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BMW Sets Out 7 Principles For Use of Artificial Intelligence – Metrology and Quality News - Online Magazine

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The use of artificial intelligence (AI) is a central element of the digital transformation process at the BMW Group. The BMW Group already uses AI throughout the value chain to generate added value for customers, products, employees and processes. Michael Würtenberger, Head of'Project AI: "Artificial intelligence is the key technology in the process of digital transformation. But for us the focus remains on people. AI supports our employees and improves the customer experience. We are proceeding purposefully and with caution in the expansion of AI applications within the company. The seven principles for AI at the BMW Group provide the basis for our approach."


How BMW Powers its Processes with Artificial Intelligence

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In this decade, companies across the globe have embraced the potential of artificial intelligence for digital transformation and enhanced customer experience. One important application of AI is enabling companies to use the pools of data available with them for smart business use. BMW is one of the world's leading manufacturers of premium automobiles and mobility services. BMW uses artificial intelligence in critical areas like production, research and development, and customer service. BMW also runs a project dedicated to this technology called Project AI, for efficient use of artificial intelligence.


Augmenting intelligence: How BMW's US IT center is putting AI into the hands of workers

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Outside Germany, one of BMW's key research organizations for business and manufacturing technology is the IT Innovation and Research Center. With bases both in Silicon Valley, as well as Greenville, South Carolina, the IT center carries out research for systems and tools across the enterprise, including financial services, sales and marketing, engineering, quality, HR, production and logistics. It is part of the carmaker's central BMW Group IT department led from Munich, which coordinates the company's enterprise and manufacturing IT backbone. Similar to other laboratory locations across BMW, the IT center operates to a large extent in research mode. It has a strong connection, for example, to Clemson University, with whom it shares a campus at the International Center for Automotive Research, working closely with engineering and software professors and students.


Artificial Intelligence and Data Science in the Automotive Industry – Data Science Blog

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Each of these areas already features a significant level of complexity, so the following description of data mining and artificial intelligence applications has necessarily been restricted to an overview. Vehicle development has become a largely virtual process that is now the accepted state of the art for all manufacturers. CAD models and simulations (typically of physical processes, such as mechanics, flow, acoustics, vibration, etc., on the basis of finite element models) are used extensively in all stages of the development process. The subject of optimization (often with the use of evolution strategies[31] or genetic algorithms and related methods) is usually less well covered, even though it is precisely here in the development process that it can frequently yield impressive results. Multi-disciplinary optimization, in which multiple development disciplines (such as occupant safety and noise, vibration, and harshness (NVH)) are combined and optimized simultaneously, is still rarely used in many cases due to supposedly excessive computation time requirements.


Artificial Intelligence in Manufacturing: The Evolution of Industry - CiOL

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Artificial Intelligence is benefiting to various industries including healthcare, education and manufacturing. But what is Artificial intelligence (AI)? In Layman language, a simulator of human intelligence, which makes the decision after analyzing various data utilizing a collection of different intelligent technologies including machine and deep learning, analytics and computer vision. The fourth industrial revolution is employing AI to enhance its overall efficiency. The technology is not only helping to reduce manufacturing cost as well as it is improving productivity and quality. Manufacturing is a capital-intensive process, and once a plant is a set-up, replacing, removing or renovating is exorbitantly expensive. New machines improve performance; reduce redundancies, while improving overall quality metrics. AI is proving an alternative route to achieve all this and at extremely competitive price points. Instead of now replacing machines, manufacturers are adding AI/ML tools to pre-inspect raw materials identify defects, perform quality evaluations, and a lot more.