{"id":93619,"date":"2019-08-15T12:00:00","date_gmt":"2019-08-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&#038;p=93619"},"modified":"2024-09-25T14:49:28","modified_gmt":"2024-09-25T12:49:28","slug":"machine-learning-in-production","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/machine-learning-in-production\/","title":{"rendered":"Machine Learning in Production"},"content":{"rendered":"<hr>\n<div class=\"gito-pub-content-bibliography\">\n<h2>Bibliography <\/h2>\n[1] Roosevelt Institute: Six Reasons Manufacturing is Central to the Economy. URL: rooseveltinstitute.org\/six-reasons-manufacturing-central-economy\/, Abrufdatum 02.04.2019.<br \/>\n[2] McKinsey: Manufacturing the future. The next era of global growth and innovation. URL: www.mckinsey.com\/~\/media\/McKinsey\/Business%20Functions\/Operations\/Our%20Insights\/The%20future%20of%20manufacturing\/MGI_%20 Manufacturing_Full%20report_Nov%202012.ashx, Abrufdatum 02.04.2019.<br \/>\n[3] Ademujimi, T. T.; Brundage, M. P.; Prabhu, V. V.: A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis. In: L\u00f6dding, H.; Riedel, R.; Thoben, K.-D. u. a. (Hrsg): Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. Cham 2017.<br \/>\n[4] Geissbauer, R.; Schrauf, S.; Berttram, P. u. a.: Digital Factories 2020: Shaping the future of manufacturing. URL: www.pwc.de\/de\/digitale-transformation\/digital-factories-2020-shaping-the-future-of-manufacturing.pdf, Abrufdatum 02.04.2019.<br \/>\n[5] Gursch, H.; Wuttei, A.; Gangloff Theresa: Learning Systems for Manufacturing Management Support. SamI40 workshop at i-KNOW \u201916. Graz 2016.<br \/>\n[6] Harding, J. A.; Shahbaz, M.; Srinivas u. a.: Data Mining in Manufacturing: A Review. Journal of Manufacturing Science and Engineering 128 (2006) 4, S. 969.<br \/>\n[7] L\u00f6dding, H.; Riedel, R.; Thoben, K.-D. u. a. (Hrsg): Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. Cham 2017.<br \/>\n[8] McKinsey &amp; Company: Smartening up with Artificial Intelligence (AI). What\u2019s in it for Germany and its Industrial Sector?. D\u00fcsseldorf, Berlin, M\u00fcnchen 2017.<br \/>\n[9] McKinsey Global Institute: The Age of Analytics: Competing in a Data-Driven World. In collaboration with McKinsey Analytics. D\u00fcsseldorf, Berlin, M\u00fcnchen 2016.<br \/>\n[10] Tata Consultancy Services Ltd. (TCS): The Emerging Big Returns on Big Data. A TCS 2013 Global Trend Study. URL: https:\/\/ch.semweb.ch\/_\/wordpress\/wp-con- tent\/uploads\/2013\/08\/TCS-Big-Data-Global-Trend-Study-2013.pdf, Abrufdatum 02.04.2019.<br \/>\n[11] Tata Consultancy Services Ltd. (TCS): Using Big Data for Machine Learning Analytics in Manufacturing 2014. URL: pdfs.semanticscholar.org\/2f6a\/0e8a8ce601b-d435aeaa140c7168177dc4820.pdf, Abrufdatum 02.04.2019.<br \/>\n[12] Wang, J.; Ma, Y.; Zhang, L. u. a.: Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems 48 (2018), S. 144-156.<br \/>\n[13] World Economic Forum with A.T. Kearney: Technology and Innovation for the Future of Production: Accelerating Value Creation. Geneva 2017.<br \/>\n[14] Wuest, T.; Weimer, D.; Irgens, C. u. a.: Machine learning in manufacturing: advantages, challenges, and applications. Production &amp; Manufacturing Research 4 (2016) 1, S. 23-45.<br \/>\n[15] Priya Singh: 10 Reasons why big data and analytics projects fail. URL: www.analyticsindiamag.com\/10-reasons-big- data-analytics-projects-fail\/, Abrufdatum 16.02.2019.<br \/>\n[16] Driscoll, M: Building data startups: Fast, big, and focused. URL: http:\/\/radar.oreilly.com\/2011\/08\/building-data-startups.html, Abrufdatum 16.02.2019.<br \/>\n[17] von Enzberg, S; Waschbusch, L. M.: Datenanalyse. Big Data in der Produktion: gro\u00dfe Daten = gro\u00dfes Potential. URL: www.industry-of-things.de\/big-data-in-der-produktion-grosse-daten-grosses-potential-a-776716\/, Abrufdatum 16.02.2019.<br \/>\n[18] Helden, J. von; Dori\u00dfen, J.: OPENMIND \u2013 On-demand production of entirely customised minimally invasive medical devices \u2013 H2020. Impact 2018 (2018) 10, S. 60-62.<br \/>\n[19] Deloitte: Predictive Maintenance. Taking pro-active measures based on advanced data analytics to predict and avoid machine failure 2017.URL: www2.deloitte.com\/ content\/dam\/Deloitte\/de\/Documents\/deloitte-analytics\/Deloitte_Predictive-Maintenance_PositionPaper.pdf, Abrufdatum 02.04.2019.<br \/>\n[20] Deloitte: Predictive maintenance and the smart factory. URL: www2. deloitte.com\/content\/dam\/Deloitte\/us\/Documents\/process-and-operations\/us-cons-predictive-maintenance.pdf Abrufdatum 02.04.2019.<br \/>\n[21] DIN: Arbeitsausschuss K\u00fcnstliche Intelligenz gegr\u00fcndet. URL: www.din.de\/de\/din-und-seine-partner\/presse\/mitteilungen\/arbeitsausschuss-kuenstliche-intelligenz-gegruendet-259904, Abrufdatum 27.02.2019.<br \/>\n[22] IEEE Standards Association: IEEE Launches Ethics Certification Program for Autonomous and Intelligent Systems. URL: standards.ieee.org\/news\/2018\/ieee-launches-ecpais.html, Abrufdatum 27.02.2019.<br \/>\n[23] T\u00dcV S\u00dcD: T\u00dcV S\u00dcD und DFKI entwickeln \u201eT\u00dcV f\u00fcr K\u00fcnstliche Intelligenz\u201c. URL: www.tuev-sued.de\/tuev-sued-konzern\/presse\/pressearchiv\/tuv-sud-und-dfki-entwickeln-tuv-fur-kunstliche-intelligenz, Abrufdatum 27.02.2019.<br \/>\n[24] VDE Presse: KI: VDE|DKE und IEEE wollen Ethik in der Technik implementieren. URL: www.vde.com\/de\/presse\/pressemitteilungen\/vde-und-ieee-wollen-ethik-in-ki-implementieren, Abrufdatum 27.02.2019.<br \/>\n[25] Bundesministerium f\u00fcr Bildung und Forschung: Forschung und Innovation f\u00fcr die Menschen. Die Hightech-Strategie 2025. Berlin 2018.<\/div>\n<div id=\"download-section\" class=\"gito-pub-download-section\" style=\"text-align:center;margin:20px;\">\n<h2>Your downloads<\/h2>\n<p><button style=\"font-size:14px;margin-right:15px;\" class=\"button gito-pub-cpt-download-button\" data-postid=\"93619\" data-userid =\"0\" data-filename=\"krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4.pdf\"><span style=\"margin-top:5px !important;\" class=\"dashicons dashicons-download\"><\/span>&nbsp;&nbsp;PDF<\/button><\/div>\n<div class=\"gito-pub-tags-social-share\" style=\"display:flex;justify-content:space-between;\"><div>Tags: <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/application-areas-en\/\">Application Areas<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/artificial-intelligence-en-2\/\">Artificial intelligence<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/big-data-en\/\">Big Data<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/data-analytics-en\/\">Data Analytics<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/kuenstliche-intelligenz-en\/\">K\u00fcnstliche Intelligenz<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/machine-learning-en\/\">machine learning<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/production-technology-en\/\">production technology<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/use-cases-en\/\">use cases<\/a><\/span> <\/div><div><div class=\"social-icons share-icons share-row relative\" ><a href=\"whatsapp:\/\/send?text=Machine%20Learning%20in%20Production - https:\/\/industry-science.com\/en\/articles\/machine-learning-in-production\/\" data-action=\"share\/whatsapp\/share\" class=\"icon button circle is-outline tooltip whatsapp show-for-medium\" title=\"Share on WhatsApp\" aria-label=\"Share on WhatsApp\"><i class=\"icon-whatsapp\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.facebook.com\/sharer.php?u=https:\/\/industry-science.com\/en\/articles\/machine-learning-in-production\/\" data-label=\"Facebook\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip facebook\" title=\"Share on Facebook\" aria-label=\"Share on Facebook\" rel=\"noopener nofollow\"><i class=\"icon-facebook\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/x.com\/share?url=https:\/\/industry-science.com\/en\/articles\/machine-learning-in-production\/\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip x\" title=\"Share on X\" aria-label=\"Share on X\" rel=\"noopener nofollow\"><i class=\"icon-x\" aria-hidden=\"true\"><\/i><\/a><a href=\"mailto:?subject=Machine%20Learning%20in%20Production&body=Check%20this%20out%3A%20https%3A%2F%2Findustry-science.com%2Fen%2Farticles%2Fmachine-learning-in-production%2F\" class=\"icon button circle is-outline tooltip email\" title=\"Email to a Friend\" aria-label=\"Email to a Friend\" rel=\"nofollow\"><i class=\"icon-envelop\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.linkedin.com\/shareArticle?mini=true&url=https:\/\/industry-science.com\/en\/articles\/machine-learning-in-production\/&title=Machine%20Learning%20in%20Production\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip linkedin\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" rel=\"noopener nofollow\"><i class=\"icon-linkedin\" aria-hidden=\"true\"><\/i><\/a><\/div><\/div><\/div><hr style=\"margin-top:0px;\">\n","protected":false},"excerpt":{"rendered":"<p>Data sets increasing data bases and computing power as well as decreasing costs for computing and storage capacities form the basis for the use of Machine Learning (ML) in production. The challenges are the identification of promising application areas, the recognition of the associated learning tasks as well as the uncovering of suitable data sets. This article therefore answers the following questions: Which application areas in production offer the greatest potential for the use of ML? Which freely accessible data sets are suitable for gaining experience and which learning tasks are associated with them? What are best practices for the application areas?<\/p>\n","protected":false},"featured_media":96410,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[80129,80028,80175,80176,80025,79574,80177,79458],"product_cat":[],"topic":[],"technology":[67599,67790],"knowhow":[],"industry":[],"writer":[83038,83037,83036,83039,81078],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":{"0":"post-93619","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"category-design-en","7":"category-translate-en","8":"category-typeset","9":"tag-application-areas-en","10":"tag-artificial-intelligence-en-2","11":"tag-big-data-en","12":"tag-data-analytics-en","13":"tag-kuenstliche-intelligenz-en","14":"tag-machine-learning-en","15":"tag-production-technology-en","16":"tag-use-cases-en","17":"technology-analytics-en","18":"technology-artificial-intelligence","19":"writer-hendrik-mende-en","20":"writer-jonas-dorissen-en","21":"writer-jonathan-krauss-en","22":"writer-maik-frye-en","23":"writer-robert-schmitt-en","24":"product","25":"first","26":"instock","27":"downloadable","28":"virtual","29":"sold-individually","30":"taxable","31":"purchasable","32":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/krauss-Maschinelles-Lernen-in-der-Produktion_IM-2019-4-1400x788-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Christoph Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Data sets increasing data bases and computing power as well as decreasing costs for computing and storage capacities form the basis for the use of Machine Learning (ML) in production. The challenges are the identification of promising application areas, the recognition of the associated learning tasks as well as the uncovering of suitable data sets.&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/93619","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/96410"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=93619"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=93619"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=93619"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=93619"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=93619"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=93619"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=93619"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=93619"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=93619"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=93619"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=93619"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=93619"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=93619"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}