Constantin Kiderlen
Managing Consultant
When breakfast meets automotive engineering & sales
Mix-your-own muesli shops are a great reminder of how quickly options multiply. A few choices – say, base grains, fruits, nuts, and toppings – can already result in thousands or even millions of combinations. But not every mix works: some ingredients don’t fit together, some combinations are too bulky for the pack, and others may be off-limits for allergy reasons.
That’s more than just a breakfast problem. In the automotive world, the same pattern applies at industrial scale. Whether you’re building compact cars or heavy trucks, each variant introduces dependencies that impact product data, manufacturing, logistics, and ultimately the customer experience.
Complexity isn’t the enemy – but it needs to be handled
In product development and configuration, complexity often gets a bad name. But in truth, this is partially intentional. Customers want choices, markets demand flexibility, and engineering needs modularity.
The real challenge is making this variety manageable. In practice, two strategies usually coexist:
- Cope with it: Build resilient systems that can absorb complexity without breaking.
- Simplify: Reduce unnecessary variance and focus on meaningful differentiation.
It’s not a binary decision; both strategies are valid and often used in parallel.
Five practical ways to tame complexity
Here are five methods that are gaining traction in the automotive industry. They address the topic from different views, like Sales, R&D or technical product data representation. They don’t eliminate complexity altogether, yet help control its effects and create clarity across teams and systems.
- Built-to-Stock: fewer choices, instant availability
- Guided Configuration: needs instead of features
- In-Car Feature Toggling: less hardware variety, more digital flexibility
- Graph-Based Product Models: making complexity navigable
- AI-Assisted Sales and Data Validation: better decisions, faster
1. Built-to-Stock: fewer choices, instant availability
Rather than letting customers configure every detail, most manufacturers are offering prebuilt models tailored to specific market needs. These predefined variants can be produced in higher volumes, stocked regionally, and delivered faster.
This approach
- cuts down the number of unique configurations,
- simplifies ordering for both customers and retailers, and
- streamlines manufacturing, parts logistics and distribution.
What’s required:
- strong market insights and the capability to derive an optimal stock mix,
- processes and tools for stock steering (“the right vehicle at the right place at the right time”), and
- stock searches that bring together customer needs and available stock intuitively.
2. Guided Configuration: needs instead of features
Some online vehicle configurators are moving away from checkbox-loaded interfaces. Instead, they guide customers through need-based questions, like “I often drive in winter conditions”, and automatically select appropriate features based on rules and availability.
The advantages are clear:
- user experience becomes much more intuitive and abstracted from the model portfolio,
- invalid combinations are caught early, and
- OEMs can suggest optimized equipment combinations in terms of usability and profitability.
To make this work, you need
- a robust rule engine that models product logic,
- semantic mappings of OEM-specific terms to common language, and
- capabilities to determine for each equipment, how good certain customer needs are fulfilled.
3. In-Car Feature Toggling: less hardware variety, more digital flexibility
Modern vehicles often include hardware components that are physically identical but functionally differentiated through software. Think of heated seats or driving assistants that are activated post-purchase or via subscription.
This setup
- reduces hardware variance on the shop floor,
- allows to adjust the configuration of stock vehicles even after production, and
- opens up new revenue streams after delivery.
You’ll need:
- secure over-the-air infrastructure with proper entitlement workflows,
- digital representation of the vehicles’ states to manage on-demand features, and
- suitable processes for subscriptions and payment by customers.
4. Graph-Based Product Models: making complexity navigable
One major blocker for managing complexity is misaligned product data. Graph-based models, used increasingly in development, sales, and service, provide a flexible way to capture relationships, dependencies, and valid combinations. Both for muesli and vehicles.
Benefits include
- state-of-the-art possibilities to define and maintain equipment relations (categories, exclusions, packaging, etc.),
- easier traceability across lifecycle stages and faster impact analysis, especially when defining local market portfolios, and
- minimized buildability conflicts during vehicle configuration.
Implementation depends on:
- re-thinking product management processes and defining a semantic product model that fits your domain,
- graph database tooling that scales, and
- clear governance around data ownership and updates.
5. AI-Assisted Sales and Data Validation: better decisions, faster
Artificial intelligence is a strong ally when complexity scales beyond human capacity. From flagging inconsistent product configurations to managing customer change requests, AI tools can detect patterns and automate decisions far faster than manual reviews.
When customer orders get delayed or stuck—whether due to missing data, unavailable parts, or conflicting configurations—AI tools can recognize these patterns early and suggest corrective actions. This improves the flow from order to delivery and prevents last-minute surprises.
Where it helps most:
- ensuring data consistency across product catalogs,
- handling of impediments and order changes during order processing, and
- reacting quickly to changes in demand or supply.
What it takes:
- an initial setting for a quickly reusable AI tool portfolio,
- well-integrated AI agents that understand product, order and customer semantics.
- a lab mindset for experiments and incremental growth of AI-driven solutions.
Bringing the streams together
These five ways will intersect and could influence each other. Two examples:
- Bringing feature toggling to life opens additional opportunities for built-to-stock vehicles, since you can change the equipment on demand. This increases sales probability and thus reduces standing time, logistics cost and discounts for aging stock.
- Having product data structured as a graph, maintaining and adapting the mapping of needs for guided configurations gets significantly easier.
And despite being only one in five mentioned here, solutions with agentic AI will raise in all those fields. Let it be the stock mix or distribution determined by AI, or the AI-assisted configuration based on common language, or assessing restriction-triggered order changes: AI is capable of handling loads of data, deducting decisions and also being in a spoken dialogue with customer, internal users and even suppliers.
However, this is large enough for a dedicated blog post.
Why Senacor is a fit for these challenges
Managing product complexity isn’t just a technical problem – it’s a question of strategy, collaboration, and the right foundation. At Senacor, we bring together deep automotive expertise with proven IT delivery skills. From designing scalable configuration systems to implementing modern data architectures, we know what it takes to align business goals with technical execution.
Our teams work closely with OEMs and suppliers across domains, helping them make complexity transparent and manageable. Whether it’s building AI-powered sales tools or modular stock vehicle solutions – we don’t just design the blueprint. We make it work.
Final Thought: complexity is a design decision
Product variety isn’t going away. In fact, it’s becoming central to how vehicles are developed, sold, and maintained. The key is to design for it. To use the right tools, processes, and mindsets that turn complexity from a headache into a competitive advantage.
So next time you’re facing a product portfolio that feels a bit like a muesli bar with too many toppings: maybe the question isn’t “How do we reduce options?” but “How do we structure them intelligently?”
Transparency note: AI supported the structure and visuals, while the content is based on the author’s own experience and perspective.
CONSTANTIN KIDERLEN
Managing Consultant
mobility@senacor.com