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Manufacturing Technology Insights | Friday, February 14, 2020
AI and machine learning enable firms to achieve unprecedented productivity, supply chain optimization, and research and development acceleration. Since the internet's inception, the world's largest manufacturers have digitized their operations.
FREMONT, CA: Artificial Intelligence (AI) is a collective and occasionally somewhat omniscient word that refers to the capabilities of learning systems that humans see as intelligent. AI and machine learning (ML) technologies have risen to prominence in manufacturing as they enable businesses to transform their business models, create new operational paradigms to support those models, and monetize data to achieve higher levels of efficiency.
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Apart from hypes and fads, AI works because it provides significant benefits to the manufacturing sector, including smart production, predictive and preventative maintenance, supply chain optimization, improved safety, product development, and optimization, enabling AR/VR (Augmented and Virtual Reality), cost reduction, and quality assurance, and enabling green operations (energy management), to name a few. The most prevalent application of AI in manufacturing boosts overall equipment efficiency and yield. AI is being used broadly to increase productivity, quality, and consistency, enabling firms to forecast more precisely.
It's reasonable to assume that AI and machine learning technologies continue to dominate the industrial business. UST has recognized a critical role for AI in transforming operations, enhancing product quality, and lowering costs through various methodologies, including innovative operation, design prediction, and product quality assessment.
Several essential benefits to adopting AI make it an excellent starting point for manufacturers embarking on their cognitive computing journey: intelligent maintenance, intelligent demand planning, forecasting, and product quality management.
While deploying AI is a complex process, as with many other aspects of digitization, this has not stopped businesses from progressing. The ability to scale and sustain an AI endeavor over time while generating company value is critical for attaining early success milestones on an AI adoption path.
Manufacturing firms are rapidly adopting AI and machine learning technologies because they enable organizations to optimize their analytical skills, create more accurate projections, and reduce inventory costs. Improved analytics capabilities help organizations transition to predictive maintenance, which lowers maintenance costs and downtime.
Manufacturers can utilize AI to forecast when or whether functional equipment will fail, allowing for proactive maintenance and repair schedules. This is critical because machines can run more efficiently and cost-effectively when AI-powered predictive maintenance is employed. AI's ability to forecast equipment failures and optimize scheduling before equipment failure makes it ideal for maintaining reliable equipment and ensuring smooth production.
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