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Generative AI
For Manufacturing & Automotive

Accelerate Smart Manufacturing with SambaNova Suite

AI Drives Industry 4.0

From connectivity and analytics to human-machine interactions and advanced engineering, in the pre-COVID-19 world, Industry 4.0 was making great strides in accelerating the digital transformation efforts of manufacturing organizations. To offset the disruption and worker shortage brought on by the pandemic, manufacturers across all industries are reprioritizing automation from “someday” to “right away.”

AI-driven accelerations and efficiencies are key to reducing costs and time to market while opening a new world of business opportunities. As manufacturers come to increasingly rely on AI to enhance product quality, enable data-driven decisions, and optimize production processes, the market growth of AI in the industry is expected to reach $11 billion by 2025.1

An Easier Way to Build Competitive New Business Models

SambaNova Suite augments the production enterprise’s AI expertise by providing a comprehensive platform, services, and models that enable manufacturing and supply chain owners to develop and deploy state-of-the-art AI solutions without the time and expense of building complex AI architectures and hiring teams of machine learning experts.

Use Cases Use Cases
Every machine repair forces a costly halt in the production line. According to the Robotic Industries Association, the cost of production-line downtime for automotive manufacturers, such as GM, can be as high as $20,000 per minute.2 Predictive maintenance helps reduce costly downtime and repair costs by collecting data from disparate sources, e.g., sensors, and applying machine learning models that enable manufacturers to accurately predict equipment failures before they occur. Predictive models also help identify the root cause of malfunction and enable a proactive response that can reduce or eliminate downtime.
High-resolution computer vision replaces human-eye inspections to increase accuracy and efficiency while reducing costs across defect detection (e.g., dust particles in paint), quality inspection, and assembly monitoring. Using AI-powered pattern recognition capabilities, computer vision systems improve quality and reduce the cost of wasted material by identifying missing parts, scratches, dents, and other flaws. Growing in importance in the auto insurance industry, computer vision is used by insurers to automate damage assessments and claims processing—reducing operational inefficiencies and costs.
  • Use Cases

  • Manufacturing—Predictive-Maintenance
    Every machine repair forces a costly halt in the production line. According to the Robotic Industries Association, the cost of production-line downtime for automotive manufacturers, such as GM, can be as high as $20,000 per minute.2 Predictive maintenance helps reduce costly downtime and repair costs by collecting data from disparate sources, e.g., sensors, and applying machine learning models that enable manufacturers to accurately predict equipment failures before they occur. Predictive models also help identify the root cause of malfunction and enable a proactive response that can reduce or eliminate downtime.
  • Manufacturing—Visual-Inspection
    Every machine repair forces a costly halt in the production line. According to the Robotic Industries Association, the cost of production-line downtime for automotive manufacturers, such as GM, can be as high as $20,000 per minute.2 Predictive maintenance helps reduce costly downtime and repair costs by collecting data from disparate sources, e.g., sensors, and applying machine learning models that enable manufacturers to accurately predict equipment failures before they occur. Predictive models also help identify the root cause of malfunction and enable a proactive response that can reduce or eliminate downtime.

Learn how SambaNova can help your organization leverage AI to improve E2E productivity and efficiency.