· 4 min read

Accelerating Time-to-Market in the Age of Electrification: How Graph Databases Pave the Road to the Transformation of Automotive Manufacturing

The article explores how the automotive industry can leverage AI and graph technologies to solve eBOM mBOM, and thus accelerate time-to-market.

The article explores how the automotive industry can leverage AI and graph technologies to solve eBOM mBOM, and thus accelerate time-to-market.

The automotive industry is at a pivotal point, moving aggressively toward electrification and carbon neutrality. With environmental regulations becoming more stringent and innovation being pivotal, automotive enterprises, be it suppliers or manufacturers, are compelled to optimize their product development cycles to maintain a competitive edge. Sluggish time-to-markets are not just operational bottlenecks but strategic impediments, hindering competitiveness and market responsiveness in a rapidly evolving sector.

The Root of Slow Time-to-Markets

Exploring the root-cause of these prolonged time-to-markets, one can identify two things that are directly linked to the data architecture of the company: the fragmented communication across the value chain (engineering, manufacturing and supply) and suboptimal management of burgeoning product data (an under of years of enterprise knowledge).

Fragmented communication across the value chain stems from the existing business softwares. Legacy systems like CAD, PLM, and ERP brought enormous gains for vertical functions but fostered silos that impede the flow of essential information through various stages of development.

In the automotive industry, the conventional approach to Bill of Materials (BOM) – involving both engineering (eBOM) and manufacturing (mBOM) – is fettered with constraints that overlook critical details, stifling innovation and adaptability.

The suboptimal management of product data is mainly due to the data structure of legacy systems (that are based on relational databases) which impedes the ability to draw links between objects across the value chain (products, parts, suppliers) or define the rules of how those relate to each other.

Those who want to win the race towards electrification will have to implement smart, agile, and collaborative frameworks that drive synergy and coherence across the diverse functions of the value chain.

The Advent of Graph Databases Unlocks New Strategic Opportunities for Automotive Companies

Graph databases bring unprecedented possibilities in accelerating time-to-market, offering a dynamic and fluid architecture that facilitates the integration, synchronization, and enrichment of technical information. This structure transforms the traditional BOM into a synchronized, intelligent tool, linking business constraints with components knowledge, and fostering innovation at an expedited pace. Knowledge Graph

Business Dividends: Accelerated Electrification and Market Dominance

A strong graph data platform, by unleashing engineering capabilities, is expected to have several key business outcomes

  • Better reaction and adaptation to market dynamics and client demand
  • Significant cost savings, namely by reducing NRE (non-recurring engineering costs)
  • Increased portfolio profitability through standardization, automation of dataflows and generative design
  • Faster response to regulatory mandates by capturing data through the Value Chain

Automotive companies have a new way of developing their competitive advantage which is the wave of AI and analytics, powered by the graph platform.

Tesla’s Unprecedented Leap

In the real-world business sphere, Tesla vividly illustrates the potential of this transformative approach. By building its own graph platform, Tesla managed to trim its time-to-market by a staggering third, effectively outpacing competition.

This significant reduction in development cycles has enabled Tesla to pioneer innovations, promptly respond to market demands, and secure a substantial market share in the burgeoning electric vehicle landscape. Tesla’s success story stands as a tangible testament to the massive strides possible through the strategic application of graph database technology, showcasing the ability to not just keep up with, but actively define, the industry pace.


Embarking on the Transformation with Cognyx

To counter the difficulty of building such a platform in-house, Cognyx aims at offering a ready-to-use no-code platform in order to allow industrials to benefit from the power of Graph in a matter of weeks.

Indeed in a span of 6 to 8 weeks, Cognyx aids manufacturing entities in establishing a single source of truth for BOM and creating the companies’ knowledge graph.

This journey involves manipulating and automating data to deliver value, and constructing an ecosystem of apps (be they legacy systems of SaaS) that leverages the knowledge graph, enabling previously inconceivable collaborations.

By fostering a deep understanding of the product portfolio, Cognyx paves the path to deep insights and analytics.

In conclusion, as the automotive industry forges ahead toward a future grounded in electrification and carbon neutrality, the strategic acceleration of time-to-market through graph databases emerges as pivotal. Cognyx stands as a testament to the potential of this transition, championing a future where innovation is swift and strategically aligned with the evolving landscape. It’s a call to forge partnerships echoing with the pulse of innovation, embarking on transformative journeys grounded in insight, agility, and a forward-looking outlook.

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