The Essential Guide: Putting Digital Twin to Work in Industry 4.0
Manufacturing | 11 min READ
    
Digital Twin in Industry 4.0
The digital twin is one of the hottest techs in Industry 4.0 and is set to revolutionize industries in ways very few expected. Its market touched USD 2.86 billion in 2020 and is expected to grow at a staggering CAGR of 57.2% to reach USD 53.61 billion by 2027.
Shirish Sahay
Shirish Sahay

VP & Sales Head

Manufacturing & Life Sciences, Europe

Birlasoft

Sumit Mehrish
Sumit Mehrish

Associate Vice President

Manufacturing

Birlasoft

 
While the technology is relatively new and uses modern tech chops, such as AI (artificial intelligence), AR (augmented reality), VR (virtual reality), and IoT (Internet of Things), such a massive growth has rarely been seen. A lot of industries are striving hard to teach it for their benefit. But very few sectors would benefit from it as much as the manufacturing landscape.
This article walks you through the basics of digital twins and their benefits for the manufacturing sector.
What is Digital Twin Technology and How it Benefits Manufacturing in the Industry 4.0 Era?
Depending on the use cases, there have been a plethora of definitions of the digital twin. In contrast, some define it as a sensor-enabled digital replica of a physical object used for simulating the object as if in the real world. In contrast, others define it as an integrated model of an as-built product that presents all the manufacturing issues with an item and can be modified to keep it in line with the actual asset's present condition.
Stay Ahead
Visit our Manufacturing page
In reality, a digital twin is an adaptable digital profile of the past and current behavior of a physical object or process that brings both the physical and digital worlds together to optimize business performance. It uses a plethora of real-time, real-world, and cumulative data management across many data points to create a flexible and evolving digital profile that can be used for designing, upgrading, or repairing purposes.
What Is a Digital Twin in Manufacturing?
While the concept of the digital twin is barely anything that we haven't heard before, the advancement in the involved factors, specifically technology, has led to people renewing their focus on it for a plethora of reasons.
As for the manufacturing sector, the digital twin companies can collaborate with OEM players to help them digitally plot a physical object, such as heavy machinery. It can help them cut downtime and resources required for assembly, installation, and validating their rich resources. It does so by producing a digital twin model as-built, as-designed, and as-maintained version of the physical asset.
What Are Enabling Technologies for Digital Twin?
The digital twin can safely be regarded as the literal meaning of digital transformation. Like many Industry 4.0 innovations, AI algorithms form the base of the digital twin. In addition, IIoT (Industrial IoT), actuators, and data analytics are crucial for its success.
The process of creating a digital twin starts with people, usually, experts in applied mathematics or data science, looking to deduce and congregate the physics and operational data of a physical object or system. They then use the findings to develop a simulation-based mathematical model capable of replicating the original. For this, they fit sensors, specifically IIoT products, to receive data from the source. They then use it to simulate the happenings in the digital twin in real-time.
Digital twins receive inputs about what is happening to the physical object at any time via actuators. These indicate if the action must be warranted and requires human intervention via 3D rendering, intuitive tables, or a dashboard.
Depending on your use case, you can create a digital twin to offer feedback or act as a prototype to create something along the same lines.
What Challenges Do Digital Twin Solve
Industry 4.0 has had a significant impact on the way Manufacturing takes place. Combining mechatronic technologies seamlessly with AI, ML, IIoT, and data management has already shaken Manufacturing SOPs. Furthermore, the inculcation of the digital twin technology further enables manufacturers to implement complex multi-disciplinary processes backed by the latest technologies around them.
The amalgamation of physical and digital worlds and the presence of superior automation abilities has meant that the digital twin is already solving many issues that hinder the seamless growth of Manufacturing as a sector. Examples include product lifestyle extension, holistic Manufacturing, process improvements, and rapid prototyping backed by big data and analytics. Essentially, a digital twin solves a problem virtually before it occurs in the real world, which is a boon for a capital-intensive sector like Manufacturing.
Digital Twin Advantages and Disadvantages
The phenomenal growth of digital twins has meant that it has become the center of attraction and is being looked at as the next big thing when Industry 4.0 sets its foot in totality. Companies that fail to adapt will fall behind in the race, and even those in the race will have to tackle the set of challenges it poses.
Benefits of Digital Twin in Manufacturing
Here are the benefits of digital twins in the manufacturing world –
Cost-cutting
The prototype is a vital part of a product design and the development of its final iteration. We have come across several OEMs designing hundreds of models before zeroing on to the one they think is the best fit. Traditionally, these models had to be developed physically, which meant that organizations had to devote a significant portion of time and money. With digital twins at play, you can now detect design defects before producing and simulation testing. As a result, fewer employees can test and develop the final product with minimum additional expenses.
The Essential Guide: Putting Digital Twin to Work in Industry 4.0
Faster development and launch of new products
OEMs are in a perennial race of launching new products ahead of their competition. The speed has only supercharged with the arrival of Industry 4.0. With digital twins available to deconstruct and optimize the manufacturing processes, companies are now shifting the product life cycle to the digital world. They are now adopting virtual prototyping at a rapid pace. This move has allowed them to reduce their time to market as improvements in the digital models are easier to conceive and implement.
The Essential Guide: Putting Digital Twin to Work in Industry 4.0
Continuous improvements
In the traditional scenario, you would depend on the physical object or its CAD renders to figure out the room for improvement. But the adoption of digital twins has allowed virtual reality to take over, and manufacturers are now equipped with a real-time digital copy of the object, allowing them to construe its needs for improvements better.
Improved financial decision-making
With digital twins and the data analytics it produces, OEMs now have more in-depth information about material and labor costs. In addition, they can also integrate available advanced financial data to break down silos and ensure they understand the financial implications of the adjustments to the manufacturing value chain beforehand, enabling them to improve their financial decision-making abilities.
Digital Twin Disadvantages
Here are the potential disadvantages of adopting digital twins for your manufacturing ecosystem –
Security vulnerability
As per Gartner's estimate, over three-fourths of the digital twins implemented for IoT-connected manufacturing products will have a minimum of five different kinds of integration endpoints within 2023. Each of these endpoints represents a threat of security vulnerability. Given the multitude of data collected by digital twins, it can prove catastrophic for the organization unless they are well-prepared and address all the security concerns beforehand.
Stringent workforce training requirements
Manufacturing is predominantly labor-intensive, and adopting sophisticated tech can lead to a plethora of unforeseen bottlenecks. Moreover, to make the most of digital twins, organizations have to adapt to new ways of working and integrating technical data more than ever. As a result, it can lead to a reluctance to work with digital twins.
Digital Twin in Industry 4.0 Technologies Applications and Challenges
Digital twins provide comprehensive visibility into products and systems, allowing OEMs and others to understand them better, identify bottlenecks, and streamline operations. In addition, its ability to showcase a 360-degree view of the health and performance of physical objects means that it allows for predictive maintenance, thereby improving product reliability and performance across sectors.
Here are some of its applications –
Digital Twin Applications in Manufacturing
A digital twin offers critical insights into in-use machinery and processes, such as workload capacity, degradation points, usage patterns, and more. In addition, it allows OEMs to develop connected factories that simulate complete processes digitally with all their stages. As a result, it enables streamlining strategies before they are rolled out and helps figure out possible errors, thereby helping in improved efficiency throughout the value chain.
In addition, digital twins are also capable of using empirical data to develop virtual prototypes and simulations seamlessly. Finally, manufacturers can easily evaluate product usability and improve the future component design, given the granular controls.
The Essential Guide: Putting Digital Twin to Work in Industry 4.0
Digital Twin in Supply Chain Management
Automation is a core necessity of modern-day supply chains. But for that, there is a need to harness big data and leverage existing operations. Integrated digital twins create the requisite leverage for supply chain management, allowing organizations to pre-empt the potential impact of disruptions and proactively point out vulnerabilities to prevent jeopardizing supply chains' existing flow.
The AI and ML-backed tech can help identify bottlenecks by providing a continuous and end-to-end view of issues throughout the supply chain. In addition, they aid in process design testing and outcome prediction to maximize your resource utilization across the supply chain. The digital twin is also a proven performer in enhancing risk monitoring and finding the best way forward via emergency simulation.
Digital Twin Smart Factory
The inculcation of IoT in Manufacturing paves the way for the transition of traditional legacy manufacturing systems into smart, modern places capable of leveraging Big Data to revamp and call themselves 'smart factories.' Enabling them digitally with a high level of automation, they use digital twins to capture real-world data and simulate it for product and process development. It also allows them to use the internet and sensors to use real-time data for analytics and decision-making.
Digital twins also pave the way for the creation of digital threads that helps improve traceability and use ML to improve the decisions on the product or the process. As a result, it helps cut down unplanned downtime risks, facilitates the improvement of operational and production potential throughout the value chain, and allows organizations to embark on their path to smart Manufacturing.
Digital Twin Application for Production Optimization
Not only is digital twin adoption capable of cutting down unplanned downtime, but it can also help achieve production optimization. Moreover, the technology is a crucial enabler in setting up advanced monitoring and performance evaluation regarding the machinery and the process.
Its inculcation facilitates root cause analysis (RCA) and planned maintenance of physical objects by connecting them with digital models, thereby leading the way for the development of advanced models for running and managing machines. Pair it with improved fleet management, and organizations can seamlessly optimize production.
Digital Twin for Production Planning and Control in Industry 4.0
The internet's growth and availability have led to a shift in the demand for products and services worldwide. Industry 4.0 is banking on modern-day solutions like digital twins to counter the increased complexities. In addition, IoT and cyber-physical systems are seen as the key enablers in establishing and managing smart production planning and control.
The digital twin will allow for improvement in data quality by leaps and bounds, which will give rise to newer and more evolved planning and control methods throughout the value chain. Plus, the quantitative increase in data will propel decentralized decision-making and using agent-based automated computation, all of which would lead to improved production planning and control.
What Are the Challenges of Digital Twin Technology?
Technology, while reliable, will always suffer from bottlenecks that require careful handling. The scenario is no different for digital twins. Here are the challenges that it will need to overcome if it wants to make an impact –
  • Accuracy – It must accurately capture detailed data of simple and complex objects and their relationship with the surrounding entities.
  • Conflict detection – Given the large sets of data in play, successful digital twin implementation would require the ability to detect and react to changes in real-time without giving rise to conflicts.
  • Seamless collaboration – Traditional equipment isn't smart, and even after IoT sensors are in play, they would suffer from a degree of incapability. It means that organizations will always have to keep an additional leeway while using data generated from digital twins.
The Essential Guide: Putting Digital Twin to Work in Industry 4.0
Is Digital Twins the Future?
A digital twin is not just a virtual prototype and provides access to a world of unlimited possibilities. While creating large-scale digital replicas raises questions about security, the value these models have been able to generate has been immense for Manufacturing across sectors. In addition, the technology is still in its early stages, but there are several reasons why next-gen Manufacturing already needs digital twins for a myriad of its processes. So, we can expect the development of many innovative use cases as we move forward, making it a contender for being the future.
 
 
Was this article helpful?