Passenger Vehicles

The objective of virtualization is not to eliminate physical testing completely. The optimal balance depends on product complexity, organizational maturity, validation requirements, and software content. Manufacturers typically achieve the greatest benefits when virtualization focuses on repetitive, expensive, or time-consuming development activities. The largest gains usually come from integrating virtualization into development processes rather than introducing simulation tools in isolation.

Prototype costs are often driven by system integration, validation, commissioning, calibration, and late-stage issue resolution rather than by prototype hardware alone. As software content and E/E complexity increase, physical prototypes can become a major bottleneck for timing and cost. Many manufacturers therefore focus on shifting commissioning, testing, and validation activities into virtual environments before prototype builds occur.

Electrification challenges are increasingly driven by economics rather than technology readiness. Profitability is influenced by battery costs, platform utilization, vehicle positioning, manufacturing scale, software content, charging strategies, and regional market dynamics. Organizations often underestimate the long-term implications of architecture and portfolio decisions made early in the development process.

Extending an existing architecture can reduce investment requirements and accelerate market entry, while a new architecture may provide greater flexibility for future technologies, software-defined functionality, and electrification requirements. FEV Consulting typically helps clients evaluate this decision based on expected market lifetime, technology roadmap requirements, customer expectations, development effort, and long-term competitiveness.

In FEV Consulting projects, we frequently observe that organizations focus on technology before addressing operating model implications. While new software platforms and E/E architectures are important, long-term success often depends on engineering processes, systems engineering practices, governance structures, capability development, and organizational alignment. In many cases, software-defined vehicle programs become organizational transformation initiatives rather than pure technology projects.

As software content increases, manufacturers are strengthening capabilities in systems engineering, software integration, architecture management, model-based development, virtualization, and cross-domain collaboration. The most successful organizations combine software expertise with deep vehicle engineering knowledge to manage increasing system complexity without sacrificing development speed or product quality.

Development acceleration is rarely achieved through additional resources alone. The largest opportunities often come from reducing rework, improving decision quality, increasing engineering transparency, strengthening front-loading activities, and expanding the use of virtualization and systems engineering practices. Organizations that improve development efficiency early in the process are generally better positioned to maintain quality while reducing time-to-market.

AI use cases should not be prioritized only by the largest isolated value potential. For engineering organizations, scalability is often the more important decision criterion. A use case that can be adopted across several development processes, engineering functions, or vehicle programs may create greater impact than a highly specific use case with limited rollout potential. Prioritization should therefore consider business value, implementation feasibility, data availability, process fit, and organizational scalability.