What are the Difficulties of Adopting AI in Manufacturing?

What are the Difficulties of Adopting AI in Manufacturing?

Manufacturing Technology Insights | Friday, July 30, 2021

While challenges to AI adoption still exist in manufacturing companies, but it is also helping them to reduce costs, improve efficiency and solve new problems.

FREMONT, CA: Automation is not a new concept to the manufacturers. For more than 50 years, the manufacturing sector has been dedicated to developing better products, primarily through automating operations, cutting operating costs, and improving quality.

Manufacturers are leading the way in adopting Artificial Intelligence technology, using AI-powered analytics to increase productivity, product quality, and employee safety, just as they did with automation.

According to a recent survey on AI adoption in manufacturing, 93 percent of businesses believe AI will be a critical technology for driving growth and innovation. But, as the sector embraces AI, it faces new hurdles such as shorter time-to-market deadlines, increasingly complicated goods, and stringent quality laws and standards.

A large proportion of manufacturing firms must overcome numerous roadblocks to digital transformation and AI activities, including:

Shortage of AI talent

Experienced data scientists and AI workers are rare and difficult to find in organizations throughout all industries. Data scientists, machine learning engineers, software architects, BI analysts, and SMEs are necessary for AI projects. But several businesses lack these resources and cannot afford to deploy them to a single data science project. It's considerably more challenging to scale to deliver on time when companies have numerous data-science projects which they have to complete on time.

Technology infrastructure. 

Manufacturing facilities frequently include a diverse range of machines, tools, and production systems that employ various technologies, some of which could be operating on outdated software incompatible with the rest of the system. Plant engineers must select the optimum way to link their equipment and systems with sensors or convertors to install without standards and common frameworks.

Real-time decision-making. 

This is becoming more significant in manufacturing applications, like quality control, and following customer delivery deadlines. To discover a problem before it causes unscheduled downtime, a manufacturing defect, or a safety risk, choices must often be made quickly, within seconds, or even milliseconds. Such speedy decision-making necessitates streaming analytics and real-time prediction technologies that allow producers to react swiftly and avoid unfavorable outcomes.

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