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Scaling AI in Manufacturing Operations

If the first industrial revolution was set in motion by steam, then Industry 4.0 is being powered by artificial intelligence. And with its ability to automate, digitize, and optimize, AI is the perfect fit for manufacturing operations, from product development to quality control. A computer vision system, for example, allowed GM to detect 72 instances of component failure, preventing massive downtime (a single minute of which can cost a company of that size up to $20,000) while a machine learning system significantly improved Danone’s demand forecast accuracy (reducing forecast error by 20%, lost sales by 30%, product obsolescence by 30%, and demand planner’s workload by 50%).

Enormous potential across the board

The latest report from the Capgemini Research Institute – Scaling AI in manufacturing operations – shows that intelligent maintenance, together with product quality inspection and demand planning constitute a good starting point for manufacturers to focus their efforts in manufacturing operations. That’s because:

  • They offer clear business value/benefits
  • They are relatively easy to implement
  • There is a ready availability of data and know-how
  • There is a possibility to add features that aid visibility and explainability, for ease of adoption by operational teams.

Focus and scale are critical

To tap into the manifold benefits AI can bring to manufacturing operations, organizations need to move beyond the pilot/proof-of-concept stage and deploy at scale. To these ends, we recommend deploying successful AI prototypes in live engineering environments, investing in laying down a foundation of data & AI systems and talent, and scaling the AI solution across the manufacturing network. AI is the rocket fuel behind Industry 4.0, and its natural fit with manufacturing means that organizations that are able to execute these capabilities are the ones that will really take off into the future.

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