Generative AI is still a relatively new technology, but it is already being used in a variety of ways to scale up the manufacturing industry. Here are a few examples:

Product Design
(Source)

Siemens is using Generative AI to design new wind turbine blades that are lighter and more efficient. The company says that this has helped them to reduce the weight of the blades by up to 20%, which has resulted in a significant improvement in fuel efficiency.

Product Enhancement
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Source)

Boeing is using Generative AI to design new aircraft parts that are stronger and lighter. The company says that this has helped them to reduce the weight of the aircraft by up to 5%, which has resulted in a reduction in fuel consumption and emissions.

Now, let us drill down to some recent statistics on how AI is impacting the manufacturing industry:

  • A study by Accenture estimated that AI could increase labor productivity by up to 40% by 2035.
  • According to McKinsey, AI can reduce quality control errors by up to 90%.
  • IBM reports that AI can improve supply chain visibility by 25% or more.
  • General Electric (GE) implemented AI-driven energy efficiency measures and saved $20 million in just one year.
  • A study by the National Institute for Occupational Safety and Health (NIOSH) found a 85% reduction in repetitive strain injuries with AI-powered exoskeletons.
  • The global AI in manufacturing market size is expected to grow from $1.1 billion in 2020 to $16.7 billion by 2026, at a CAGR of 57.2%.
A Report by McKinsey

# Use Cases

AI Implementations to Solve Real-World Manufacturing Industry Problems

Previously, we have given example on how big manufacturing enterprises are leveraging AI technologies. As an AI solution provider, we have gained hands-on experience on implementing AI technologies for our manufacturing industry customers. 

In this segment, we are going to showcase some of the primary use cases of AI adoption in the manufacturing industry.