Automotive and auto parts-Leading Technology

Automotive and auto parts

The platform merges the real and digital worlds through digital threads, streamlining processes and helping automotive companies become sustainable digital enterprises more quickly and gain a competitive edge.

Industry pain points

Solution

Application scenarios

Application value

Software-Driven Innovation Increases Complexity

• Technological Challenges: As cars increasingly become software-defined mobile terminals, the amount of software code has increased dramatically. This not only requires higher technical capabilities for development and maintenance but also increases system complexity and integration difficulty. For example, the electronic architecture of modern cars needs to support multiple functions, such as autonomous driving and intelligent connectivity, making the development and testing process more complex.

• Extended Development Cycles: Software-driven innovation requires longer development cycles to ensure system stability and security. This contradicts the rapidly changing demands of the market, requiring companies to find a balance between quickly launching new products and ensuring product quality.

Sustainability Regulations Require Effective Net-Zero Strategies

• Regulatory Pressure: Global environmental requirements for the automotive industry are becoming increasingly stringent, such as the EU's carbon emission standards and China's dual-credit policy. These regulations require automakers to reduce carbon emissions during production and promote the development of new energy vehicles. • Supply Chain Management: The complexity of the automotive supply chain increases the difficulty of achieving sustainable development. Companies need to ensure that the entire supply chain, from raw material procurement to manufacturing and product recycling, complies with environmental standards. For example, Geely Holding Group has developed the GeeTrace blockchain traceability platform to achieve full lifecycle traceability from complete vehicles to raw material miners, ensuring the transparency and sustainability of the supply chain.

**Adaptability and Sustainability of Product Development, Production, and Supply Chain** • Rapid Market Changes: Rapidly changing consumer demands require automakers to quickly adjust product development and production plans. For example, increasing consumer demand for personalization and customization requires companies to have flexible production capabilities and the ability to respond quickly to the market. • Supply Chain Resilience: Global pandemics and geopolitical factors have posed serious challenges to the stability of the automotive supply chain. Businesses need to strengthen the resilience and transparency of their supply chains to ensure the stability of raw material supply and the continuity of production processes. • Cost Control: Under the requirements of software-driven innovation and sustainable development, businesses need to control costs while ensuring product quality and environmental performance. This requires businesses to optimize production processes, improve production efficiency, and reduce waste.

**The Necessity of Digital Transformation** • Agile Development: Through digital tools and agile workflows, automakers can accelerate product development cycles and improve market responsiveness. For example, Volkswagen has significantly shortened the development cycle of new models and improved product quality and consistency through the PEP process and digital twin technology. • Smart Manufacturing: By leveraging big data, artificial intelligence, and IoT technologies, businesses can achieve intelligent management of production processes, improving production efficiency and product quality. For example, BMW uses digital twin technology to optimize the operation of its car assembly plants in real time, improving production efficiency and flexibility. • Supply Chain Collaboration: Digital platforms enable collaboration between upstream and downstream enterprises in the supply chain, improving transparency and efficiency. For example, Geely Holding Group uses the GeeTrace platform to achieve full lifecycle traceability of its supply chain, ensuring its sustainable development.

Addressing Automotive Manufacturing Challenges Through Digitalization

Maintaining a Leading Position in the Competition Through Successful Business Transformation

Siemens' Siemens Xcelerator platform streamlines processes and helps automotive companies become sustainable digital enterprises and gain a competitive edge faster by merging the real and digital worlds through digital threads. Digital threads are traceable paths through digital business workflows throughout the product and production lifecycle, and Siemens offers a wide range of solutions that automotive companies can use to successfully transform into sustainable digital enterprises.

Achieving Automotive Manufacturing Transformation

Transform your manufacturing operations to enable the next generation of mobility. Embrace digitalization and leverage the advantages of virtual representation, simulation, and data to become a fully digital enterprise. Adapt your manufacturing processes to accommodate various product variations to meet the needs of global customers and your sustainability goals.

Exploring Three Key Paths to Building a Digital Pillar:

Virtual Manufacturing Engineering

Combining the Digital and Real Worlds: Virtual manufacturing technology integrates digital models with real-world production environments to achieve automated design. For example, Siemens' virtual manufacturing technology is widely used in all aspects of manufacturing, including product design, site planning, production decisions, process flow, testing, assembly guidance, remote monitoring, scheduling, skills training, marketing, and after-sales service. Achieving Operational Flexibility: Virtually commissioning equipment and production lines ensures rapid and successful start-up. Virtual commissioning technology simulates and verifies long-term, uninterrupted production of manufacturing systems, assesses program stability, and identifies and corrects errors in advance. Reducing Costs and Start-up Time: Simulating energy and material usage enables sustainable production. For example, Siemens' digital twin technology helps companies design products and plan processes in a virtual environment, reducing the use of physical prototypes and lowering development costs.

**Rapid Factory Transformation**

Advanced Automation and Digital Twins: Connecting IT and OT by leveraging AI, machine learning, IIoT, edge computing, cloud, and the industrial metaverse. For example, Siemens' digital twin technology enables interconnectivity between equipment and systems, real-time monitoring of equipment status, collection of production data, and remote monitoring, predictive maintenance, and intelligent scheduling.

Establishing a Single Source of Product and Process Data: Ensuring consistency and accuracy of product and process data through digital threads. For example, Siemens' digital thread technology enables full lifecycle data traceability from raw materials to the final product, ensuring supply chain transparency and sustainability.

Virtual Solution Training: Using virtual reality technology to train maintenance personnel reduces training time and minimizes the risk of human error. For example, Metso's virtual training solutions provide technicians with a risk-free environment that allows them to practice skills as many times as needed until fully mastered.

Smart Manufacturing Excellence

Autonomous, predictable, and responsive manufacturing ecosystem: Real-time monitoring and adjustment of processes, optimization of supply chain operations and logistics, moving towards carbon neutrality. For example, Siemens' smart manufacturing solutions enable precise control of production processes, improving production efficiency and product quality, while optimizing supply chain management and reducing operating costs.

Comprehensive understanding of every step of operations: Eliminating unplanned downtime in production by identifying and mitigating problems through automated anomaly detection. For example, Alibaba Cloud's intelligent operations and maintenance framework uses machine learning algorithms to analyze historical data, monitor key performance indicators in real time, and trigger automated repair processes when potential problems are detected.

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LINGYU Technology's intelligent manufacturing system provides comprehensive solutions for the automotive and auto parts industry, optimizing every stage from product design and production to quality control.In particular, its real-time monitoring and analysis of production data help enterprises identify and resolve issues in a timely manner, enhancing product quality and production efficiency.

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