[Tech Report] Automotive Production Systems Evolve Into Smart Manufacturing Based on Virtual Twins, Bridging IT and OT

As the center of gravity in the auto industry shifts to EVs and software-defined vehicles (SDVs), complexity on production floors is rising. Accordingly, the ability to reconfigure production equipment and processes is emerging as a key factor determining automakers' competitiveness.

Demand for a single production line is also expanding. Automakers must flexibly produce a wide range of models and specifications, and the need to rapidly reconfigure production equipment and processes in response to new model launches and design changes is growing.

Dassault Systèmes said that, amid these changes, OEMs are facing the challenge of developing and producing EVs and internal combustion engine vehicles at the same time. It also said production methods are shifting from mass production to mass customization.

An automotive production line is made up of a complex network of equipment such as robots, sensors, controllers, and logistics systems. Dassault Systèmes said that this is why discovering problems only after a real line has been built can lead to significant time and cost.

Product and process data secured at the design stage are separate from the data generated and operated by actual production equipment. This separation of data is making production optimization difficult.

To solve this, the IT domain of product and factory design needs to be connected in a single flow with the OT domain of equipment control and operations. Connecting IT and OT is also a core task of smart manufacturing.

Virtual twins serve as the link that closes the IT-OT gap. Dassault Systèmes defines a manufacturing virtual twin as a data-driven, executable digital model that represents real components and operations.

Before building an actual production line, automakers use a virtual environment to implement the production system. In this virtual environment, they virtually verify equipment placement, robot movements, work sequences, and logistics flows, and simulate various production scenarios.

The scope of digital verification is expanding from individual equipment and robot workcells to entire factory layouts and even global supply chains. This discussion begins with the point that the target of application is widening from the equipment level to the supply chain level.

One hypothetical example is introducing a new vehicle model into an existing production line. In this case, verification can be performed in a virtual environment before changing the actual equipment, and the items to be checked include robot trajectories, interference between tasks, and bottlenecks caused by changes in output. If problems are found, the process can be adjusted in the virtual environment, equipment can be modified without physical changes to the hardware, and re-verification is also possible after adjustments.

This approach helps reduce errors during the commissioning process and also helps cut costs and risks. Virtual Commissioning is presented as the actual point of integration between IT and OT.

Virtual Commissioning is a simulation-based process for verifying and optimizing control systems and automation before physical implementation. It requires a virtual twin that reproduces equipment behavior, control logic embedded in the PLC, and a communication interface linking the virtual twin and the control logic.

Verification begins with checking robot cycle times, reach, and interference. It then proceeds to validating a control program prototype through MiL (Model-in-the-Loop), running a control program based on a virtual PLC through SiL (Software-in-the-Loop), and finally connecting actual PLC hardware through HiL (Hardware-in-the-Loop).

This process supports EtherCAT, OPC-UA, and Modbus-TCP. As a result, PLC connections that are not dependent on a specific vendor are possible. Dassault Systèmes says this can reduce commissioning time by 40% to 60%.

The role of the virtual twin is not limited to the period before a production line is built. Once the plant is in operation, data generated by sensors, controllers, and robots is continuously reflected in the virtual twin. As a result, the real production line and the virtual model can be linked.

Dassault Systèmes said that comparing actual equipment behavior with simulation results based on a virtual twin makes it possible to fine-tune production conditions and identify equipment performance and maintenance timing in advance. One automaker improved productivity by 30% with this approach.

Virtual-twin-centered connectivity is also expanding into IT-OT convergence. In this process, the way production data is used changes, and design and simulation data are combined with actual production-floor data.

This combination makes it possible to analyze the cause of problems in a particular process. It is also possible to review outcomes in advance in a virtual environment based on changes in production conditions.

One pharmaceutical company reduced production line changeover time by 40% using this method. It can also review the production conditions that are appropriate when output or product specifications change.

When AI is combined with this, the scope of prediction and optimization in production systems expands. Based on accumulated production data and virtual simulations, it can predict equipment anomalies, predict maintenance needs, and even handle responses to changing conditions.

Across the industry, efforts to combine virtual twins and AI infrastructure continue. As one example of this trend, Dassault Systèmes announced a partnership with NVIDIA last February.

The direction of this partnership is to integrate physics-based AI libraries into DELMIA production-system virtual twins. The goal is to evaluate scenarios across production constraints, resources, and variability.

Another goal is to reduce unexpected situations during the commissioning stage. Related analysis suggests that AI can improve shop-floor productivity by up to 20% in the auto industry and is also being used for quality management based on real-time defect detection by vision systems.

This trend aligns with the direction in which manufacturing environments are evolving beyond standardized, repetitive automation factories. It is described as a move toward a flexible manufacturing environment that continuously reflects real production conditions and responds to change.

Subheadline: Connecting virtual and real production environments with Dassault Systèmes and OMRON. Dassault Systèmes provides a virtual-twin environment based on the 3DEXPERIENCE platform and DELMIA, connecting product design with production system planning, simulation, and operations.

The environment is designed so that 3D-based factory layout modeling and simulation, assembly line modeling and simulation, and robot workcell modeling and simulation can all be handled in the same environment. It also allows workload balancing, resource allocation, and production planning and scheduling to be managed in one place.

In the OEM segment, a 10% to 40% improvement in production throughput was cited. Reductions of 10% to 20% in manufacturing lead time and 10% to 20% in the OTIF gap were also cited.

Recently, cooperation with OMRON has expanded the connection between virtual twins and actual production equipment. This has the character of broader IT-OT convergence.

Announced last April, this partnership was first unveiled at Hannover Messe 2026 in the same month. The two companies are combining Dassault Systèmes' 3D UNIV+RSES and OMRON's industrial automation platform Sysmac to create an integrated environment that spans production-system design, simulation, and verification through actual deployment and operations.

After an actual production line is built, real-time data generated by sensors, controllers, and robots becomes the source. By feeding this real-time data back into the virtual twin, it becomes possible to compare the behavior of the real production system and the virtual model. This makes continuous optimization possible.

This structure can be used to pre-verify production-line performance, safety, and maintenance. It also makes it possible to improve production efficiency during operations.

Smart manufacturing competitiveness in the auto industry is not limited to expanding equipment automation. An important factor is the continuous connection from product design to production-line deployment and actual operating data. Verification in a virtual environment before actual production is also an important element.

Virtual twins and IT-OT convergence serve as the foundation for transforming auto plants into flexible production systems. The direction of transformation is shifting from fixed production equipment to production systems that can be continuously verified and optimized.

Source: TECHWORLD · Lee Gwang-jae
Original: https://www.epnc.co.kr/news/articleView.html?idxno=407742

References

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