<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=5292226&amp;fmt=gif">
Skip to content
All Posts

Mastering product complexity: how to meet industry standards and customer expectations

by SPREAD Team on
Engineering Product

As software becomes a critical revenue driver in the automotive industry, manufacturers are facing an unprecedented challenge—managing the increasing complexity of connected, software-defined vehicles. The transition toward digitalization, electrification, and advanced driver assistance systems (ADAS) has made modern vehicles more interconnected than ever. However, this shift also exposes engineers to a fragmented data landscape, making it difficult to track dependencies, ensure data quality, and control costs.

In traditional automotive engineering software, system complexity often leads to unknown dependencies, inefficiencies, and compliance risks. Engineers must sift through disparate tools and legacy systems to gather the necessary information, leading to delays and costly design errors. Without a holistic view of vehicle architectures, teams struggle to make informed decisions and meet evolving customer expectations and industry standards.

To overcome these challenges, the industry must embrace Engineering Intelligence solutions such as Knowledge Graphs. By contextualizing engineering data and visualizing dependencies across vehicle systems, Knowledge Graphs provide end-to-end engineering data integration, enabling teams to understand how software, electronics, and wiring interact within a complex system. This approach allows automotive OEMs and suppliers to improve decision-making, reduce inefficiencies, and ensure seamless collaboration across departments.

Join Daniel Metzinger, Senior Solution Manager at SPREAD, as he explores how Knowledge Graphs are transforming automotive engineering. Discover how they:

  • Enable holistic product lifecycle visibility
  • Improve data traceability to enhance compliance with safety and industry standards
  • Address software-defined vehicle complexity by creating a structured, interconnected data model
  • Help engineers visualize dependencies, eliminate inefficiencies, and accelerate product development

The future of automotive engineering lies in intelligent data management. Watch the full session now to see how automakers and suppliers can stay ahead in the era of software-defined manufacturing.