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Bottom line:<\/strong> Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimise manufacturing operations to the shop floor level.<\/p>\n 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.<\/p>\n The ten ways machine learning is revolutionising manufacturing in 2018 include the following:<\/p>\n Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimisation are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector?<\/a> (52 pp., PDF, no opt-in) McKinsey & Company.<\/p>\n The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimisation. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation<\/a> (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.<\/p>\n Analytics and MI-driven process and quality optimisation are predicted to grow 35% and process visualisation and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing<\/a> (48 pp., PDF, no opt-in) PriceWaterhouseCoopers<\/p>\n Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector?<\/a> (52 pp., PDF, no opt-in) McKinsey & Company.<\/p>\n Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management. Source: Honeywell Connected Plant: Analytics and Beyond<\/a>. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.<\/p>\n AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimisation across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance. Source: Transform the manufacturing supply chain with Multi-Echelon inventory optimisation<\/a>, Microsoft, March 1, 2018.<\/p>\n Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimising the best possible set of machines for a given production run is now possible using machine learning algorithms. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile,<\/a> (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<\/p>\n Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity, and related variables gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios. Source: Leveraging AI for Industrial IoT<\/a> (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017<\/p>\n The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process. Source: The Value Of Data Science Standards In Manufacturing Analytics<\/a> (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services<\/p>\n OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today. Source: TIBCO Manufacturing Solutions<\/a>, TIBCO Community, January 30, 2018<\/p>\n Additional reading:<\/strong><\/em><\/p>\n Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT<\/a> (26 pp., PDF, no opt-in) Hitachi<\/em><\/p>\n Artificial Intelligence and Robotics and Their Impact on the Workplace<\/a> (120 pp., PDF, no opt-in) IBA Global Employment Institute<\/em><\/p>\n Artificial Intelligence: The Next Digital Frontier?<\/a> (80 pp., PDF, no opt-in) McKinsey and Company<\/em><\/p>\n Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing<\/a> (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services<\/em><\/p>\n Connected Factory and Digital Manufacturing: A Competitive Advantage<\/a>, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)<\/em><\/p>\n Demystifying AI, Machine Learning, and Deep Learning<\/a>, DZone, AI Zone<\/em><\/p>\n Digital Factories 2020: Shaping the future of manufacturing<\/a> (48 pp., PDF, no opt-in) PriceWaterhouseCoopers<\/em><\/p>\n Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses<\/a> (76 pp., PDF, no opt-in) University of Cambridge<\/em><\/p>\n Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile,<\/a> (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<\/em><\/p>\n Get started with the Connected factory preconfigured solution<\/a>, Microsoft Azure<\/em><\/p>\n Honeywell Connected Plant: Analytics and Beyond<\/a>. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.<\/em><\/p>\n Impact of the Fourth Industrial Revolution on Supply Chains<\/a> (22 pp., PDF, no opt-in) World Economic Forum<\/em><\/p>\n Leveraging AI for Industrial IoT<\/a> (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017<\/em><\/p>\n Machine Learning & Artificial Intelligence Presentation<\/a> (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group<\/em><\/p>\n Machine Learning Techniques in Manufacturing Applications & Caveats,<\/a> (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell<\/em><\/p>\n Machine learning: the power and promise of computers that learn by example<\/a> (128 pp., PDF, no opt-in) Royal Society UK<\/em><\/p>\n Predictive maintenance and the smart factory<\/a> (8 pp., PDF, no opt-in) Deloitte<\/em><\/p>\n Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review<\/a>. Ai Edam, 28(1), 83-97.<\/em><\/p>\n Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector?<\/a> (52 pp., PDF, no opt-in) McKinsey & Company<\/em><\/p>\n Technology and Innovation for the Future of Production: Accelerating Value Creation<\/a> (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney<\/em><\/p>\n The Future of Manufacturing; Making things in a changing world<\/a> (52 pp., PDF, no opt-in) Deloitte University Press<\/em><\/p>\n The transformative potential of AI in the manufacturing industry<\/a>, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017<\/em><\/p>\n The Value Of Data Science Standards In Manufacturing Analytics<\/a> (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services<\/em><\/p>\n TIBCO Manufacturing Solutions<\/a>, TIBCO Community, January 30, 2018<\/em><\/p>\n Transform the manufacturing supply chain with Multi-Echelon inventory optimisation<\/a>, Microsoft, March 1, 2018.<\/em><\/p>\nImproving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimising fab operations are is achievable with machine learning<\/strong><\/h3>\n
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<\/a><\/p>\nAsset management, supply chain management, and inventory management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today<\/strong><\/h3>\n
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<\/a><\/p>\nManufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC<\/strong><\/h3>\n
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<\/a><\/p>\nMcKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability<\/strong><\/h3>\n
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<\/a><\/p>\nImproving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well<\/strong><\/h3>\n
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<\/a><\/p>\nAutomating inventory optimisation using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%<\/strong><\/h3>\n
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<\/a><\/p>\nCombining real-time monitoring and machine learning is optimising shop floor operations, providing insights into machine-level loads and production schedule performance<\/strong><\/h3>\n
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<\/a><\/p>\nImproving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more<\/strong><\/h3>\n
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<\/a><\/p>\nA manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning<\/strong><\/h3>\n
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<\/a><\/p>\nImproving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE)<\/strong><\/h3>\n
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