Abstract
Various novel and data-driven business concepts have emerged during the fourth industrial revolution. Smart manufacturing, for example, utilizes data from manufacturing equipment, human operators, and organizational IT systems to enable dynamic adaptions in production systems. Nowadays, these data are often distributed among multiple partners in collaborative value creation networks. Hence, to identify and collect relevant data for given business cases has become an important, but complex issue. To support the process of establishing comprehensive data provision in industrial practice, a reference model for knowledge-driven data provision processes was developed. It describes a systematic approach to drive operationalization of data provision from knowledge requirements to identify, extract and provide raw data until the application of such data sets. To evaluate the applicability of the reference model, a case study was conducted in which it was used to guide the implementation of an IoT Solution in four Swedish manufacturing companies.
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References
Kang, H.S., et al.: Smart manufacturing: past research, present findings, and future directions. Int. J. Precis. Eng. Manuf. Green Technol. 3(1), 111–128 (2016). https://doi.org/10.1007/s40684-016-0015-5
Wang, W.M., Preidel, M., Fachbach, B., Stark, R.: Towards a reference model for knowledge driven data provision processes. In: Camarinha-Matos, L.M., Afsarmanesh, H., Ortiz, A. (eds.) PRO-VE 2020. IAICT, vol. 598, pp. 123–132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62412-5_10
Kusiak, A.: Smart manufacturing. Int. J. Prod. Res. 56, 508–517 (2018)
Jiang, J.-R.: An improved cyber-physical systems architecture for Industry 4.0 smart factories. Adv. Mech. Eng. 10, 15 (2018)
Lee, J., Bagheri, B., Kao, H.-A.: A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015). https://doi.org/10.1016/j.mfglet.2014.12.001
Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M.: How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4.0 perspective. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 8, 37–44 (2014)
Stark, R.: Virtual Product Creation in Industry. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-662-64301-3
Forza, C., Salvador, F.: Information flows for high-performance manufacturing. Int. J. Prod. Econ. 70, 21–36 (2001)
ISO: IEC 62264-1: 2013: Enterprise-control system integration—Part 1: models and terminology
Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manuf. Syst. 48, 157–169 (2018)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37–54 (1996)
Cios, K.J., Kurgan, L.A.: Trends in data mining and knowledge discovery. In: Pal, N.R., Jain, L. (eds.) Advanced Techniques in Knowledge Discovery and Data Mining, pp. 1–26. Springer, London (2005). https://doi.org/10.1007/1-84628-183-0_1
Han, J., Kamber, M., Pei, J.: Data Mining. Concepts and Techniques. Morgan Kaufmann/Elsevier, Waltham (2012)
Mariscal, G., Marbán, Ó., Fernández, C.: A survey of data mining and knowledge discovery process models and methodologies. Knowl. Eng. Rev. 25, 137–166 (2010)
Smyth, P.: Data mining: data analysis on a grand scale? Stat. Methods Med. Res. 9, 309–327 (2000)
Azevedo, A., Santos, M.F.: KDD, SEMMA and CRISP-DM: a parallel overview. In: IADIS European Conference on Data Mining, Amsterdam, The Netherlands (2008)
Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, vol. 1, pp. 29–40 (2000)
Wiemer, H., Drowatzky, L., Ihlenfeldt, S.: Data mining methodology for engineering applications (DMME)—a holistic extension to the CRISP-DM model. Appl. Sci. 9, 2407 (2019). https://doi.org/10.3390/app9122407
Loosen, W.: Das Leitfadeninterview – eine unterschätzte Methode. In: Averbeck-Lietz, S., Meyen, M. (eds.) Handbuch nicht standardisierte Methoden in der Kommunikationswissenschaft. SN, pp. 139–155. Springer, Wiesbaden (2016). https://doi.org/10.1007/978-3-658-01656-2_9
Longhurst, R.: Semi-structured interviews and focus groups. In: Key Methods in Geography, vol. 3, pp. 143–156 (2003)
Mayring, P.: Qualitative content analysis: theoretical foundation, basic procedures and software solution (2014)
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Wang, W.M., Ebel, H., Kohler, S., Stark, R. (2022). Knowledge-Driven Data Provision to Enhance Smart Manufacturing – A Case Study in Swedish Manufacturing SME. In: Camarinha-Matos, L.M., Ortiz, A., Boucher, X., Osório, A.L. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2022. IFIP Advances in Information and Communication Technology, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-031-14844-6_2
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