Machine Learning in Production

Application areas and freely available data sets

JournalIndustrie 4.0 Management
Issue Volume 35, 2019, Edition 4, Pages 39-42
Open Accesshttps://doi.org/10.30844/I40M_19-4_S39-42
Bibliography Share Cite Download

Abstract

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?

Keywords


Bibliography

[1] Roosevelt Institute: Six Reasons Manufacturing is Central to the Economy. URL: rooseveltinstitute.org/six-reasons-manufacturing-central-economy/, Abrufdatum 02.04.2019.
[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.
[3] Ademujimi, T. T.; Brundage, M. P.; Prabhu, V. V.: A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis. In: Lödding, H.; Riedel, R.; Thoben, K.-D. u. a. (Hrsg): Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. Cham 2017.
[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.
[5] Gursch, H.; Wuttei, A.; Gangloff Theresa: Learning Systems for Manufacturing Management Support. SamI40 workshop at i-KNOW ’16. Graz 2016.
[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.
[7] Lödding, H.; Riedel, R.; Thoben, K.-D. u. a. (Hrsg): Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. Cham 2017.
[8] McKinsey & Company: Smartening up with Artificial Intelligence (AI). What’s in it for Germany and its Industrial Sector?. Düsseldorf, Berlin, München 2017.
[9] McKinsey Global Institute: The Age of Analytics: Competing in a Data-Driven World. In collaboration with McKinsey Analytics. Düsseldorf, Berlin, München 2016.
[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.
[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.
[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.
[13] World Economic Forum with A.T. Kearney: Technology and Innovation for the Future of Production: Accelerating Value Creation. Geneva 2017.
[14] Wuest, T.; Weimer, D.; Irgens, C. u. a.: Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research 4 (2016) 1, S. 23-45.
[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.
[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.
[17] von Enzberg, S; Waschbusch, L. M.: Datenanalyse. Big Data in der Produktion: große Daten = großes Potential. URL: www.industry-of-things.de/big-data-in-der-produktion-grosse-daten-grosses-potential-a-776716/, Abrufdatum 16.02.2019.
[18] Helden, J. von; Dorißen, J.: OPENMIND – On-demand production of entirely customised minimally invasive medical devices – H2020. Impact 2018 (2018) 10, S. 60-62.
[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.
[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.
[21] DIN: Arbeitsausschuss Künstliche Intelligenz gegründet. URL: www.din.de/de/din-und-seine-partner/presse/mitteilungen/arbeitsausschuss-kuenstliche-intelligenz-gegruendet-259904, Abrufdatum 27.02.2019.
[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.
[23] TÜV SÜD: TÜV SÜD und DFKI entwickeln „TÜV für Künstliche Intelligenz“. URL: www.tuev-sued.de/tuev-sued-konzern/presse/pressearchiv/tuv-sud-und-dfki-entwickeln-tuv-fur-kunstliche-intelligenz, Abrufdatum 27.02.2019.
[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.
[25] Bundesministerium für Bildung und Forschung: Forschung und Innovation für die Menschen. Die Hightech-Strategie 2025. Berlin 2018.

Your downloads