Bibtex
Cite as text
@Select Types{,
Journal = "Band-2",
Title= "Closing the Gap between Smart Manufacturing Applications and Data Management",
Author= "Emanuel Marx, Matthias Stierle, Sven Weinzierl, Martin Matzner Friedrich-Alexander",
Doi= "https://doi.org/10.30844/wi_2020_u1-marx",
Abstract= "Smart manufacturing refers to the intensified collaboration of machines, products, and people throughout the manufacturing and the supply chain. This facilitates innovative products, services, business models, and processes. Smart manufacturing is premised on emerging technologies such as cloud computing, mobile computing, the Internet of Things, data analytics, and artificial intelligence. A plethora of companies struggles with the implementation of corresponding applications. In research and practice, we see general data management approaches with primary attention on building architectures that are not tailored to fit a particular domain/ application scenario. However, a robust data management concept is vital, as smart manufacturing decisively depends on data. To address this substantial deficit, we conduct a comprehensive literature review, an expert workshop, and semistructured expert interviews with one of the leading German automotive manufacturers. The result is a catalog of requirements and a framework for data management that fosters the implementation of smart manufacturing applications.
",
Keywords= "smart manufacturing, smart factory, data management, data analytics, expert interview
",
}
Emanuel Marx, Matthias Stierle, Sven Weinzierl, Martin Matzner Friedrich-Alexander: Closing the Gap between Smart Manufacturing Applications and Data Management. Online: https://doi.org/10.30844/wi_2020_u1-marx (Abgerufen 23.11.24)
Open Access
Smart manufacturing refers to the intensified collaboration of machines, products, and people throughout the manufacturing and the supply chain. This facilitates innovative products, services, business models, and processes. Smart manufacturing is premised on emerging technologies such as cloud computing, mobile computing, the Internet of Things, data analytics, and artificial intelligence. A plethora of companies struggles with the implementation of corresponding applications. In research and practice, we see general data management approaches with primary attention on building architectures that are not tailored to fit a particular domain/ application scenario. However, a robust data management concept is vital, as smart manufacturing decisively depends on data. To address this substantial deficit, we conduct a comprehensive literature review, an expert workshop, and semistructured expert interviews with one of the leading German automotive manufacturers. The result is a catalog of requirements and a framework for data management that fosters the implementation of smart manufacturing applications.
smart manufacturing, smart factory, data management, data analytics, expert interview
1. Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing. Methods and applications. Journal of Manufacturing Systems 48, 144–156 (2018)
2. Luckow, A., Kennedy, K., Manhardt, F., Djerekarov, E., Vorster, B., Apon, A. (eds.): Automotive big data. Applications, workloads and infrastructures (2015)
3. Urbach, N., Drews, P., Ross, J.: Digital business transformation and the changing role of the IT function. MIS Quarterly Executive 16, 2–4 (2017)
4. Urbach, N., Ahlemann, F., Böhmann, T., Drews, P., Brenner, W., Schaudel, F., Schütte, R.: The impact of digitalization on the IT department. Business & Information Systems Engineering 61, 123–131 (2019)
5. Mittal, S., Khan, M.A., Romero, D., Wuest, T.: Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, 1342–1361 (2019)
6. Surdilovic, D., Schreck, G., Schmidt, U. (eds.): Development of collaborative robots (COBOTS) for flexible human-integrated assembly automation (2010)
7. Bullinger, H.-J., Meiren, T., Nägele, R.: Smart services in manufacturing companies. Unpublished
8. Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. Journal of Manufacturing Systems 48, 157–169 (2018)
9. Widom, J.: Research Problems in Data Warehousing. In: Fourth International Conference on Information and Knowledge Management (CIKM 1995) (1995)
10. Fang, H.: Managing data lakes in big data era: What’s a data lake and why has it became popular in data management ecosystem. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 820–824. IEEE (2015 – 2015)
11. Sang, G.M., Xu, L., Vrieze, P.T.d. (eds.): A reference architecture for big data systems (2016)
12. Groggert, S., Wenking, M., Schmitt, R.H., Friedli, T. (eds.): Status quo and future potential of manufacturing data analytics. An empirical study (2017)
13. Li, J., Tao, F., Cheng, Y., Zhao, L.: Big data in product lifecycle management. The International Journal of Advanced Manufacturing Technology 81, 667–684 (2015)
14. Tariq, R.S., Thabet, N.: Big data challenges. Computer Engineering & Information Technology 4 (2015)
15. Ji, W., Wang, L.: Big data analytics based fault prediction for shop floor scheduling. Journal of Manufacturing Systems 43, 187–194 (2017)
16. Santos, M.Y., Oliveira e Sá, J., Costa, C., Galvão, J., Andrade, C., Martinho, B., Lima, F.V., Costa, E. (eds.): A big data analytics architecture for industry 4.0 (2017)
17. Riggins, F.J., Wamba, S.F. (eds.): Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics (2015)
18. Li, S., Peng, G.C., Xing, F.: Barriers of embedding big data solutions in smart factories. Insights from SAP consultants. Industrial Management & Data Systems 119, 1147–1164 (2019)
19. Gröger, C., Kassner, L., Hoos, E., Königsberger, J., Kiefer, C., Silcher, S., Mitschang, B. (eds.): The data-driven factory. Leveraging big industrial data for agile, learning and human-centric manufacturing (2016)
20. Hai, R., Geisler, S., Quix, C. (eds.): Constance. An intelligent data lake system (2016)
21. Cuzzocrea, A., Song, I.-Y., Davis, K.C.: Analytics over large-scale multidimensional data. In: International Workshop on Data Warehousing, pp. 101–103
22. Bauernhansl, T., Hompel, M. ten, Vogel-Heuser, B.: Industrie 4.0 in Produktion, Automatisierung und Logistik. Anwendung – Technologien – Migration. Springer Fachmedien Wiesbaden, Wiesbaden (2014)
23. Wixom, B.H., Watson, H.J.: An empirical investigation of the factors affecting data warehousing success. MIS Quarterly 25, 17 (2001)
24. Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. ACM Sigmod Record 26, 65–74 (1997)
25. Gupta, H.: Selection of views to materialize in a data warehouse. IEEE Transactions on Knowledge and Data Engineering 17, 98–112 (2005)
26. Tryfona, N., Busborg, F., Borch Christiansen, J.G. (eds.): starER. A conceptual model for data warehouse design (1999)
27. Kimball, R., Ross, M.: The data warehouse toolkit. The complete guide to dimensional modeling. John Wiley & Sons, Indianapolis, IN (2011)
28. Davenport, T.H., Barth, P., Bean, R.: How ‘big data’ is different. MIT Sloan Management Review 54 (2012)
29. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data. The next frontier for innovation, competition, and productivity. New York, NY (2011)
30. Mathis, C.: Data lakes. Datenbank Spektrum 17, 289–293 (2017)
31. Bruckner, R.M., List, B., Schiefer, J. (eds.): Striving towards near real-time data integration for data warehouses (2002)
32. Miloslavskaya, N., Tolstoy, A.: Big data, fast data and data lake concepts. Procedia Computer Science 88, 300–305 (2016)
33. van der Lans, Rick F.: Architecting the multi-purpose data lake with data virtualization. Lisse (2018)
34. Madera, C., Laurent, A. (eds.): The next information architecture evolution. The data lake wave (2016)
35. Hevner, A.R., Ram, S., March, S.T.: Design science in information systems research. MIS Quarterly 28, 75–105 (2004)
36. March, S.T., Smith, G.F.: Design and natural science research on information technology. Decision Support Systems 15, 251–266 (1995)
37. Benbasat, I., Goldstein, D.K., Mead, M.: The Case Research Strategy in Studies of Information Systems. MIS Quarterly 11, 369 (1987)
38. Eisenhardt, K.M.: Building theories from case study research. The Academy of Management Review 14, 532–550 (1989)
39. Yin, R.K.: Case study research and applications. Design and methods. Sage, Los Angeles, CA (2018)
40. Ketokivi, M., Choi, T.: Renaissance of case research as a scientific method. Journal of Operations Management 32, 232–240 (2014)
41. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future. Writing a literature review. MIS Quarterly 26, 13–23 (2002)
42. Cooper, D.R., Schindler, P.S.: Business research methods. McGraw-Hill Irwin, New York, NY (2014)
43. Bauer, D., Maurer, T., Henkel, C., Bildstein, A.: Big-Data-Analytik. Datenbasierte Optimierung produzierender Unternehmen. Zenodo, Stuttgart (2017)
44. Delen, D., Demirkan, H.: Data, information and analytics as services. Decision Support Systems 55, 359–363 (2013)
45. Watson, H.J.: Tutorial: big data analytics. Concepts, technologies, and applications. Communications of the Association for Information Systems 34, 1247–1268 (2014)
46. Hu, H., Wen, Y., Chua, T.-S., Li, X.: Toward scalable systems for big data analytics. A technology tutorial. IEEE Access 2, 652–687 (2014)
47. Freitag, M., Kück, M., Alla, A.A., Lütjen, M.: Potenziale von Data Science in Produktion und Logistik. Teil 1 – Eine Einführung in aktuelle Ansätze der Data Science. Industrie Management 31, 22–26 (2015)
48. Bokranz, R., Landau, K.: Handbuch Industrial Engineering. Produktivitätsmanagement mit MTM. Schäffer-Poeschel, Stuttgart (2012)
49. Verband für Arbeitsgestaltung, Betriebsorganisation und Unternehmensentwicklung: Industrial Engineering. Standardmethoden zur Produktivitätssteigerung und Prozessoptimierung. Hanser, München (2015)
50. Dorner, M., Stowasser, S.: Das Produktivitätsmanagement des Industrial Engineering. • Modellentwicklung • Führungssystem • Produktivitätskennzahl • Verbesserung der Arbeitsproduktivität • Produktivitätscontrolling. Zeitschrift für Arbeitswissenschaft 66, 212–225 (2012)
51. Scheffler, A., Wirths, C.P.: Data innovation @ AXA Germany. Journey towards a datadriven insurer. In: Urbach, N., Röglinger, M. (eds.) Digitalization cases, pp. 363–378. Springer International Publishing, Cham (2019)
52. Nyhuis, P., Reinhart, G., Abele, E. (eds.): Wandlungsfähige Produktionssysteme. Heute die Industrie von morgen gestalten. Produktionstechnisches Zentrum Hannover, Garbsen (2008)
53. Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F.: Guide to intelligent data analysis. How to intelligently make sense of real data. Springer London, London (2010)
54. Anagnostopoulos, I., Zeadally, S., Exposito, E.: Handling big data. Research challenges and future directions. The Journal of Supercomputing 72, 1494–1516 (2016)
55. Balachandran, B.M., Prasad, S.: Challenges and benefits of deploying big data analytics in the cloud for business intelligence. Procedia Computer Science 112, 1112–1122 (2017)
56. Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A.S., Buyya, R.: Big data computing and clouds. Trends and future directions. Journal of Parallel and Distributed Computing 79-80, 3–15 (2015)
57. Sheth, A.P.: Changing focus on interoperability in information systems. From system, syntax, structure to semantics. In: Goodchild, M., Egenhofer, M., Fegeas, R., Kottman, C. (eds.) Interoperating geographic information systems, 495, pp. 5–29. Springer US, Boston, MA (1999)
58. Gerloff, C., Cleophas, C. (eds.): Excavating the treasure of IoT data. An architecture to empower rapid data analytics for predictive maintenance of connected vehicles (2017)
59. Jacoby, M., Antonić, A., Kreiner, K., Łapacz, R., Pielorz, J. (eds.): Semantic interoperability as key to IoT platform federation (2017)
60. Wunder, M., Grosche, J.: Verteilte Führungsinformationssysteme. Springer Berlin Heidelberg, Berlin, Heidelberg (2009)
61. Khatri, V., Brown, C.V.: Designing data governance. Communications of the ACM 53, 148/152 (2010)
62. Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A.: Data quality for data science, predictive analytics, and big data in supply chain management. An introduction to the problem and suggestions for research and applications. International Journal of Production Economics 154, 72–80 (2014)
63. Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1, 51–59 (2013)
64. Baesens, B., Bapna, R., Marsden, J.R., Vanthienen, J., Zhao, J.L.: Transformational issues of big data and analytics in networked business. MIS Quarterly 40, 807–818 (2016)65. Ghasemaghaei, M., Ebrahimi, S., Hassanein, K.: Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems 27, 101– 113 (2018)
66. Bose, R.: Advanced analytics. Opportunities and challenges. Industrial Management & Data Systems 109, 155–172 (2009)
67. Divate, R., Sah, S., Singh, M.: High performance computing and big data. In: Srinivasan, S. (ed.) Guide to big data applications, 26, pp. 125–147. Springer International Publishing, Cham (2018)
68. Singh, D., Reddy, C.K.: A survey on platforms for big data analytics. Journal of Big Data 2, 1–20 (2015)
69. Kimball, R., Ross, M.: The data warehouse toolkit. The definitive guide to dimensional modeling. John Wiley & Sons, Indianapolis, IN (2013)
70. van der Aalst, W.: Process mining. Data science in action. Springer Berlin Heidelberg, Berlin, Heidelberg (2016)
71. Vermeulen, A.F.: Practical data science. A guide to building the technology stack for turning data lakes into business assets. Apress, Berkeley, CA (2018)
72. Periasamy, M., Chelliah, P.R.: Big data analytics. Enabling technologies and tools. In: Mahmood (Ed.) 2016 – data science and big data, pp. 221–243
73. Diemer, J.: Sichere Industrie-4.0-Plattformen auf Basis von Community-Clouds. In: Vogel-Heuser, B., Bauernhansl, T., Hompel, M. ten (eds.) Handbuch Industrie 4.0 Bd.1. Produktion, pp. 177–204. Springer Berlin Heidelberg, Berlin, Heidelberg (2017)
74. Krishnan, K.: Data warehousing in the age of big data. Morgan Kaufmann, Waltham, MA (2013)
75. D. Sudarsan, S., Jetley, R., Ramaswamy, S.: Security and privacy of big data. In: Big Data, pp. 121–136