Machine learning algorithms, both supervised and unsupervised, can view real-time data from multiple production shifts in seconds and uncover previously unknown processes, items, and workflow trends.
FREMONT, CA: Real-time monitoring advantages include troubleshooting production bottlenecks, tracking scrap rates, meeting customer delivery dates, and more. It is a wonderful source of contextually meaningful data for machine learning models to learn from. Machine learning algorithms, both supervised and unsupervised, can view real-time data from multiple production shifts in seconds and uncover previously unknown processes, items, and workflow trends. Some ways AI is improving manufacturing are mentioned below.
In the automotive and consumer products sectors, analyzing images in real-time to complete product quality inspections helps manufacturers comply with strict regulatory requirements. High-resolution cameras are becoming more affordable, and AI-based image recognition tools and technologies are improving. These two factors, as well as others, are driving real-time in-line inspection adoption. One automotive firm is a pioneer in implementing these technologies, having developed a deep learning-based image recognition system.
Improving demand forecast accuracy yields positive results in various sectors, with consumer-packaged-goods producers leading the pack. One global food-products company based in France is currently using a machine learning technology to increase demand forecast accuracy. Machine learning is being used to enhance planning teamwork through marketing, distribution, account management, supply chain, and finance, resulting in more precise forecasts. The business can satisfy demand from product promotions and reach its target service levels for channel or store-level inventories using machine learning. The method resulted in a 20 percent drop in forecast error, a 30 percent decrease in lost sales, a 30 percent decline in product obsolescence, and a 50 percent reduction in demand planners' workload.
The maintenance of machinery and production properties accounts for 29 percent of AI implementations in manufacturing. The most common use case of AI in manufacturing today, according to one research, is predicting when machines/equipment will malfunction and suggesting optimal maintenance times (condition-based maintenance). With its supplier's assistance, one leading vehicle manufacturer analyzes images from cameras installed on assembly robots to detect signs and indications of failing robotic components. The device detected 72 instances of component failure across 7,000 robots in a pilot test, catching the issue before it caused unplanned outages.
