{"id":1932,"date":"2019-09-05T05:06:01","date_gmt":"2019-09-05T05:06:01","guid":{"rendered":"http:\/\/kusuaks7\/?p=1537"},"modified":"2024-04-18T17:30:31","modified_gmt":"2024-04-18T17:30:31","slug":"ten-ways-machine-learning-is-revolutionizing-manufacturing-in-2019","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/ten-ways-machine-learning-is-revolutionizing-manufacturing-in-2019\/","title":{"rendered":"Ten Ways Machine Learning Is Revolutionizing Manufacturing In 2019"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1932\" class=\"elementor elementor-1932\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-6dbacb53 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6dbacb53\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-71e19f5a\" data-id=\"71e19f5a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-72781075 elementor-widget elementor-widget-text-editor\" data-id=\"72781075\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li>AI has the potential to create $1.4T to $2.6T of value in marketing and sales across the world\u2019s businesses, and\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/most-of-ais-business-uses-will-be-in-two-areas\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/most-of-ais-business-uses-will-be-in-two-areas\" class=\"broken_link\">$1.2T to $2T in supply-chain management and manufacturing<\/a>.<\/li>\n \t<li>By 2021, 20% of leading manufacturers will rely on embedded intelligence, using AI, IoT, and blockchain applications to automate processes and increase execution times by up to 25% according to<a href=\"https:\/\/www.cnbc.com\/advertorial\/2018\/04\/27\/how-manufacturing-can-harness-digital-innovation-and-reap-the-benefits-of-growth.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.cnbc.com\/advertorial\/2018\/04\/27\/how-manufacturing-can-harness-digital-innovation-and-reap-the-benefits-of-growth.html\">\u00a0IDC<\/a>.<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-22569ec elementor-widget elementor-widget-text-editor\" data-id=\"22569ec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<li>Machine learning improves product quality up to 35% in discrete manufacturing industries, according to\u00a0<a href=\"https:\/\/www2.deloitte.com\/content\/dam\/Deloitte\/us\/Documents\/about-deloitte\/us-a-turnkey-iot-solution-for-manufacturing.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www2.deloitte.com\/content\/dam\/Deloitte\/us\/Documents\/about-deloitte\/us-a-turnkey-iot-solution-for-manufacturing.pdf\" class=\"broken_link\">Deloitte<\/a>.<\/li>\n \t<li>50% of companies that embrace AI over the next five to seven years have the potential to double their cash flow with manufacturing leading all industries due to its heavy reliance on data according to\u00a0<a href=\"http:\/\/c:%5CUsers%5Clcolumbus%5CDownloads%5Cdigital-manufacturing-capturing-sustainable-impact-at-scale.pdf\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:http:\/\/C:%5CUsers%5Clcolumbus%5CDownloads%5Cdigital-manufacturing-capturing-sustainable-impact-at-scale.pdf\">McKinsey<\/a>.<\/li>\n \t<li>By 2020, 60% of leading manufacturers will depend on digital platforms to support as much as\u00a0<a href=\"https:\/\/www.cnbc.com\/advertorial\/2018\/04\/27\/how-manufacturing-can-harness-digital-innovation-and-reap-the-benefits-of-growth.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.cnbc.com\/advertorial\/2018\/04\/27\/how-manufacturing-can-harness-digital-innovation-and-reap-the-benefits-of-growth.html\">30% of their overall revenue<\/a>.<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ef9b69a elementor-widget elementor-widget-text-editor\" data-id=\"ef9b69a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t \t<li>48% of Japanese manufacturers are seeing greater opportunities to integrate machine learning and digital manufacturing techniques into their operations than initially believed\u00a0<a href=\"https:\/\/www.mckinsey.com\/~\/media\/mckinsey\/business%20functions\/operations\/our%20insights\/how%20digital%20manufacturing%20can%20escape%20pilot%20purgatory\/digital-manufacturing-escaping-pilot-purgatory.ashx\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/~\/media\/mckinsey\/business%20functions\/operations\/our%20insights\/how%20digital%20manufacturing%20can%20escape%20pilot%20purgatory\/digital-manufacturing-escaping-pilot-purgatory.ashx\" class=\"broken_link\">according to McKinsey\u2019s landmark study, Digital Manufacturing \u2013 escaping pilot purgatory.<\/a><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-cb092b0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cb092b0\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-13fa256\" data-id=\"13fa256\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0c30cb7 elementor-widget elementor-widget-text-editor\" data-id=\"0c30cb7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<strong>Bottom Line:\u00a0<\/strong>The leading growth strategy for manufacturers in 2019 is improving shop floor productivity by investing in machine learning platforms that deliver the insights needed to improve product quality and production yields.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e42e94f elementor-widget elementor-widget-text-editor\" data-id=\"e42e94f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tUsing machine learning to streamline every phase of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is now a priority in manufacturing. According to a\u00a0<a href=\"https:\/\/www2.deloitte.com\/content\/dam\/Deloitte\/us\/Documents\/about-deloitte\/us-a-turnkey-iot-solution-for-manufacturing.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www2.deloitte.com\/content\/dam\/Deloitte\/us\/Documents\/about-deloitte\/us-a-turnkey-iot-solution-for-manufacturing.pdf\" class=\"broken_link\">recent survey by Deloitte<\/a>, machine learning is reducing unplanned machinery downtime between 15 \u2013 30%, increasing production throughput by 20%, reducing maintenance costs 30% and delivering up to a 35% increase in quality.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df106c6 elementor-widget elementor-widget-text-editor\" data-id=\"df106c6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe following are ten ways machines learning is revolutionizing manufacturing in 2019:\n<ul>\n \t<li><strong>AI has the potential to create $1.4T to $2.6T of value in marketing and sales across the world\u2019s businesses, and $1.2T to $2 in supply-chain management and manufacturing.<\/strong>\u00a0McKinsey predicts AI-based predictive maintenance has the potential to deliver between $.5T to $.7T value to manufacturers. McKinsey cites AI\u2019s ability to process massive amounts of data, including audio and video, means it can quickly identify anomalies to prevent breakdowns. Machine learning can determine whether a specific sound is an aircraft engine operating correctly under quality tests or a machine on an assembly line about to fail. Source: McKinsey\/Harvard Business Review.\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/most-of-ais-business-uses-will-be-in-two-areas\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/most-of-ais-business-uses-will-be-in-two-areas\" class=\"broken_link\">Most of AI\u2019s business uses will be in two areas<\/a>\u00a0by Michael Chui, Nicolaus Henke, and Mehdi Miremadi. March 2019<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-567e94d elementor-widget elementor-widget-image\" data-id=\"567e94d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/blogs-images.forbes.com\/louiscolumbus\/files\/2019\/08\/McKinsey-Market-Sizing.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0ce846c elementor-widget elementor-widget-text-editor\" data-id=\"0ce846c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: MCKINSEY\/HARVARD BUSINESS REVIEW. MOST OF AI\u2019S BUSINESS USES WILL BE IN TWO AREAS BY MICHAEL CHUI, NICOLAUS HENKE, AND MEHDI MIREMADI. MARCH 2019<\/small><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-403213a elementor-widget elementor-widget-text-editor\" data-id=\"403213a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>Manufacturers are gaining new insights into how they can become more sustainable using machine learning and predictive analytics that scale on cloud platforms.<\/strong>\u00a0Process manufacturers are using Azure\u2019s Symphony Industrial AI to deploy equipment models from a template library that includes heat exchangers, pumps, compressors, and other assets process manufacturers rely on. Symphony AI\u2019s Process 360 AI helps users create predictive models of their processes. A process is defined at the high level as the items (such as chemicals, fuels, metals, other intermediate and finished products) in production through the equipment. Process template examples include an ammonia process, an ethylene process, an LNG process, and a polypropylene process. Process models help predict process upsets and trips \u2014 which equipment models alone may not be able to predict. Source: Microsoft Azure blog,\u00a0<a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/implement-predictive-analytics-for-manufacturing-with-symphony-industrial-ai\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/azure.microsoft.com\/en-us\/blog\/implement-predictive-analytics-for-manufacturing-with-symphony-industrial-ai\/\">Implement predictive analytics for manufacturing with Symphony Industrial AI<\/a>,<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-172e6eb elementor-widget elementor-widget-image\" data-id=\"172e6eb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/thumbor.forbes.com\/thumbor\/960x0\/https%3A%2F%2Fblogs-images.forbes.com%2Flouiscolumbus%2Ffiles%2F2019%2F08%2Fazure-dashboard.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9970202 elementor-widget elementor-widget-text-editor\" data-id=\"9970202\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: MICROSOFT AZURE BLOG, IMPLEMENT PREDICTIVE ANALYTICS FOR MANUFACTURING WITH SYMPHONY INDUSTRIAL AI,<\/small><\/span><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-754993a elementor-widget elementor-widget-text-editor\" data-id=\"754993a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<ul>\n \t<li><strong>Boston Consulting Group (BCG) found that manufacturers\u2019 use of AI can reduce producer\u2019s conversion costs by up to 20% with up to 70% of the cost reduction resulting from higher workforce productivity.<\/strong>\u00a0BCG found that producers will be able to generate additional sales by using AI to develop and produce innovative products tailored to specific customers and to deliver them in a much shorter lead-time. The following graphic illustrates how AI will bring increased flexibility and scale to production processes based on BCG\u2019s analysis. Source:\u00a0<a href=\"https:\/\/www.bcg.com\/publications\/2018\/artificial-intelligence-factory-future.aspx\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.bcg.com\/publications\/2018\/artificial-intelligence-factory-future.aspx\" class=\"broken_link\">Boston Consulting Group, AI in the Factory of the Future, April 18, 2018<\/a>.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53f26c4 elementor-widget elementor-widget-image\" data-id=\"53f26c4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/thumbor.forbes.com\/thumbor\/960x0\/https%3A%2F%2Fblogs-images.forbes.com%2Flouiscolumbus%2Ffiles%2F2019%2F08%2FBCG-Graphic.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5bb61c elementor-widget elementor-widget-text-editor\" data-id=\"a5bb61c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: BOSTON CONSULTING GROUP, AI IN THE FACTORY OF THE FUTURE, APRIL 18, 2018.<\/small><\/span><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dc2ac8a elementor-widget elementor-widget-text-editor\" data-id=\"dc2ac8a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>Discrete and process manufacturers who rely on heavy assets are using AI and machine learning to improve throughput, energy consumption, and profit per hour.<\/strong>\u00a0Manufacturers with heavy equipment, including large-scale machinery, are exploring the use of algorithms to improve throughput, sustainability, and yield rates. McKinsey is finding AI can automate complex tasks and provide consistency and precise optimum set points to enable machinery to run in auto-pilot mode, which is essential for achieving lights-out manufacturing on one or more production shifts. Source: McKinsey,\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/ai-in-production-a-game-changer-for-manufacturers-with-heavy-assets\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/ai-in-production-a-game-changer-for-manufacturers-with-heavy-assets\" class=\"broken_link\">AI in production: A game-changer for manufacturers with heavy assets<\/a>, by Eleftherios Charalambous, Robert Feldmann, G\u00e9rard Richter, and Christoph Schmitz<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61e71e2 elementor-widget elementor-widget-image\" data-id=\"61e71e2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/blogs-images.forbes.com\/louiscolumbus\/files\/2019\/08\/AI-Asset-Optimizer.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aecf11a elementor-widget elementor-widget-text-editor\" data-id=\"aecf11a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: MCKINSEY, AI IN PRODUCTION: A GAME CHANGER FOR MANUFACTURERS WITH HEAVY ASSETS, BY ELEFTHERIOS CHARALAMBOUS, ROBERT FELDMANN, G\u00c9RARD RICHTER, AND CHRISTOPH SCHMITZ<\/small><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a839b4 elementor-widget elementor-widget-text-editor\" data-id=\"4a839b4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>AI- and machine learning-based product defect detection and quality assurance show the potential to increase manufacturing productivity by 50% or more.<\/strong>\u00a0Machine learning\u2019s inherent advantages in finding anomalies in a product and its packaging have significant potential to improve product quality and stop defective products from leaving a production facility.\u00a0 Improvements of up to 90% in defect detection as compared to human inspection are feasible using deep-learning-based systems. Given the availability of open-source AI environments and inexpensive hardware in terms of cameras and powerful computers, even small businesses are expected to increasingly rely on AI-based visual inspection. \u00a0In AI-enabled visual quality inspection, reference examples are created by visual imaging of good and defective products from different perspectives that fuel the training of supervised learning algorithms.\u00a0Source:\u00a0<a href=\"https:\/\/www.mckinsey.de\/files\/170419_mckinsey_ki_final_m.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.de\/files\/170419_mckinsey_ki_final_m.pdf\" class=\"broken_link\">Smartening up with Artificial Intelligence (AI) &#8211; What\u2019s in it for Germany and its Industrial Sector?<\/a>\u00a0(52 pp., PDF, no opt-in) McKinsey &amp; Company.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ea36e7e elementor-widget elementor-widget-image\" data-id=\"ea36e7e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/blogs-images.forbes.com\/louiscolumbus\/files\/2019\/08\/AI-based-inspections-2.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9b764e3 elementor-widget elementor-widget-text-editor\" data-id=\"9b764e3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: SMARTENING UP WITH ARTIFICIAL INTELLIGENCE (AI) &#8211; WHAT\u2019S IN IT FOR GERMANY AND ITS INDUSTRIAL SECTOR? (52 PP., PDF, NO OPT-IN) MCKINSEY &amp; COMPANY.<\/small><\/span><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9179f1b elementor-widget elementor-widget-text-editor\" data-id=\"9179f1b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>Machine learning has the potential to reduce manufacturing\u2019s chronic labor shortage while finding new ways to retain employees at the same time.<\/strong>\u00a0Manufacturing is facing a severe labor shortage today, with every survey of manufacturers reflecting this issue as one of the top three most constraining the industry\u2019s growth. One of the most interesting companies taking on this challenge is\u00a0<a href=\"https:\/\/eightfold.ai\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/eightfold.ai\/\">Eightfold<\/a>. Their AI-based Talent Intelligence Platform relies on a series of supervised and unsupervised machine learning algorithms to match a candidate\u2019s unique set of capabilities, experience, and strengths. Manufacturers, including\u00a0ConAgra, are relying on\u00a0<a href=\"https:\/\/eightfold.ai\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/eightfold.ai\/\">Eightfold<\/a>\u00a0to improve recruiting and rediscover talent they need to staff their teams and pursue growth opportunities. The following diagram explains how the Eightfold Talent Intelligence Platform works:<\/li>\n<\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-69ee68c elementor-widget elementor-widget-image\" data-id=\"69ee68c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/blogs-images.forbes.com\/louiscolumbus\/files\/2019\/08\/eightfold.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fcb73c9 elementor-widget elementor-widget-text-editor\" data-id=\"fcb73c9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>HTTPS:\/\/EIGHTFOLD.AI\/<\/small><\/span><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3068b30 elementor-widget elementor-widget-text-editor\" data-id=\"3068b30\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>Machine learning is helping manufacturers solve previously impenetrable problems and reveal those that they never knew existed, including hidden bottlenecks or unprofitable production lines<\/strong>. Improving predictive maintenance accuracy for every machine on the shop floor, uncovering how to increase the yield\/throughputs of each machine and associated workflow, and optimizing systems and supply chain optimization. The following graphic illustrates how machine learning is improving shop floor productivity beginning at the machine level first, then scaling out to workflows and the systems they rely on. Source: McKinsey,\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/manufacturing-analytics-unleashes-productivity-and-profitability\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/manufacturing-analytics-unleashes-productivity-and-profitability\" class=\"broken_link\">Manufacturing: Analytics unleashes productivity and profitability<\/a>, by Valerio Dilda, Lapo Mori, Olivier Noterdaeme, and Christoph Schmitz<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-345ab1f elementor-widget elementor-widget-image\" data-id=\"345ab1f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/blogs-images.forbes.com\/louiscolumbus\/files\/2019\/08\/bottleneck-assets.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-025d415 elementor-widget elementor-widget-text-editor\" data-id=\"025d415\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: MCKINSEY, MANUFACTURING: ANALYTICS UNLEASHES PRODUCTIVITY AND PROFITABILITY, BY VALERIO DILDA, LAPO MORI, OLIVIER NOTERDAEME, AND CHRISTOPH SCHMIT<\/small><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0374232 elementor-widget elementor-widget-text-editor\" data-id=\"0374232\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t<li><strong>Machine learning can significantly improve product configuration, and Configure-Price-Quote (CPQ) workflows manufacturers rely on to build-to-order products.<\/strong>\u00a0Siemens\u2019 approach to selling, designing, and installing railway interlocking control systems uses AI and machine learning to find the optimal configuration out of 10<sup>90<\/sup>\u00a0possible combinations. Machine learning is adept at defining the optimal configurations that best meet customers\u2019 needs while also being the most reliably manufactured. Source: Siemens,\u00a0<a href=\"https:\/\/assets.new.siemens.com\/siemens\/assets\/public.1559011182.cb8f9288-6f4a-4568-b8fe-7a1c03deef5b.15-22-may-en-ai-presentation-sid-2019-dr--michael-may-en-final-0.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/assets.new.siemens.com\/siemens\/assets\/public.1559011182.cb8f9288-6f4a-4568-b8fe-7a1c03deef5b.15-22-may-en-ai-presentation-sid-2019-dr--michael-may-en-final-0.pdf\" class=\"broken_link\">Next Level AI \u2013 Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May<\/a>, Chengdu, May 15th, 2019<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1ba9483 elementor-widget elementor-widget-image\" data-id=\"1ba9483\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/blogs-images.forbes.com\/louiscolumbus\/files\/2019\/08\/Product-Configuration.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7352a65 elementor-widget elementor-widget-text-editor\" data-id=\"7352a65\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: SIEMENS, NEXT LEVEL AI \u2013 POWERED BY KNOWLEDGE GRAPHS AND DATA THINKING, SIEMENS CHINA INNOVATION DAY, MICHAEL MAY, CHENGDU, MAY 15TH, 2019<\/small><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-40bd253 elementor-widget elementor-widget-text-editor\" data-id=\"40bd253\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>AI and machine learning adoption in manufacturing are predicted to eclipse robotics in the next five years, becoming the leading use case in manufacturing.<\/strong>\u00a0The complexity and constraints of supply chain operations are an ideal use case for machine learning algorithms to provide recommended solutions. Manufacturers are pursuing pilots on predictive maintenance today with those that deliver clear revenue gains being the most likely to move into production. Source: MAPI Foundation,\u00a0<a href=\"https:\/\/mapifoundation.org\/manufacturing-evolution\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/mapifoundation.org\/manufacturing-evolution\">The Manufacturing Evolution: How AI Will Transform Manufacturing &amp; the Workforce of the Future<\/a>\u00a0by Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 pp., opt-in)<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da97af0 elementor-widget elementor-widget-image\" data-id=\"da97af0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/blogs-images.forbes.com\/louiscolumbus\/files\/2019\/08\/MAPI-Study-AI-Deployment-Levels-1.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fc7835e elementor-widget elementor-widget-text-editor\" data-id=\"fc7835e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>SOURCE: MAPI FOUNDATION, THE MANUFACTURING EVOLUTION: HOW AI WILL TRANSFORM MANUFACTURING &amp; THE WORKFORCE OF THE FUTURE BY ROBERT D. ATKINSON, STEPHEN EZELL, INFORMATION TECHNOLOGY AND INNOVATION FOUNDATION (PDF, 56 PP., OPT-IN)<\/small><\/span><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ca250e elementor-widget elementor-widget-text-editor\" data-id=\"8ca250e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>Machine learning is revolutionizing how manufacturers secure every threat surface, relying on the Zero Trust Security (ZTS) framework to secure and scale their operations.<\/strong>\u00a0Manufacturers are turning to the\u00a0<a href=\"https:\/\/go.forrester.com\/blogs\/what-ztx-means-for-vendors-and-users\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/go.forrester.com\/blogs\/what-ztx-means-for-vendors-and-users\/\">Zero Trust Security (ZTS)\u00a0<\/a>framework to\u00a0secure every network, cloud and on-premise platform, operating system, and application across their supply chain and production networks.\u00a0<a href=\"https:\/\/go.forrester.com\/blogs\/author\/chase_cunningham\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/go.forrester.com\/blogs\/author\/chase_cunningham\/\">Chase Cunningham of Forrester<\/a>, Principal Analyst, is the leading authority on Zero Trust Security and his recent video,\u00a0<a href=\"https:\/\/go.forrester.com\/blogs\/zero-trust-in-practice\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/go.forrester.com\/blogs\/zero-trust-in-practice\/\">Zero Trust In Action<\/a>, is worth watching to learn more about how manufacturers can secure their IT infrastructures.\u00a0<a href=\"https:\/\/go.forrester.com\/blogs\/author\/chase_cunningham\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/go.forrester.com\/blogs\/author\/chase_cunningham\/\">You can find his blog here<\/a>. There are several fascinating companies to watch in this area, including\u00a0<a href=\"https:\/\/www.mobileiron.com\/en\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mobileiron.com\/en\">MobileIron<\/a>, which has created a mobile-centric, zero-trust enterprise security framework manufacturers are relying on today.\u00a0<a href=\"https:\/\/www.centrify.com\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.centrify.com\/\">Centrify\u2019s<\/a>\u00a0approach to Identity Access Management thwarts privileged account abuse, which is the leading cause of breaches today.\u00a0<a href=\"https:\/\/www.centrify.com\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.centrify.com\/\">Centrify\u2019s<\/a>\u00a0most recent survey,\u00a0<a href=\"https:\/\/www.centrify.com\/resources\/industry-research\/pam-survey\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.centrify.com\/resources\/industry-research\/pam-survey\/\">Privileged Access Management in the Modern Threatscape<\/a>, found that\u00a0<a href=\"https:\/\/www.centrify.com\/resources\/industry-research\/pam-survey\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.centrify.com\/resources\/industry-research\/pam-survey\/\">74% of all breaches involved access to a privileged account<\/a>. Privileged access credentials are hackers\u2019 most popular technique for initiating a breach to\u00a0exfiltrate valuable data from manufacturers and sell it on the Dark Web.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-726e7c0 elementor-widget elementor-widget-image\" data-id=\"726e7c0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/thumbor.forbes.com\/thumbor\/960x0\/https%3A%2F%2Fblogs-images.forbes.com%2Flouiscolumbus%2Ffiles%2F2019%2F08%2Fzts.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-503653c elementor-widget elementor-widget-text-editor\" data-id=\"503653c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\"><small>FORRESTER, WHAT ZTX MEANS FOR VENDORS AND USERS JANUARY 23, 2018<\/small><\/span><\/p>\n<strong>Additional reading:<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-a23910b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a23910b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a0095b7\" data-id=\"a0095b7\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3b661b5 elementor-widget elementor-widget-text-editor\" data-id=\"3b661b5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAccenture,\u00a0<a href=\"https:\/\/www.accenture.com\/t20180327t080053z__w__\/us-en\/_acnmedia\/pdf-74\/accenture-pov-manufacturing-digital-final.pdf#zoom=50\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.accenture.com\/t20180327t080053z__w__\/us-en\/_acnmedia\/pdf-74\/accenture-pov-manufacturing-digital-final.pdf#zoom=50\" class=\"broken_link\">Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies<\/a>\u00a0(PDF, 20 pp., no opt-in)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b1d1da elementor-widget elementor-widget-text-editor\" data-id=\"4b1d1da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAnderson, M. (2019). Machine learning in manufacturing.<em>\u00a0Automotive Design &amp; Production, 131<\/em>(4), 30-32.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-54703a4 elementor-widget elementor-widget-text-editor\" data-id=\"54703a4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tBruno, J. (2019). How the IIoT can change business models.<em>\u00a0Manufacturing Engineering, 163<\/em>(1), 12.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a91e336 elementor-widget elementor-widget-text-editor\" data-id=\"a91e336\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\nGreenfield, D. (2019). Advice on scaling IIoT projects.<em>\u00a0ProFood World<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb3bc00 elementor-widget elementor-widget-text-editor\" data-id=\"fb3bc00\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHayhoe, T., Podhorska, I., Siekelova, A., &amp; Stehel, V. (2019). Sustainable manufacturing in industry 4.0: Cross-sector networks of multiple supply chains, cyber-physical production systems, and AI-driven decision-making.<em>\u00a0Journal of Self-<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b37d9dc elementor-widget elementor-widget-text-editor\" data-id=\"b37d9dc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<em>Governance and Management Economics, 7<\/em>(2), 31-36.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-56610a3 elementor-widget elementor-widget-text-editor\" data-id=\"56610a3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHoneywell,\u00a0<a href=\"https:\/\/www.honeywellprocess.com\/library\/news-and-events\/presentations\/hug-america-2018-honeywell-connected-plant-introduction.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.honeywellprocess.com\/library\/news-and-events\/presentations\/hug-america-2018-honeywell-connected-plant-introduction.pdf\">The Honeywell Connected Plant, June, 2018<\/a>\u00a0(PDF, 36 pp., no opt-in)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f75291 elementor-widget elementor-widget-text-editor\" data-id=\"6f75291\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHow and why to digitize your supply chain. (2019).\u00a0<em>Manufacturing.Net.<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4837212 elementor-widget elementor-widget-text-editor\" data-id=\"4837212\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHow emerging technologies can transform the supply chain. (2019).\u00a0<em>Manufacturing.Net,<\/em>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-90f787d elementor-widget elementor-widget-text-editor\" data-id=\"90f787d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tIRI offers AI and machine learning in leading suite of analytic solutions. (2019).\u00a0<em>Manufacturing Close \u2013 Up<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c82d437 elementor-widget elementor-widget-text-editor\" data-id=\"c82d437\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tKazuyuki, M. (2019).\u00a0<em>Digitalization of manufacturing process and open innovation: Survey results of small and medium sized firms in japan<\/em>. St. Louis: Federal Reserve Bank of St Louis.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-619aa07 elementor-widget elementor-widget-text-editor\" data-id=\"619aa07\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/emerj.com\/ai-sector-overviews\/machine-learning-in-manufacturing\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/emerj.com\/ai-sector-overviews\/machine-learning-in-manufacturing\/\">Machine Learning in Manufacturing \u2013 Present and Future Use-Cases<\/a>, Emerj Artificial Intelligence Research, last updated May 20, 2019, published by Jon Walker\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a9ea440 elementor-widget elementor-widget-text-editor\" data-id=\"a9ea440\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMachine learning, AI are most impactful supply chain technologies. (2019).\u00a0<em>Material Handling &amp; Logistics<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1cedaba elementor-widget elementor-widget-text-editor\" data-id=\"1cedaba\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMAPI Foundation,\u00a0<a href=\"https:\/\/mapifoundation.org\/manufacturing-evolution\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/mapifoundation.org\/manufacturing-evolution\">The Manufacturing Evolution: How AI Will Transform Manufacturing &amp; the Workforce of the Future<\/a>\u00a0by Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 pp., opt-in)\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-328c930 elementor-widget elementor-widget-text-editor\" data-id=\"328c930\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMcKinsey Global Institute<a href=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/visualizing-the-uses-and-potential-impact-of-ai-and-other-analytics\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/visualizing-the-uses-and-potential-impact-of-ai-and-other-analytics\" class=\"broken_link\">, Visualizing the uses and potential impact of AI and other analytics<\/a>, Interactive Visualization Tool. April, 2018\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6524969 elementor-widget elementor-widget-text-editor\" data-id=\"6524969\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMcKinsey<a href=\"https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/lighthouse-manufacturers-lead-the-way\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/lighthouse-manufacturers-lead-the-way\" class=\"broken_link\">, \u2018Lighthouse\u2019 manufacturers lead the way\u2014can the rest of the world keep up?,<\/a>by Enno de Boer, Helena Leurent, and Adrian Widmer; January, 2019.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1bb5f69 elementor-widget elementor-widget-text-editor\" data-id=\"1bb5f69\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMcKinsey,\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/ai-in-production-a-game-changer-for-manufacturers-with-heavy-assets\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/ai-in-production-a-game-changer-for-manufacturers-with-heavy-assets\" class=\"broken_link\">AI in production: A game changer for manufacturers with heavy assets<\/a>, by Eleftherios Charalambous, Robert Feldmann, G\u00e9rard Richter, and Christoph Schmitz\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-491d906 elementor-widget elementor-widget-text-editor\" data-id=\"491d906\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMcKinsey,\u00a0<a href=\"https:\/\/www.mckinsey.com\/~\/media\/mckinsey\/business%20functions\/operations\/our%20insights\/how%20digital%20manufacturing%20can%20escape%20pilot%20purgatory\/digital-manufacturing-escaping-pilot-purgatory.ashx\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/~\/media\/mckinsey\/business%20functions\/operations\/our%20insights\/how%20digital%20manufacturing%20can%20escape%20pilot%20purgatory\/digital-manufacturing-escaping-pilot-purgatory.ashx\" class=\"broken_link\">Digital Manufacturing \u2013 escaping pilot purgatory<\/a>\u00a0(PDF, 24 pp., no opt-in)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-54b9c80 elementor-widget elementor-widget-text-editor\" data-id=\"54b9c80\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMcKinsey,\u00a0<a href=\"https:\/\/www.mckinsey.com\/~\/media\/McKinsey\/Business%20Functions\/McKinsey%20Digital\/Our%20Insights\/Driving%20impact%20at%20scale%20from%20automation%20and%20AI\/Driving-impact-at-scale-from-automation-and-AI.ashx\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/~\/media\/McKinsey\/Business%20Functions\/McKinsey%20Digital\/Our%20Insights\/Driving%20impact%20at%20scale%20from%20automation%20and%20AI\/Driving-impact-at-scale-from-automation-and-AI.ashx\" class=\"broken_link\">Driving Impact and Scale from Automation and AI<\/a>, February 2019 (PDF, 100 pp., no opt-in).\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-378113b elementor-widget elementor-widget-text-editor\" data-id=\"378113b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMcKinsey,\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/manufacturing-analytics-unleashes-productivity-and-profitability\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/manufacturing-analytics-unleashes-productivity-and-profitability\" class=\"broken_link\">Manufacturing: Analytics unleashes productivity and profitability<\/a>, by Valerio Dilda, Lapo Mori, Olivier Noterdaeme, and Christoph Schmitz, March, 2019\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1190ee4 elementor-widget elementor-widget-text-editor\" data-id=\"1190ee4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMcKinsey\/Harvard Business Review,\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/most-of-ais-business-uses-will-be-in-two-areas\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/most-of-ais-business-uses-will-be-in-two-areas\" class=\"broken_link\">Most of AI\u2019s business uses will be in two areas,<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-520671f elementor-widget elementor-widget-text-editor\" data-id=\"520671f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMorey, B. (2019). Manufacturing and AI: Promises and pitfalls.<em>\u00a0Manufacturing Engineering, 163<\/em>(1), 10.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f1666fe elementor-widget elementor-widget-text-editor\" data-id=\"f1666fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tOtto, S. (2018). How predictive maintenance is improving asset efficiency.<em>\u00a0Machine Design.<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5ae21cf elementor-widget elementor-widget-text-editor\" data-id=\"5ae21cf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tReducing the barriers to entry in advanced analytics. (2019).\u00a0<em>Manufacturing.Net,<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-95ddc6b elementor-widget elementor-widget-text-editor\" data-id=\"95ddc6b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tSeven ways real-time monitoring is driving smart manufacturing. (2019).\u00a0<em>Manufacturing.Net,<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-939b56b elementor-widget elementor-widget-text-editor\" data-id=\"939b56b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tSiemens,\u00a0<a href=\"https:\/\/assets.new.siemens.com\/siemens\/assets\/public.1559011182.cb8f9288-6f4a-4568-b8fe-7a1c03deef5b.15-22-may-en-ai-presentation-sid-2019-dr--michael-may-en-final-0.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/assets.new.siemens.com\/siemens\/assets\/public.1559011182.cb8f9288-6f4a-4568-b8fe-7a1c03deef5b.15-22-may-en-ai-presentation-sid-2019-dr--michael-may-en-final-0.pdf\" class=\"broken_link\">Next Level AI \u2013 Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May<\/a>, Chengdu, May 15th, 2019\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fca74bf elementor-widget elementor-widget-text-editor\" data-id=\"fca74bf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/manufacturingpolicy.indiana.edu\/doc\/Smart%20Factories.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/manufacturingpolicy.indiana.edu\/doc\/Smart%20Factories.pdf\" class=\"broken_link\">Smart Factories: Issues of Information Governance Manufacturing Policy Initiative School of Public and Environmental Affairs Indiana University, March 2019<\/a>\u00a0(PDF, 68 pp., no opt-in)\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce430b4 elementor-widget elementor-widget-text-editor\" data-id=\"ce430b4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.mckinsey.de\/files\/170419_mckinsey_ki_final_m.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/www.mckinsey.de\/files\/170419_mckinsey_ki_final_m.pdf\" class=\"broken_link\">Smartening up with Artificial Intelligence (AI) &#8211; What\u2019s in it for Germany and its Industrial Sector?<\/a>\u00a0(52 pp., PDF, no opt-in) McKinsey &amp; Company.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-041b7c6 elementor-widget elementor-widget-text-editor\" data-id=\"041b7c6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTeam predicts the useful life of batteries with data and AI. (2019, Mar 28).\u00a0<em>R &amp; D.<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5661793 elementor-widget elementor-widget-text-editor\" data-id=\"5661793\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/3er1viui9wo30pkxh1v2nh4w-wpengine.netdna-ssl.com\/wp-content\/uploads\/prod\/sites\/393\/2019\/06\/Microsoft_TheFutureComputed_AI_MFG_Final_Online.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/3er1viui9wo30pkxh1v2nh4w-wpengine.netdna-ssl.com\/wp-content\/uploads\/prod\/sites\/393\/2019\/06\/Microsoft_TheFutureComputed_AI_MFG_Final_Online.pdf\">The Future of AI and Manufacturing<\/a>, Microsoft, Greg Shaw (PDF, 73 pp., PDF, no opt-in).\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0fa292b elementor-widget elementor-widget-text-editor\" data-id=\"0fa292b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/pdfs.semanticscholar.org\/1034\/e70ec2d1ba1fe0dfa7a872f63ea8cbd11f69.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/pdfs.semanticscholar.org\/1034\/e70ec2d1ba1fe0dfa7a872f63ea8cbd11f69.pdf\">The Use of Machine Learning in Industrial Quality Control Thesis<\/a>\u00a0by Erik Granstedt M\u00f6ller for the degree of Master of Science in Engineering. KTH Royal Institute of Technology, published 2017. (PDF, 55 pp., no opt-in)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05ec18b elementor-widget elementor-widget-text-editor\" data-id=\"05ec18b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/medium.com\/activewizards-machine-learning-company\/top-8-data-science-use-cases-in-manufacturing-749256b8f1ee\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" track=\"ExternalLink:https:\/\/medium.com\/activewizards-machine-learning-company\/top-8-data-science-use-cases-in-manufacturing-749256b8f1ee\" class=\"broken_link\">Top 8 Data Science Use Cases in Manufacturing<\/a>, ActiveWizards: A Machine Learning Company Igor Bobriakov, March 12, 2019\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-26fae45 elementor-widget elementor-widget-text-editor\" data-id=\"26fae45\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tWalker, M. E. (2019). Armed with analytics: Manufacturing as a martial art.<em>\u00a0Industry Week<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fa051db elementor-widget elementor-widget-text-editor\" data-id=\"fa051db\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tWhittle, T., Gregova, E., Podhorska, I., &amp; Rowland, Z. (2019). Smart manufacturing technologies: Data-driven algorithms in production planning, sustainable value creation, and operational performance improvement.<em>\u00a0Economics, Management and Financial Markets, 14<\/em>(2), 52-57.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-44da853 elementor-widget elementor-widget-text-editor\" data-id=\"44da853\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tWhy software will drive the smart factory and the future of manufacturing. (2019).\u00a0<em>Manufacturing.Net<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a15fca3 elementor-widget elementor-widget-text-editor\" data-id=\"a15fca3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tZulick, J. (2019). How machine learning is transforming industrial production.<em>\u00a0Machine Design<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>AI has the potential to create $1.4T to $2.6T of value in marketing and sales across the world\u2019s businesses, and\u00a0$1.2T to $2T in supply-chain management and manufacturing. By 2021, 20% of leading manufacturers will rely on embedded intelligence, using AI, IoT, and blockchain applications to automate processes and increase execution times by up to 25%<\/p>\n","protected":false},"author":138,"featured_media":3845,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[92],"ppma_author":[2679],"class_list":["post-1932","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-machine-learning"],"authors":[{"term_id":2679,"user_id":138,"is_guest":0,"slug":"louis-columbus","display_name":"Louis Columbus","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_65812956-54d2-4b01-9e05-0d23abb284c7-150x150.jpg","user_url":"https:\/\/softwarestrategiesblog.com\/","last_name":"Columbus","first_name":"Louis","job_title":"","description":"Louis Columbus is currently serving as Principal, IQMS. He is Marketing and Product Management Leader, Forbes Columnist, Software Expertise in Analytics, Cloud, CPQ &amp; ERP Solutions. He teaches MBA courses in international business, global competitive strategies, international market research, and capstone courses in strategic planning and market research. He has taught at California State University, Fullerton: University of California, Irvine; Marymount University, and Webster University."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1932","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/138"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1932"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1932\/revisions"}],"predecessor-version":[{"id":36664,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1932\/revisions\/36664"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3845"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1932"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1932"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1932"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1932"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}