The latest manufacturing trends in Industry 4.0 and how our customers are implementing them with our help.
A digital twin is a type of simulation model that aims to represent a machine or business process in real-time. It is created by cross-referencing the subject's current data with its model. The results improve the understanding and performance of businesses across all verticals and industries. According to Gartner, it is a key strategic and competitive asset for improving operational efficiency.
"Organizations looking to drive innovation and performance should explore the transformative opportunities that digital twins offer." Deloitte
Adopting a digital twin is becoming increasingly easy. Computing and communication costs continue to fall, while modeling tools are being developed. In each sector, digital twins can have their applicability. Their use to provide real-time insight, forecasting, and decision support is immediate.
This article will examine their characteristics, the considerations to be made when constructing them, and, using examples, show their value.
The specific details of a digital twin will depend on its scale and purpose, but there are two essential characteristics:
1. Represent an existing operational object
From design proposal to system decommissioning, the digital twin will reflect a specific object.
2. Represent the actual state of the object
Provide data that describes the current and historical state of the object or process. This information (e.g. temperature, motor speed, etc.) can come from the input, from the user, or sensors.
Combined, these two features create a virtual representation of a real-world system and its state. It is the ability to determine the state of a specific object that distinguishes a digital twin from a simple simulation model.
The frequency with which data is updated to represent the real world varies. For example, the state of a turbine may be updated regularly and frequently, whereas the state of a supply chain is updated intermittently and asynchronously. As long as the model corresponds to a uniquely identifiable object and its state is sufficiently accurate, it can be considered a twin.
Where appropriate, a digital twin refers directly to the real world and itself makes operational changes. For example, historical data from older warehouse picking robots can be used as the basis for maintenance programs to ensure the efficient operation of newer units. The usefulness of digital twins, therefore, increases as they are deployed.
Reduced computational and modeling costs have certainly encouraged the development of digital twins, but there are still challenges to widespread implementation.
Data is needed to keep a digital twin up to date throughout its deployment. Indeed, a model must be associated with relevant data to become a digital twin. A model without situational data remains generic. It is the data that ensures uniqueness by giving life to the model and making it useful. Data describing the state of the subject, its environment, and its functioning are essential for diagnosis, prediction, and experimentation. Furthermore, collecting data once is rarely sufficient. They must be timely and consistent with the real-world system they represent.
A tool that includes a database can be useful in the early stages of development, as developers will have less complexity and fewer software interactions to consider. Indeed, with the right scenario, a powerful internal database can be used throughout the life of a digital twin, reducing dependencies and simplifying support.
It is also important to recognize the external data formats and query standards that the digital twin will need to work with. To facilitate development and scaling, choose a tool that offers native access to the widest possible range of data management systems. A good digital twin will integrate with other databases or parts of an organization's IT infrastructure as they are developed and deployed.
Developing a model of a real-world system involves multiple components and can quickly become complex. Models can also span several domains. However, modeling these environments may require different approaches, perhaps because of the information available or the nature of the operations. For example, warehouse operations will generally be modeled differently from network-level supply chain operations. Unifying complex and disparate processes and operations requires a flexible modeling environment, ideally one that can link different methods.
Another consideration is software dependency. Reducing the number of software platforms used to create and operate a digital twin streamlines support, maintenance, and further development.
A powerful modeling environment is crucial. However, when considering a digital twin platform, it is necessary to go beyond the ability to create a good model. Developers will also need to consider information flow and visualization. A multi-method modeling environment can simplify development by providing a single tool to accurately capture all the required details.
In keeping with the trend, many industries are using digital twins in their deployments. The following cases examine the real-world benefits that can be achieved by using digital twins.
The integration of a new product into a given fleet (production fleet, rail, equipment, assets, etc.) can create unforeseen challenges for operations and maintenance. Spreadsheet-based forecasts will not be effective in managing the new amount of data and identifying anomalies. To solve this type of problem, industrial companies use digital twins based on unifying data management platforms. This solution allows the use of sophisticated data simulations and analysis to model fleet operations, including customer operations, maintenance facility operations, technical characteristics, supply chain and logistics, etc.
The main benefits of using digital twins come from their ability to operate across the entire fleet. Amongst other things, they will be able to: forecast key system performance indicators, identify and cost bottlenecks and explore 'what-if' scenarios to inform investment decisions.
Overall, these benefits mean that investing in a digital twin benefits many areas of the business: bidding and tendering, assessing network design, implementing optimization, and anticipating and assessing end-of-life.
Supply chain management
To exploit the benefits of Industry 4.0, industries often consider introducing digital matching capabilities to their production lines. Amongst others, the maintenance processes of production lines represent an area to test the use of digital twins. Failures on a production line result in costs. The economic impact of improving maintenance processes is immediate. Unlike a simulation-only solution, which would simply identify bottlenecks and constraints, a digital twin will work with real data from the production line to precisely manage maintenance as needed.
Companies will be able to start by replicating a single production line with the capture of key performance indicators and financial indicators to identify the potential added value of investing in new maintenance policies, such as predictive maintenance. The main buffers and stations in the line will need to be modeled to gather the necessary data. Then, using a digital twin, it will be possible to test the different maintenance policies, the resilience of these policies when applied to different production plans, and to apply predictive models to determine the remaining useful life of components. By combining this simulation model with production line data, industries will have a detailed and comprehensive tool for establishing efficient operations.
Digital twins have become more accessible than ever before. Their judicious use can reduce costs and boost profitability. A powerful and flexible simulation environment is essential for the successful development of a digital twin. A digital twin must interact with a variety of other software systems, data sources, and users. In addition, the scope and scale required to model entire real-world objects, operations, and processes throughout their lifecycle means that the complexity must be effectively managed.