Digital twins are on the rise. The advantages of the digital twins: more efficient development and production cycles and better planning through data-based forecasts. Digital twins simulate the structure and behavior of physical products or their components in virtual form. All levels of a product or process that sensors can measure can be mapped in a digital twin. The more precisely the sensors record the required data and the higher the computing power they are processed, the more realistic the virtual model becomes.
Companies can use digital twins throughout the product life cycle, but they are usually used primarily in product development, production, and service. Especially at the beginning of development, potentially costly changes can be checked virtually for their validity. For example, data experts validate the design using simulation methods based on real-time data from the field. Digital twins for production are the digital image of machine components or complete systems, control elements, sensors, and test programs.
Those responsible can derive process optimization from these simulations. In addition, the virtual models act as an additional control for quality. Manufacturing processes can be tracked and adjusted in real-time. During the manufacture of a product, the digital twin for service is created for each product instance. The possible uses include, for example, remote maintenance work or predictive maintenance.
Make The Benefits Tangible
The quality of a product is an essential factor that determines the success of the manufacturing company. Digital twins offer companies numerous new opportunities to further improve their products through the innovative use of data. With the data-based virtual models, 3D views of products and prototypes can theoretically be displayed in real-time at any workstation connected to the company network – provided that the appropriate computing power is available. In this way, developers can present their design proposals regardless of location and efficiently improve them with global feedback.
The practical effects of planned changes and their effects on, for example, production or packaging can also be immediately understood using the simulations. The efficiency gained here positively affects the entire product life cycle and gives those responsible more flexibility and faster response times, especially in the development phase. Digital twins help companies respond better to the fast-moving digital business models of the modern market. The wealth of data also makes it easier to analyze and forecast the products in question.
Experts can use this data, for example, to automatically adapt the production process to current situations to optimize the material flow as a whole and increase productivity. In the after-sales area, forecasts on the behavior of products and components help to anticipate your own customers’ purchasing behavior and expectations. On the other hand, they allow you to set goals more precisely and estimate your own financial goals. Essential indicators such as the return on investment (ROI), i.e., the amortization of your investments, can be assessed as best as possible using the findings from the digital twin.
Collect Data Securely
Since digital twins depend on large amounts of current data from at least one, but usually several data sources, it is essential to secure this traffic accordingly. Ideally, the digital twin can be connected to the company’s existing User Account & Authentication (UAA) via the application interface (API).
This ensures a consistent level of security without making access unnecessarily tricky. So before choosing the digital twin for the service, decision-makers should check whether it is compatible with their security protocols and standards. The connection of open standards such as OAuth (Open Authorization) is possible without any problems in many cases.
Digital Twin And IoT Platforms
Especially in production plants with an already high degree of digitization, companies organize the sensors that collect the required data in IoT platforms. Because these make it easier to control and manage the data. The sensors are integrated into the digital twin and thus implement a so-called digital thread. In this way, the digital twins can draw from the combined data pool of the platform instead of querying the required data individually from the devices and sensors of the IoT Edge.
The connection to platforms also enables a standardized procedure for using the data. This applies even if the original data come from different data sources, possibly from different manufacturers or formats. With digital twins, companies can further increase the added value of their IoT platforms and better achieve the digitized economy’s new requirements and target values . When connecting platforms, however, a large number of proprietary IoT standards must be taken into account. Until overarching protocols become established here, the integration will continue to depend on the proprietary technology of the individual manufacturers.
When choosing the service provider for a digital twin, companies should rely on a consulting company with experience that is familiar with the portfolio of the individual manufacturers and knows how their solutions interact with one another. On the other hand, it is advisable to purchase the actual digital twin from a single source to minimize the risk of software and hardware conflicts and secure the best possible support and services after the investment. To make it easier for companies to choose, Many providers organize their offerings according to industrial sectors for which individual digital twins are most likely. There are specialized solutions for heavy industry, electricity providers, transport and logistics, or automotive.
How To Get In
There are a multitude of variants and potential fields of application for digital twins. This sometimes makes it difficult for companies to find the entry point for their project. Where, when, and how do I best start with the digital twin? The first question should be about existing design and operational data. In the next step, the first simulations are created based on this data. Because the more complex the simulations, the more data points are required for a representative representation.
Therefore, the digital twin only functions as part of a holistic data strategy: the more precisely the company records its data, the more precisely the digital twin’s work and the more helpful the knowledge gained from them. The data relevant for the digital twin can be obtained by integrating IoT-capable devices into the operational process. A well-thought-out and step-by-step approach are essential here.
Your own IT architecture cannot be turned inside out in one jolt. Instead, companies need an individual strategy that they should develop together with all departments and external experts. One approach that has proven itself in practice is to concentrate on the so-called minimum viable products initially. The capacities of the digital twin are initially applied to products that only require minimal adjustments but offer the user a tangible advantage because nothing is more convincing than making your work processes more accessible.
The number of practical applications for digital twins is increasing. Companies have recognized that technology generates added value that goes beyond mere networking and data aggregation. Instead, the connection and integration of more and more IoT endpoints in interaction with other information sources in the company create the breeding ground on which successful digital twin projects can flourish.