FREMONT, CA: Today AI-embrace is nearing to completion in manufacturing industry. From significant cuts in unplanned downtime to better-designed products, manufacturers are employing AI-powered analytics to augment efficiency, product quality and employee safety.

Having a significant impact on the essence of any asset-reliant production operation, ongoing maintenance of production line machinery and equipment represents a considerable expense, in manufacturing. Unplanned downtime costs and asset failures make predictive maintenance a must-have solution for manufacturers. Predictive maintenance utilizes advanced AI algorithms-machine learning and artificial neural networks to formulate predictions regarding asset malfunction. This allows reductions in costly unplanned downtime. Technicians are briefed about components that need inspection and required tools and methods, which may result in accurate repair work in cases where maintenance is unavoidable.

Because of today’s short-time-to market deadlines and rise in complexity of product manufacturing companies have tough time in maintaining quality and complying with quality regulations. Along with these customers need for flawless products is pushing manufacturers to up their quality game. Quality involves the use of AI algorithms to notify manufacturing teams of production faults including variation from standards and abnormal machine behavior that are likely to cause product quality issues. By attending to these issues early on, a high level of quality can be maintained.

Employees will be trained for more advanced positions in design, maintenance, and programming as more and more jobs are taken over by robots. As more industrial robots invade the production floor alongside human laborers, human-robot collaboration has to be efficient and safe. Advances in AI will be the nucleus of this development, supporting robots to manage more cognitive tasks and make decisions based on real-time data, further optimizing processes.

Furthermore, AI algorithms can also be used to optimize manufacturing supply chains, helping companies anticipate market changes. AI algorithms underpin market demand estimation and highlight patterns connecting location, socioeconomic factors, weather patterns, political status, consumer behavior and more.

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