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Safety compliance: Computer vision monitors video feeds for PPE compliance, unsafe behaviors, and hazardous conditions.

Safety monitoring: Sensor monitoring of environmental conditions can predict heat stress, fatigue, and accidents before they occur.

Production scheduling optimization: Algorithmic processing of machine capacity, materials, costs, and deadlines to create optimal schedules. Machine learning enables the adaptation to disruptions and changes, thereby reducing scheduling time.

Automated quality control: Computer vision is being used to detect defects, dimensional variations, and assembly errors in real-time.

Demand forecasting: Models combine sales data with weather, economic indicators, and social sentiment to predict customer demand across products.

Assembly line automation: Using computer vision and robotics to perform assembly tasks on moving production lines.

Predictive materials procurement: Algorithms analyze usage patterns, supplier data, and market conditions to predict material needs. Systems optimize ordering timing and quantities, reducing stockouts while minimizing excess inventory costs.

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