10 Ways AI Is Improving Manufacturing In 2020 Posted on : May 18 - 2020

• Machinery Maintenance and Quality are the leading AI transformation projects in manufacturing operations today, according to Capgemini.
• Caterpillar's Marine Division is saving $400K per ship per year after machine learning analyzed data on how often hulls should be cleaned for maximum efficiency.
• The BMW Group uses AI to evaluate component images in ongoing production lines to spot deviations from the standard in real-time.

Perceiving the pandemics' hard reset as a chance to grow stronger, more resilient, and resourceful dominates manufacturers' mindsets who continue to double down on analytics and AI-driven pilots.

Combining human experience, insight, and AI techniques, they're discovering new ways to differentiate themselves while driving down costs and protecting margins. And they're all up for the challenge of continuing to grow in tough economic times. They're not alone in accepting that challenge. Boston Consulting Group's recent study The Rise of the AI-Powered Company in the Postcrisis World found that in the four previous global economic downturns, 14% of companies were able to increase both sales growth and profit margins as the following graphic shows:

AI Is Core To Manufacturing's Real-Time Future 

Real-time monitoring provides many benefits, including troubleshooting production bottlenecks, tracking scrap rates, meeting customer delivery dates, and more. It's an excellent source of contextually relevant data that can be used for training machine learning models. Supervised and unsupervised machine learning algorithms can interpret multiple production shifts' real-time data in seconds and discover previously unknown processes, products, and workflow patterns.

The following are ten ways AI is enhancing manufacturing in 2020 based on Capgemini's recently published Scaling AI in Manufacturing Operations: A Practitioners Perspective study and interviews with manufacturers over the last four months:

• 29% of AI implementations in manufacturing are for maintaining machinery and production assets. Capgemini's research team found that predicting when machines/equipment are likely to fail and recommending optimal times to conduct maintenance (condition-based maintenance) is the most popular use case of AI in manufacturing today. General Motors analyzes images from cameras mounted on assembly robots, to spot signs and indications of failing robotic components with the help of its supplier. In a pilot test of the system, it detected 72 instances of component failure across 7,000 robots, identifying the problem before it could result in unplanned outages. The following graphic from the study illustrates how AI is used for intelligent maintenance in manufacturing:
• General Motors' Dreamcatcher system is based on Autodesk's generative design algorithm that relies on machine learning techniques to factor in design constraints and provides an optimized product design.  Having constraint-optimizing logic within a CAD design environment helps GM attain the goal of rapid prototyping. Designers provide a definitions of the functional requirements, materials, manufacturing methods and other constraints. GM and AutoDesk have customized Dreamcatcher to optimize for weight and other key product criterion essential for the parts being designed to succeed with additive manufacturing. The solution was recently tested with the prototyping of a seatbelt bracket part, which resulted in a single-piece design that is 40% lighter and 20% stronger than the original eight component design.. Please see the Harvard Business Review case analysis, Project Dreamcatcher: Can Generative Design Accelerate Additive Manufacturing? for additional information. View More