It has been over a decade since Industry 4.0 emerged, and its potential to revolutionize manufacturing is now well-recognized. Most manufacturers have initiated pilots to validate the benefits of data and advanced analytics, applying autonomous solutions such as predictive maintenance to optimize operations. However, accelerating the adoption of autonomous operations presents challenges. High-value use cases often necessitate the deployment of autonomous solutions across multiple assets, lines, or functions. Additionally, the extensive data required is typically beyond the capacity of existing manufacturing operations management (MOM) architectures.
Some manufacturers have turned to data lakes to store manufacturing data in a single repository. Yet, these solutions often fail to provide the structured information needed for end-to-end optimization. Consequently, CIOs and manufacturing managers are increasingly exploring digital twins as a comprehensive solution. Digital twins unify and contextualize data from multiple sources, facilitating autonomous solutions.
Digital-twin technologies promise to expedite product development and enhance product quality. Over the next five years, approximately $30 trillion in corporate revenues will depend on products not yet launched. As developing successful new products becomes increasingly challenging, companies are investing in digital-product-development capabilities to streamline design and engineering processes while reducing R&D costs. Advances in computing power, analytics, and artificial intelligence have given rise to digital twins—virtual replicas that simulate the characteristics of physical products. These technologies are expected to accelerate product development, improve outcomes, and lower costs, with the global market for digital-twin technologies projected to grow substantially.
What is a Digital Twin?
A digital twin is a virtual replica of a system’s behavior within its operating environment. This system, which could be a product, manufacturing process, or supply chain, is represented through a collection of digital models. These models, process react to various data stimuli from the external environment. Digital twins integrate multiple model types and data sources, providing a more accurate approximation of real objects than traditional simulation methods.
Current Applications of Digital Twins
According to survey data, nearly 75 percent of companies in advanced industries have adopted digital-twin technologies with at least medium complexity. However, adoption varies significantly by sector. The automotive, aerospace, and defense industries are more advanced in their use of digital twins, while sectors such as logistics, infrastructure, and energy are often still developing initial digital-twin concepts.
In the United States, an automotive company is building a system to model all its software and hardware configurations, enabling the simulation of design improvements before delivering them to customers via over-the-air updates. Additionally, a US multinational industrial company is developing multiphysics models of turbines to predict wear in real-time, fine-tune performance, and optimize output against service life and total cost of ownership.
These digital-twin pioneers invest in this technology for several key reasons:
- R******** Product Development: Digital twins allow design and engineering teams to explore more design options without the costs associated with producing and testing physical prototypes.
- Improved Testing and Validation: Digital twins enable the evaluation of new solutions in a wide range of lifelike scenarios, including unusual and extreme operating conditions.
- Deeper Insights into Product Behavior: Engineers can use digital-twin models to monitor any part of the system at any time and trace complex interactions between product elements.
- Informed Product Improvements: Real-world data informs product enhancements by simulating the impact of proposed design changes using data collected from products in the field.
With companies facing increasing pressure to accelerate transformation efforts in order to compete, they need a way to gather forensic insights across their operations. As a result, digital twin capabilities are now being directed at companies, giving rise to a “digital twin of an organization” (DTO). – Rupert Colbourne, Chief Technology Officer at Orbus Software
Read Rupert’s insights on ‘Why Every Organization Needs a Digital Twin‘
The Role of Digital Twins in Manufacturing
Smart manufacturing depends on data from supply chains, factories, systems, and equipment. Manufacturers exploring Industry 4.0 applications utilize digital twins to monitor and analyze real-time data throughout their production processes.
Key Applications of Digital Twin Technology in Smart Manufacturing
Digital twins offer numerous benefits in smart manufacturing, from predicting maintenance issues to informing product upgrades and financial decisions. Here are five ways digital twins meet various needs in smart manufacturing:
Decreasing Product Time to Market
Digital twins provide real-time insights by creating a virtual replica connected to the physical asset. This eliminates delays associated with physical products, allowing operators to expedite design, development, testing, and maintenance.
Case Study: ENGIE Lab CRIGEN
- Problem: Carbon emissions are harming the climate.
- Solution: Ansys Twin Builder helps reduce simulation time from hours to seconds, accelerating the development of new energy solutions.
- Result: Real-time optimization of energy and environmental performance aids the zero-carbon transition.
Optimizing Process and Product Performance
Digital twins enable manufacturers to predict product quality, leading to informed decisions about material upgrades and process enhancements. They ensure consistency across large-scale production, aligning the end product with specifications.
Case Study: Kärcher
- Problem: Reduce the weight and heat of small battery packs.
- Solution: Kärcher used Twin Builder for a digital engineering workflow, allowing fast and accurate concept comparisons.
- Result: Identified a battery cell type and housing design, reducing the number of cells in the battery pack by 20%.
Also Read: Misconceptions and Myths around the Role of a Modern CTO
Increasing Production Efficiency
Digital twins allow operators to continuously monitor processes and systems for efficiency improvements. They reveal opportunities for enhancing production flow, helping manufacturers reduce energy and material consumption and achieve sustainability goals.
Case Study: EDF Group
- Problem: Developing affordable low-carbon power generation systems.
- Solution: EDF used Twin Builder to create digital twins of plant turbo-alternators, enabling predictive maintenance and reducing repair expenses.
- Result: Engineers designed highly efficient systems that meet stringent regulatory requirements and deliver superior power output.
Enabling Predictive Maintenance
Digital twins help prevent unplanned downtime, which can be costly. They allow manufacturers to anticipate maintenance issues by analyzing internal workings and environmental pressures, enabling timely interventions.
Case Study: Phoenix Contact Electronics
- Problem: Need for smaller elementary relays to meet customer demands.
- Solution: Phoenix Contact used Ansys optiSLang and Twin Builder to analyze electromechanics, structural, and thermal properties.
- Result: Developed PSRmini safety relays with full performance in a compact design.
Facilitating Virtual Commissioning
Digital twins allow early validation of system designs, predicting and solving integration problems. They show how entire systems interact, improving overall production floor performance.
Case Study: Rockwell Automation
- Problem: Impact of environmental and material effects on in-field assets.
- Solution: Connected Twin Builder to Studio 5000 Simulation Interface.
- Result: Enabled engineers to simulate complex physical phenomena, enhancing asset performance understanding.
Implementing Digital Twins in Your Manufacturing Facility
Creating a digital twin for your manufacturing facility enhances operational efficiency and decision-making. Follow these steps to implement digital twins effectively:
1. Asset Selection
Identify which components of your facility would benefit most from a digital representation. Prioritize assets where enhanced visibility could lead to improved efficiency, reduced downtime, or better performance. Conduct a gap analysis to determine potential impacts and feasibility.
2. Digital Representation Creation
Develop digital models of the selected assets. Use existing digital blueprints or manually create models that reflect the physical characteristics of these assets. The method will depend on the available software.
3. Sensor Integration and Data Collection
Equip the selected assets with appropriate sensors to collect and transmit operational data. This step ensures the digital twin reflects real-time conditions. Integrate this data to maintain robustness and accuracy in the digital twin’s representation of operations.
4. Development of Analytical Models
Utilize analytical tools to build models that interpret the collected data. These models enable the prediction of operations and allow process adjustments before outcomes occur, transforming the digital twin into a proactive tool for operational excellence and strategic planning. Ensure seamless integration with existing software for complex models.
5. Activation and Operational Integration
Integrate the developed digital twin into daily operations. Use it for continuous monitoring, scenario analysis, and decision support. Ensure that the digital twin remains a dynamic component of your operational ecosystem, providing ongoing value and adaptability. Make real-time data accessible to operators for data-driven decisions.
6. Continuous Improvement and Training
Provide training resources to ensure your team is proficient in using the digital twin. Encourage ongoing feedback and use insights from the digital twin to drive continuous improvement in processes and asset management.
Also Read: Top 5 Must-Know AI Use Cases in Cybersecurity: For CIOs
Integrating Augmented Reality with Digital Twins
Implementing augmented reality (AR) technologies in conjunction with digital twins enhances productivity across various industries, including manufacturing, aerospace, and industrial sectors. This integration facilitates experimentation and predictive analysis of existing products, reducing unplanned downtime and associated costs.
Combining AR with digital twins allows frontline workers to interact with virtual replicas of physical objects in an immersive manner. This technology transforms the design, construction, and operation of complex systems by providing an intuitive and visual interaction with data.
In industrial settings, workers using AR smart glasses can visualize machinery along with its key data outputs and functions, enhancing efficiency and reducing turnaround times. This approach is also beneficial for servicing, repair, and maintenance activities.
AR-powered digital twins enable the creation of 3D models for training purposes, eliminating the need for actual machines. This reduces the risk of accidents and errors during the training process.
Overall, the integration of AR in digital twins improves the understanding, operation, and performance of complex systems, resulting in better outcomes and increased efficiency.
Some of the Top Digital Twin solution providers
General Electric
ANSYS
Dassault Systèmes
Siemens
IBM
Microsoft
Oracle
ABB Ltd
CISCO Systems
Conclusion
One of the key advantages of digital twins is their ability to integrate with existing solutions, allowing manufacturers to leverage this technology without replacing current systems. This results in faster time to value and reduced costs. The manufacturing organization’s underlying Manufacturing Operations Management (MOM) architecture remains the foundation of its operations.
By implementing a digital twin alongside the existing MOM architecture, manufacturers can maximize the value of their longstanding technology investments, avoiding the need for a complete system overhaul. This approach enables the effective and quick contextualization of data from all existing systems. Depending on the complexity of the use case, manufacturers can realize benefits within three to six months.
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