Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level.
Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.
The ten ways machine learning is revolutionizing manufacturing in 2018 include the following:
Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT (26 pp., PDF, no opt-in) Hitachi
Artificial Intelligence and Robotics and Their Impact on the Workplace (120 pp., PDF, no opt-in) IBA Global Employment Institute
Artificial Intelligence: The Next Digital Frontier? (80 pp., PDF, no opt-in) McKinsey and Company
Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services
Connected Factory and Digital Manufacturing: A Competitive Advantage, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)
Demystifying AI, Machine Learning, and Deep Learning, DZone, AI Zone
Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers
Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses (76 pp., PDF, no opt-in) University of Cambridge
Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D
Get started with the Connected factory preconfigured solution, Microsoft Azure
Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.
Impact of the Fourth Industrial Revolution on Supply Chains (22 pp., PDF, no opt-in) World Economic Forum
Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017
Machine Learning & Artificial Intelligence Presentation (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group
Machine Learning Techniques in Manufacturing Applications & Caveats, (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell
Machine learning: the power and promise of computers that learn by example (128 pp., PDF, no opt-in) Royal Society UK
Predictive maintenance and the smart factory (8 pp., PDF, no opt-in) Deloitte
Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review. Ai Edam, 28(1), 83-97.
Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company
Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney
The Future of Manufacturing; Making things in a changing world (52 pp., PDF, no opt-in) Deloitte University Press
The transformative potential of AI in the manufacturing industry, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017
The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services
TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018
Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.
Turning AI into concrete value: the successful implementers’ toolkit (28 pp., PDF, no opt-in) Capgemini Consulting
Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23-45.
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