Variant Management

Vehicle variants refer to the numerous configurations of a vehicle model created to meet market demands, regional specifics, and regulatory requirements, as illustrated in the diagram. Customers may customize their vehicles using options like engine power, wheel size, seat type, or color via a sales configurator. Additionally, variations are introduced to comply with region-specific regulations or preferences, such as emission standards, safety requirements, or driving habits.

While these variants are essential to satisfy customer preferences and address diverse market needs, they introduce significant challenges across the entire vehicle lifecycle. From design and engineering, the need to accommodate multiple combinations of features increases system complexity, requiring sophisticated management tools and processes. In manufacturing, the growing number of variants complicates production lines, demanding flexible assembly processes and increasing costs. Once on the market, the maintenance of these vehicles becomes equally challenging, as each variant may require specific parts, diagnostics, and updates.

Incumbent OEMs vs EV Start-ups

Incumbent OEMs, both in the mass market and luxury segments, face particularly high variant complexity. Luxury manufacturers cater to customers with extremely high levels of personalization, offering a wide range of options to differentiate their vehicles. This results in significant engineering and manufacturing complexity but is necessary to meet premium customer expectations. Mass-market manufacturers, on the other hand, balance customization with production efficiency, offering fewer options but still managing significant complexity due to high volumes and broad market coverage.

In contrast, EV start-ups take a fundamentally different approach to variants. With a focus on simplicity, they limit customization options and portfolio complexity. Their vehicles are often designed with a smaller number of configurations, reducing engineering and manufacturing overhead. Instead of hardware-based personalization, EV start-ups rely on software-driven features to provide differentiation, such as over-the-air updates and digital services. This approach allows them to streamline production, lower costs, and adapt quickly to market demands.

The Variant Space

The variant space refers to the total number of mathematically possible combinations of vehicle features and configurations. This space grows exponentially as more options are introduced. For example, a low-end car with around 50 features and 3 options per feature results in over 718 trillion possible combinations. In contrast, a high-end car with 200 features and 10 options per feature generates an astronomically large number of possibilities.

This sheer scale of combinatorial complexity has significant implications. From an engineering perspective, every variant must be validated to ensure it meets performance, safety, and regulatory standards. In manufacturing, the variant space complicates production lines, as assembly must adapt to an immense range of configurations. Finally, in maintenance, managing spare parts, diagnostics, and software updates for such a large number of variants becomes a logistical challenge.

Variant Management Tools

To manage this complexity, OEMs rely on specialized variant management tools that integrate across various systems within the engineering and production lifecycle. These tools help track, configure, and validate variants efficiently, ensuring consistency and reducing errors. They work in close alignment with Model-Based Systems Engineering (MBSE) to define and analyze variant behavior early in the design process, ensuring all requirements are met.

Variant management tools are also tightly connected to Product Lifecycle Management (PLM) systems, where product configurations, dependencies, and lifecycle information are centrally managed. In parallel, Computer-Aided Design (CAD) systems provide detailed design models that accommodate variant-specific features, while Manufacturing Execution Systems (MES) ensure that production lines adapt seamlessly to the required combinations.

Further upstream, sales configurators enable customers to select their preferred vehicle options, directly feeding into the variant management ecosystem. This ensures a smooth flow of information from customer choices to engineering design, manufacturing planning, and final production. By connecting these systems, OEMs can streamline variant handling, reduce complexity, and maintain a single source of truth across the entire lifecycle.

Handling Variants in Software-Defined Vehicles (SDVs)

Software-Defined Vehicles (SDVs) must be capable of addressing vehicle variants dynamically, as software algorithms must operate reliably across diverse configurations. For SDVs, algorithms need to be developed and tested with multiple variants in mind, ensuring compatibility and seamless functionality.

Take the Passenger Welcome Sequence as a simple example: this feature involves actions like seat adjustments and dashboard illumination when a driver enters the vehicle. In vehicles equipped with seat adjustment, the algorithm must include logic to trigger seat movement. However, for vehicles without seat adjustment, the algorithm must bypass this feature without failure. Similarly, the feature must adapt to both left-hand drive and right-hand drive configurations, accounting for sensor and actuator placements that differ across these variants.

There are two primary approaches to make SDV algorithms aware of variants:

Explicit Configuration Feeding: The concrete vehicle configuration is explicitly provided to the software, allowing the algorithm to adapt its behavior based on predefined inputs. This method ensures clarity but requires consistent management of configuration data throughout the vehicle lifecycle.

Dynamic Detection: The algorithm dynamically detects the availability of sensors, actuators, and features during runtime. By querying the system for accessible components, the algorithm can adapt to the specific vehicle configuration autonomously. This approach enhances flexibility but demands robust detection mechanisms and fallback logic to handle missing components gracefully. Also, Homologation is probably much more difficult to achieve for this approach, at least with current Homologation processes between OEMs and approving agencies in place.

In summary, SDVs must manage variants at the software level, ensuring that algorithms like the Passenger Welcome Sequence can adapt seamlessly across different configurations. Whether through explicit configuration feeding or dynamic detection, handling variants effectively is essential for delivering a consistent and reliable user experience in the face of growing vehicle complexity.