize workflows, identify potential problems, and make timely improvements.
- Using VR/AR technologies, it is possible to create interactive models that enable effective interaction with digital twins and prompt decision-making.
There are several digital twin manufacturing use cases:
- Digital twins are widely used in prototyping as they enable engineers to test various designs and performance in diverse settings to prevent unnecessary costs associated with redesign.
- Digital twins can be used to optimize production lines as they allow real-time data analysis, which can then be used to reduce downtime, maximize resource allocation, and achieve perfect production efficiency.
- Thanks to IoT devices, digital twins continuously monitor equipment condition, allowing for the identification of possible breakdowns before they occur. This helps factories reduce maintenance expenses and increase the lifespan of equipment.
- Digital twins can model supply chains from production to delivery. They can boost the general effectiveness, improve customer service, decrease expenses, and identify possible bottlenecks.
Key Benefits of Using Digital Twins in Manufacturing
Digital twin initiatives have been fully or partially implemented by 29% of multinational manufacturing organizations. The benefits of digital twin in manufacturing include enhanced productivity, cost optimization, downtime reduction, and factory floor plan efficiency.
Enhanced Product Development
Leaders in product development anticipate that digital twins will speed up the process, enhance results, and cut expenses. A digital twin for a manufacturing workshop allows companies to represent the whole process and conduct numerous design interactions without physical prototyping. This reduces costs, improves the product quality, and accelerates time-to-market.
Improved Productivity
Digital twins can significantly improve an organization’s capacity to make proactive, data-driven decisions, boosting productivity and averting possible problems. Besides, Siemens reported a 20% productivity increase from using digital twins.
Digital twins continuously monitor entire systems and processes, enabling the timely detection of production issues. In addition, they allow you to test the sequence of production lines, ensuring continuity and high efficiency.
Predictive Maintenance
Digital twins can predict equipment failures. This minimizes downtime by scheduling maintenance before malfunctions happen. Digital twins continuously monitor all parameters like vibration, energy consumption, temperature, and operating hours and notify when parts are worn out. This guarantees predictive maintenance before disrupting the entire production.
Cost Reduction
By removing errors and reducing the development phase to 9–15 months, a digital twin can reduce production costs by up to 80%. They also allow them to test different materials and manufacturing techniques to find the most efficient and cost-effective way to produce specific products. In this way, they will save money on prototypes and materials.
Reduced Downtime
Unplanned downtime costs the 500 largest companies in the world over $1.4 trillion a year, or 11% of their total sales. Besides, automotive manufacturers currently lose an astounding $2.3 million for each hour of inefficiency. Therefore, a digital twin in manufacturing example is displaying current equipment downtime and forecasting future downtimes by evaluating data from sensors, machinery, and other equipment.
Best Practices for Scalable and Modular Digital Twin Implementation
Digital twins will be exceptionally beneficial for manufacturing if they are developed on a modular architecture and follow clearly defined goals. To ensure their efficiency and perfect scalability, follow these strategies.
1. Start with a Clear Architecture Plan
Comprehensively analyze all manufacturing processes and identify areas that will benefit most from digital twin implementation. It can be machinery operation, quality control issues, production scheduling, etc. Then, assemble a clear architecture plan outlining advanced technologies, for instance, cloud and AI, strategies for successful integration with the current IT infrastructure, and risks that should be mitigated.
2. Invest in Data Infrastructure
Before embarking on your digital twin endeavor, invest in robust infrastructure. Ensure you have all the necessary IoT devices and powerful storage and processing capabilities to transmit high volumes of data to digital twins.
3. Assemble a Professional Team
A multidisciplinary team can create efficient and scalable digital twins. Therefore, you need data scientists, manufacturing experts, software engineers, and AI/ML specialists. If you lack this expertise in-house or want to add unique perspectives to your project, partner with a digital twin provider. Qubit Labs will provide you with unique information about countries for IT Outsourcing in Eastern Europe and land a perfect dedicated team with the necessary skill sets for building digital twins.
If you haven’t decided on a team composition, our professionals can share their unique insights and conduct custom salary research to find the best price-to-quality ratio. You can get relevant data for the data scientist, ML, and AI engineer salary to make informed decisions.
4. Test On a Small Scale First
Digital twin use cases in manufacturing are versatile, but to test your concept, create a digital twin for a separate component or process. This allows you to test all operations without disrupting existing processes and investing many resources. Usually, creating a virtual model of a physical environment comprises the following steps:
- Installing sensors on the necessary equipment to track pressure, vibration, temperature, and other data regarding real-time performance.
- Creating a virtual model that precisely depicts the physical structure and processes.
- Linking a digital twin to the physical environment via IoT devices to transmit real-world data.
- Stimulation of various scenarios and analysis of the response patterns. Digital twin manufacturing examples can involve speed increases to test performance and identify potential issues.
- Optimization of physical systems based on simulation insights. They usually range from minor changes to massive system overhauls.
- Predicting performance using a digital twin in the manufacturing industry to make specific operational and maintenance changes.
- Continuous improvements of digital models to ensure real-time insights and top-notch efficiency.
5. Scale
After you have tested this technology on a separate process or unit, scale gradually to improve efficiency. You can expand these digital replicas to other business departments, processes, or equipment, while monitoring their performance. Then, optimize these models continuously using relevant data and stakeholder input.
6. Plan for Maintenance and Upgrades
To guarantee the maximum value, regularly monitor the performance of digital twins and implement updates. Create a roadmap covering all the necessary cycles and strategies to ensure your digital twins meet your evolving demands and are an indispensable part of your scaling strategy. Besides, encourage knowledge sharing and open feedback to guarantee your team handles all processes and asset management effectively.
Digital Twin Use Cases and Examples
Digital twin examples in manufacturing involve design and performance optimization, resource management enhancement, and asset tracking and visualization. However, this list of digital twin examples is not exhaustive and depends on the industry. We’ll cover several use cases and real-world examples to help you realize how to leverage this technology to achieve incredible results.
Manufacturing
Digital twin examples in manufacturing encompass the virtual representation of physical objects for tracking manufacturing operations and simulations of hazardous scenarios to improve safety protocols. Chevron and Shell, for instance, use digital twins to automate maintenance orders and test long-term plant facility roles. This allowed them to increase operational efficiency and make informed decisions.
Automotive
In this industry, digital twins are used to test safety protocols and certain features (e.g., autonomous driving). Tesla, for instance, uses this technology to forecast maintenance requirements, provide over-the-air updates, and enhance vehicle efficiency.
Aviation
Digital twins are used to improve all phases of aircraft development and monitor processes to ensure flawless operation and timely part replacement. For example, a manufacturing giant, Airbus, utilizes digital twins to improve the quality and design, streamline production, and reduce expenses.
Digital Twin in Manufacturing with Qubit Labs
Thus, a digital twin in manufacturing allows companies to improve the product quality, accelerate development, test diverse designs and concepts for maximum efficiency, reduce costs, improve safety, and boost overall productivity. Digital twins are powered by diverse technologies, with IoT, ML/AI, VR, and cloud computing leading the way. Such tech giants like Boeing, Tesla, Rolls-Royce, Siemens, and Bosch leverage all the benefits of this technology to optimize machinery performance and avoid any quality and safety issues.
However, any business needs a clear strategy and a trustworthy partner for smooth digital twin implementation. Qubit Labs has over nine years of experience building top-tier dedicated teams across diverse industries. We know how to find top professionals to integrate digital twins into diverse manufacturing processes effectively. With us, you will enhance hiring speed by 50% and the quality of candidates by 80%.
Do you have a concept you want to bring to life? Book a free consultation call to discuss all the details.
