Why use fragmented tools for modern error management?
It’s clear: Modern embedded systems, integrating electrical, electronic (E/E), and software components, are incredibly complex. Each subsystem introduces intricate interdependencies that complicate not only design and development but also error management.
And yet, information and knowledge are often distributed across different systems and departments within the same organization, making it inaccessible to all engineers working on the same product.
Traditional error management methods like manual processes or pinpointing issues using disconnected tools are no longer effective in this context. This fragmentation results in slowdowns and inefficiencies, preventing engineers from quickly identifying root causes. This creates a frustrating environment where teams spend more time addressing recurring issues rather than focusing on the development of innovative features. But the focus of R&D should be on innovation, not troubleshooting repetitive problems.
The negative impact of expert dependency and siloed engineering
For Function developers, Hardware and EE responsibles the stakes are high. Especially in the modern automotive environment, identifying and resolving errors quickly is crucial for maintaining production efficiency. Every day of production delay can cost up to 1 million euros, in addition to damaging the brand’s reputation.
The following factors make troubleshooting in R&D even more time-consuming and dependent on expert knowledge:
- Limited access to valuable engineering data
- Inability to map complex interdependencies with traditional tools
- Manual search in diagnostics
- Over-reliance on unsustainable expert knowledge
- Existing systems unsuitable for cross-domain collaboration
The consequence: Rising costs and lost innovations
In such a fragmented environment, error management is inefficient. The financial impact of production errors is significant. Misdiagnosed or undetected errors can lead to:
- 50% of expert’s time spent on search for information on error sources
- Reliance on expert knowledge makes error search more challenging
- Less time spent on innovations due to time-intensive admin tasks
- Increased operational costs, up to 1 mio euros per each day of delay
- Missed SOPs
The root cause of these issues lies in the inadequate tools currently available for managing complex, software-driven vehicle architectures. Without the right systems in place, troubleshooting becomes dependent on the knowledge of a few experts, and the time to resolution increases significantly.
What if there was a better way?
With a solution that links user features like ambient lighting, seat heating, and head-up displays to the physical wiring harness and electric components, engineers would have a complete understanding of how these various systems interact with each other.
By integrating logical effect chains in one source of truth, engineers could see interdependencies between functions, connections, components, and errors.
Furthermore, with advanced topology integration, electrical communication would no longer be limited to the physical layer. The solution would display interactions on the logical layer as well, linking signals from the physical harness to buses and protocols in the logical topology. This would provide even greater level of visibility into how components communicate, enhancing troubleshooting and error prediction.
What if there was a solution that:
- Connects relevant systems of records across the whole product lifecycle with smart connectors to one single platform (integrated product data).
- Specifies relevant dashboards, so that everyone gets intuitive access to up-to-date product descriptions, such as 3D coordinates of connectors and wires in reference to 3D wiring harness geometries, DTC mapping lists etc. in dedicated applications.
- Enables case-specific overview of fault hotspots for predictive fault case identification.
- Connects impact chain data from various systems to KPM ticket information, ensuring error tickets are routed to the appropriate resolution teams – with the correct contact person identified.
- Makes PDF-files with 1000s of pages obsolete.
Cut error search time & predict faults before they happen – with SPREAD
To achieve this ideal state, engineers need a future-ready platform designed to intelligently connect all relevant product data and make it accessible in real-time. It needs to transform error management by integrating data from all domains and providing an intuitive interface everyone involved in the process.
By integrating all relevant data into a single platform and providing intuitive dashboards, companies will reduce the time spent on error detection, improve product quality, and cut operational costs.
Key features of SPREAD’s Engineering Intelligence solution include:
- Smart Connectors for integrated product data: SPREAD links systems such as PLM, ERP, and wiring harness databases, providing a unified view of all product data
- Intuitive, customizable dashboards: Engineers can access specific dashboards with critical information such as 3D coordinates of connectors and wires, enabling faster error diagnosis and repair.
- VIN-specific 3D views for predictive maintenance: Engineers can proactively identify fault hotspots based on vehicle identification numbers (VIN), ensuring that potential issues are prevented before they impact production.
The impact of SPREAD’s E/E Inspector, the guided error management solution
Implementing SPREAD’s E/E Inspector delivers measurable improvements in error management and production efficiency:
- Up to 70% reduction in error search time: Engineers can access all the necessary data in real-time, improving first-pass yield.
- 90% accuracy in error prediction: By leveraging predictive analytics and VIN-specific views, teams can identify potential fault hotspots before they occur.
- Up to 66% reduction in error processing time with up to 75% reduced process steps.
- Up to 50% faster detection of duplicate errors.
SPREAD’s solution positions automotive companies to face the challenges of modern production with greater agility and efficiency. Production downtimes and delays due to late undiagnosed errors in R&D have become problems of the past.
Impact beyond R&D: A glimpse into Production and Aftermarket
While the SPREAD solution is tailored to optimize R&D efforts, its benefits extend into the Production and Aftermarket phases, as well.
Production: Engineers spend up to 50% of their time manually navigating through 2D wiring diagrams or chasing down elusive data points in siloed databases. With all relevant data unified and accessible, production downtimes due to unidentified issues are minimized, reducing costs associated with expert dependency and misdiagnosed errors.
- Increase inline rework efficiency
- Higher first-pass yield
- With faster error resolution, production lines can handle up to 5 additional vehicles per day.
Aftermarket: The same centralized data repository aids in diagnosing post-market issues quickly. Service teams can access historical data, error patterns, and configurations tied to specific vehicle identification numbers (VINs), leading to faster and more accurate troubleshooting.
- Up to 50% lower warranty costs
The power of our Product Twin for guided error management
SPREAD's Product Knowledge Digital Twin (PT) provides a centralized source of truth that connects and unifies data from PLM, ERP, and custom databases into a single, intuitive interface. At the heart of the PT is the Knowledge Graph technology, a powerful tool for visualizing and managing complex systems by representing entities (nodes) and their relationships (edges). It is able to combine structured data from other tools and systems, as well as unstructured data from e.g. requirements.
Graph databases, unlike traditional databases, excel at managing and querying all these complex relationships, making it easier to track and optimize the flow of data and interactions.
Beyond centralizing data, the PT leverages, connects, and analyzes comprehensive information related to a physical product within an extensive parametric model known as the Engineering Intelligence Graph. This advanced system delivers complete, up-to-date product intelligence that can combine structured and unstructured data, enabling engineers to address complex challenges effectively.
In R&D and production environments, this technology is particularly useful for understanding intricate connections between components—such as a vehicle's wiring harness and its links to various systems.
For production executives and engineers, this means faster troubleshooting, better visibility into system dependencies, and more efficient management of complex mechatronic systems, such as tracking how specific wires connect to pins and components in a vehicle's electrical system.
Key capabilities of SPREAD’s Product Twin include:
Intelligent Data Integration
SPREAD's PT ensures seamless integration of data from various systems, such as Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), and custom databases. Through its Data Ingestion & Mapping layer, it brings together structured and unstructured data across domains, transforming disconnected datasets into a cohesive, accessible intelligence layer.
This intelligent integration minimizes data silos, enabling teams to see a unified view of product-related information in real-time. The result is a comprehensive data foundation that supports accurate, data-driven decisions and minimizes the chances of overlooked dependencies or blind spots during complex change processes.
App-based analysis & workflows
With SPREAD’s low-code/no-code platform, engineering teams can create custom applications to analyze data and manage workflows effortlessly. These applications are purpose-built to handle product engineering tasks, from impact analysis to real-time monitoring.
This app-based approach ensures engineers can adapt workflows to project needs rapidly, optimizing productivity and enabling precise analysis without extensive technical configuration. The platform supports intuitive app-building with pre-built templates, making it easy to adapt the solution to changing requirements or new insights.
Cross-domain change management
SPREAD’s PT connects data across multiple engineering domains, providing a cross-functional view essential for managing change effectively. By leveraging its Engineering Intelligence Graph, the system enables end-to-end traceability, mapping out the impact of changes across R&D, Production, and Aftermarket phases.
The supergraph and federated subgraphs provide insights that span domains, allowing teams to anticipate how changes in one area might impact another, thus reducing costly rework and ensuring that decisions are made with a holistic understanding of the product.
Easy knowledge management & sharing
SPREAD simplifies knowledge management by centralizing all product-related information within a unified Digital Twin. Knowledge management is further enhanced by SPREAD’s Studio Share feature, which allows for secure, role-based access to data, making knowledge sharing effortless across teams and with external partners.
This setup provides engineers and other stakeholders with on-demand access to up-to-date insights, thereby enhancing collaboration and reducing the friction associated with sharing complex product information.
End-to-end collaboration
The PT fosters collaboration across all stages of product development and lifecycle management, providing tools for real-time updates, version control, and collaborative problem-solving. The unified view within SPREAD’s platform makes cross-functional alignment simpler and quicker, breaking down traditional barriers between teams.
Stakeholders across engineering, management, and production can track progress, identify bottlenecks, and align resources dynamically, ensuring projects move forward smoothly and with full visibility across all departments.
Enabling Artificial Intelligence capabilities
AI is embedded in the core of SPREAD’s PT, transforming how engineering data is interpreted and applied. With a network of Engineering Intelligence Agents, the platform offers predictive insights, automated analysis, and decision support that significantly reduce the manual effort engineers typically spend on data analysis and troubleshooting.
These AI agents bring advanced capabilities, such as similarity searches, automated change impact validation, and real-time performance monitoring, empowering engineers to make proactive decisions and focus more on innovation than routine tasks.
Conclusion: Reduce operational costs with real-time insights
As automotive systems grow more reliant on software and E/E architectures, traditional error management methods are no longer viable. By focusing on error resolution during the R&D phase, SPREAD lays the groundwork for efficient and error-free processes throughout the entire product lifecycle.The SPREAD E/E Inspector solution offers the real-time data visibility and predictive capabilities required to tackle these modern challenges. By reducing error search time, enhancing fault prediction, and improving production line efficiency, SPREAD empowers automotive manufacturers to stay ahead of the curve and maintain their competitive edge.
Get to know our E/E Inspector: One demo, all features
