It’s been estimated that AI can improve the manufacturing sector’s productivity by 45% by 2035. However, industrial companies have been slow to capitalize on this opportunity and still preside with a resource-first approach, which relies on operator expertise, manual/visual quality inspections, and teams of data scientists and lengthy consulting projects to reactively analyze and optimize an operational process.
Where a plant generates hundreds of thousands of data points each minute, less than 10% of data is used to aid decisions. This has resulted in industrial environments being data-rich but information-poor, eliminating the opportunity to make timely decisions that positively impact yield, quality and energy usage. For manufacturers, today’s normal will sink the ship. By 2030, it is estimated that the world will need 35% more food, 50% more energy, 41% more productivity — all while dramatically needing to reduce the world’s greenhouse gas emissions. The industrial sector is front and center of how food, energy and greenhouse gas emissions are produced. It is estimated that the industrial sector is the third-largest source of man-made C02 emissions, producing 20% of fossil fuel-related C02 emissions in 2010. The bottom line: manufacturers must find new and innovative was to improve how they operate and increase productivity.
This presentation will outline why the time is now for industrial manufacturers to implement AI in order to future-proof their business. Real-world case studies will be covered, outlining how major North American steel manufacturers, wind facilities, food and agriculture plants, and auto part manufacturers have implemented AI across their businesses to increase yield, improve quality, reduce waste and optimize energy usage. Learn how AI can empower manufacturers to control processes, optimize productivity, reduce risk and automate the entire manufacturing line.