SDV Guide
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  • Welcome
  • SDV101
    • Part A: Essentials
      • Smart Phone? No: Habitat on Wheels!
      • Basics: What is a Software-defined Vehicle
      • MHP: Expert Opinion
      • Challenges: What sets automotive software development apart?
      • SDV Domains and Two-Speed Delivery
    • Part B: Lessons Learned
      • Learnings from the Internet Folks
        • Innovation Management
        • Cloud Native Principles
          • DevOps and Continuous Delivery
          • Loose Coupling
            • Microservices & APIs
            • Containerization
            • Building Robust and Resilient Systems
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      • Foundation: E/E Architecture
        • Today`s E/E Architectures
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        • Case Study: Rivian
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          • Example: Real-World Application of SDV Concepts
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          • Event Chains in Vehicle SOAs
          • Vehicle SOA Tech Stack
        • Over-the-Air Updates: The Backbone of Software-Defined Vehicles
        • Vehicle App Store: The Holy Grail of Software-Defined Vehicles
      • Summary: Building Blocks for Software-Defined Vehicles
    • Part D: Implementation Strategies
      • #DigitalFirst
      • Hardware vs Software Engineering
        • The Traditional V-Model in Automotive Development
        • Agile V-Model, anybody?
        • Key: Loosely Coupled, Automated Development Pipelines
        • The SDV Software Factory
      • Implementing the Shift Left
        • Simulation and Digital Prototyping
          • Early Validation: Cloud-based SDV Prototyping
          • Detailed Validation: SDVs and Simulation
        • Towards the Virtual Vehicle
          • Case Study: Multi-Supplier Collaboration on Virtual Platform
          • Long-Term Vision
        • Physical test system
        • De-Coupled, Multi-Speed System Evolution
        • Continuous Homologation
        • Summary and Outlook
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        • Engineering Intelligence
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        • Incumbent OEMs vs EV Start-ups
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    • Digital First
    • Loose Coupling
      • API-first
      • Freeze Points
    • Automation and Engineering Intelligence
    • Continuous Homologation
    • Build / Measure / Learn
  • Glossary
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(c) 2025 Dirk Slama

On this page
  • Introduction
  • McKinsey Case Study
  1. SDV101
  2. Part D: Implementation Strategies
  3. Implementing the Shift Left

Simulation and Digital Prototyping

PreviousImplementing the Shift LeftNextEarly Validation: Cloud-based SDV Prototyping

Last updated 6 months ago

To successfully implement Shift Left, simulation and prototyping play a critical role in accelerating development cycles and ensuring higher-quality outcomes. As shown in the diagram, both approaches address key challenges in the early phases of the development process.

Introduction

Prototyping focuses on reducing project risk by validating ideas early and confirming that teams are building the right product. By testing and evaluating concepts with end-users at the earliest stages, prototyping ensures alignment between development goals and user expectations, avoiding costly course corrections later in the process.

On the other hand, simulation is essential for predicting system performance and ensuring that the product is being built right. Simulation allows teams to assess existing systems, test planned changes, and evaluate alternative solutions in a virtual environment before any physical implementation occurs. This significantly reduces the dependency on time-consuming physical prototypes and enables rapid iteration and optimization.

In the context of Shift Left, combining prototyping for validation and simulation for performance prediction helps identify and resolve issues as early as possible, minimizing downstream risks and driving efficiency throughout the development lifecycle. Together, they ensure that teams can move faster, improve quality, and deliver better outcomes with fewer surprises.

McKinsey Case Study

A recent McKinsey Case Study highlights three critical levers for improving testing and validation efficiency during product development and ramp-up phases:

  1. Push on Virtualization: By leveraging virtual simulation and digital models, teams can test and validate designs early, reducing risks and optimizing decisions with tools like 3D modeling.

  2. Optimize Use of Prototypes: Physical prototypes remain necessary, but their usage is streamlined through rapid prototyping, frontloading tests, and better coordination. This reduces testing costs and efforts significantly.

  3. Shift Tests to Preseries: Final validation happens on pre-series vehicles using series tools, enabling earlier detection of production-related issues and ensuring high-quality outcomes.

The level of impact spans from revolution—through virtualization—to evolution as processes mature.

To emphasize the importance of prototyping, McKinsey references a case study from an aircraft manufacturer that implemented a virtual development platform shared with 27 partners. This collaborative approach resulted in a 50% reduction in assembly time and a 66% decrease in tooling costs, demonstrating the enormous value of virtual tools and optimized prototyping. This example underlines how combining virtual and physical testing accelerates development while maintaining quality and cost efficiency.

Testing and validation: From hardware focus to full virtualization?McKinsey & Company
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Source: McKinsey