Why this article is worth your time:
- Learn how Snowflake's AI Data Cloud addresses automotive industry shifts like connected vehicles, autonomous driving, electrification and Industry 4.0.
- Find out how Snowflake's platform can be used by auto manufacturers to integrate IT, OT and IoT data to accelerate deployment of AI-driven solutions for predictive maintenance, demand forecasting and production optimization.
- Get insights on how Snowflake enables OEMs and suppliers to securely share real-time data, improve supply chain visibility, enhance production quality and reduce costs with actionable insights from unified analytics.
Snowflake, a supplier of cloud, data and AI technologies, has expanded its AI Data Cloud for Manufacturing with a focus on automotive-specific solutions.
The company cited four key trends in the automotive industry driving Snowflake’s expansion in this manufacturing vertical: connected and software defined vehicles, autonomous driving, electrification and advanced manufacturing (Industry 4.0).
According to Snowflake, each these factors generates massive volumes of data across vehicle development, manufacturing, supply chain and after-sales services. Snowflake’s data sharing and AI capabilities are designed to enable automotive industry suppliers, original equipment manufacturers (OEMs), distribution, sales and service providers to collaborate on vehicle development and optimize production processes by accessing real-time data insights across the automotive value chain.
The company noted that 80% of major automotive OEMs already use Snowflake's platform for their data and AI initiatives.
To learn more about what Snowflake’s expanded technology offering for the automotive industry means to manufacturers in this vertical, Automation World connected with Tim Long, global head of manufacturing at Snowflake.
AW: Does Snowflake have pre-built analytics or AI/ML models for auto manufacturers and their suppliers related to this expanded offering?
Long: What makes Snowflake unique for automotive manufacturing is our ability to enable the convergence of IT, OT and IoT data, which is critical for effective AI/ML (artificial intelligence/machine learning) implementation. This convergence allows for more comprehensive analytics and AI/ML use cases by bringing together diverse data sources — from factory floor sensors and equipment logs to ERP and CRM systems, along with high-velocity data from connected vehicles.
Our partner ecosystem includes companies like Siemens and DXC Technology, who offer specialized solutions and expertise. For example, Siemens accelerates software-defined vehicle innovation through digital twin simulations and AI-driven virtual validation that enhance automotive system development.
Snowflake's strength lies in enabling manufacturers to rapidly build and deploy their own AI/ML solutions or use those from our partners. Automotive manufacturers are already using AI/ML within Snowflake for demand forecasting, production optimization and predictive maintenance.
AW: Snowflake's AI/ML has been used for predictive maintenance in automotive production, but does this expansion extend that into connected vehicle data once the car is in use?
Long: Absolutely. Snowflake's capabilities now extend predictive maintenance beyond the factory and into vehicles on the road through streaming connected vehicle data.
Our platform is designed to handle the massive volume and velocity of data from connected vehicles — including sensor readings and operational metrics. By combining this with warranty claims and service records, manufacturers gain a holistic view of vehicle health throughout its entire lifecycle.
Connected vehicle data allows OEMs to predict potential failures before they occur, provide proactive service recommendations and optimize maintenance schedules to reduce downtime.
AW: Can you provide any examples of how auto manufacturers are sharing data between production, service and warranty systems using Snowflake?
Long: Automotive manufacturers like Toyota Motor Europe are using Snowflake to unify data across these critical functions. As an example of how this works, consider an OEM working to improve production quality while reducing warranty costs. With Snowflake, they can integrate data from previously disconnected systems onto a single platform. The manufacturer stores production data from manufacturing execution systems, quality control processes and factory sensors alongside service records from dealership networks and warranty claim information.
And this capability extends beyond car manufacturers — we've seen similar implementations with industrial equipment producers sharing data across dealership networks to enhance customer service and supply chain performance.
Snowflake's secure data sharing enables cross-functional analytics that reveal how production variations affect service issues, identify root causes of defects, streamline collaboration with suppliers to improve component quality and provide service centers with insights into potential warranty issues based on production data.
AW: Can you explain more about how Snowflake provides real-time visibility across global automotive supply chains?
Long: Snowflake integrates information from numerous first- and third-party sources including suppliers, logistics providers, inventory systems and transportation management platforms. Our platform supports continuous data ingestion, giving customers up-to-the-minute visibility into operations. And our secure data sharing capabilities allow OEMs and suppliers to exchange critical information on inventory levels, shipment status and potential disruptions.
Our partner ecosystem also plays a crucial role in delivering specialized analytics on top of this foundation. For example:
- Blue Yonder provides supply chain control towers offering real-time views of the entire supply chain for monitoring key metrics and identifying issues.
- LandingAI's LandingLens, a Snowflake native application, offers visual AI for automotive manufacturing to improve quality control, while also providing intelligent document extraction to process complex manufacturing documents like flow diagrams and inspection reports.
- Sigma helps manufacturers identify trends and predict anomalies by analyzing huge volumes of data natively within Snowflake, enabling use cases ranging from energy optimization to quality control automation.
- And partners like Cirrus Link, HighByte, and LTIMindtree facilitate the ingestion of IT/OT data into Snowflake with minimal network burden.
By combining our data platform with these partner solutions, automotive companies can spot disruptions early, assess their impact and take proactive steps — whether finding alternative suppliers or adjusting production schedules — before customer deliveries are affected.
AW: Do you have any examples of how Snowflake supports collaboration between automotive OEMs and their suppliers to improve delivery timelines and reduce costs?
Long: Consider a scenario where an OEM must coordinate critical component deliveries from multiple suppliers to meet production schedules. Using Snowflake, the OEM securely shares production forecasts, inventory levels and delivery timelines with their supplier network. Suppliers reciprocate by sharing production capacity, lead times and potential constraints.
With Snowflake as the central data platform, both OEMs and suppliers operate from a single source of truth to eliminate discrepancies, improving communication and ensuring alignment across the supply network.
This collaborative approach delivers multiple benefits: improved delivery timelines as suppliers better align with OEM production needs; reduced costs through minimized expedited shipping and optimized inventory levels; more accurate demand forecasting that helps suppliers plan production more effectively; and enhanced supply chain transparency that allows all parties to track order progress, identify bottlenecks and resolve issues quickly.
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