Artificial Intelligence (AI) in Manufacturing

Many people believe that artificial intelligence (AI) in manufacturing will play a significant role in the so-called "new industrial revolution." Robotics, digitalization, and artificial intelligence (AI) are driving the next generation of industrialization.

They can be used together to automate many time-consuming manual activities, combine information technology (IT) and operational technology (OT) systems, significantly increase efficiency, improve product quality, increase supply chain visibility, and optimize inventory management.

AI in Manufacturing Today:

 AI usage is more prevalent in some regions of the world than others, with the United States lagging behind. According to Capgemini's utilized research, Europe is in the lead. AI is being used by 51% of European manufacturers, compared to 30% in Japan and 28% in the United States.

Another study reveals how manufacturers now perceive AI. EY interviewed over 500 CEOs from prominent manufacturing companies around the world. AI was deemed critical to success by 86 percent of CEOs. However, just 30 have been able to scale AI and other new technologies to deliver business value.

"Manufacturing data is a fantastic fit for AI/machine learning," said Cem Dilmegani, founder of the AI analyst firm AI Multiple. "Manufacturing generates a lot of analytical data that machines can easily examine." Artificial Intelligence (AI)

 Artificial Intelligence (AI) in Manufacturing

5 Case Studies of Artificial Intelligence in Manufacturing

1. Streamlining production processes

AI is being utilized to streamline procedures and increase productivity in many manufacturing operations. Lindström, for example, collaborated with QPR to standardize and improve business processes as well as a process management strategy to assure future competitiveness and success.

"We chose QPR to help us execute our ambition of having the fastest and most reliable processes in the market," said Harri Puputti, Lindström Group's senior vice president of corporate quality.

2. Plant Productivity 

Mitsubishi Power's Tomoni digitalization platform includes controls, instrumentation, data analytics, AI, and other features. Its goal is to make plants smarter. A typical power plant, for example, contains roughly 10,000 sensors capable of producing over a million points of data each minute. Tomo ni takes this jumbled data and converts it into a useable format.

Tomomi is a collection of digital and AI technologies that can assist in the development of an increasingly smart facility capable of varying levels of autonomous operation. Increased digitalization of interconnected devices and systems enables control systems to perform more and interface with advanced analytics more effectively.

"Actionable insights enable plant employees to make better operations and maintenance decisions, increasing efficiency and flexibility," said Tom Logan, senior manager of technology integration at Mitsubishi Power Americas.

3. Preventative upkeep

Baker Hughes, a compressor maker, and oil and gas solutions provider, is using artificial intelligence to detect maintenance difficulties. The company collaborated with Microsoft Azure and C3.ai, an AI company, to develop an AI-based application that allows operators to examine real-time production data, better forecast future production, and optimize operations for increased output rates.

The application continuously uses machine-learning (ML) algorithms to rapidly combine historical and real-time data across production operations, resulting in the creation of a virtual representation of production across the value chain. It also detects abnormalities, forecasts output, and recommends steps to increase output. Engineers can use it to determine which injection wells to adjust for increased output.

According to Jay Crotts, CIO of Shell Group, Shell is leveraging the C3.ai platform on Microsoft Azure to drive digital transformation across its organization, improve efficiency, promote safety, and decrease environmental impact. AI is also being used by the corporation to implement predictive maintenance and to monitor half a million valves.

4. Product development

Sentry Equipment, the original equipment manufacturer, updated their Sentry Guard sampling machine to provide assistance to operators utilizing the Aveva System Platform to reduce development time. It can assess sample data, generate alerts, and help operators through the resolution process.

"Collecting data was not as simple as we had hoped. "AVEVA System Platform eliminated that complexity, allowing us to focus on the value-add of our SmartAlarm application rather than finding out how to collect and present data," stated Richard Alves, Sentry Equipment's development engineer.

5. There is no touch and no-fault:

The more people that work on an assembly line, the more potential for errors to occur. As a result, many manufacturers are introducing automation and robotics to eliminate errors. However, AI is required to ensure that even minor deviations from established The more people that work on an assembly line, the more potential for errors to occur. As a result, many manufacturers are introducing automation and robotics to eliminate errors. However, AI is required to ensure that even minor deviations from established practices workflows are noticed immediately. 

AI will drive and enable zero-touch operations and zero faults when integrated with other digital technologies and standard ways of working," said Sachin Lulla, global digital strategy and transformation head at EY.  Working Of Artificial Intelligence

 Artificial Intelligence (AI) in Manufacturing