Abstract
Digital transformation and Industry 4.0 pose challenges for all industries. Small and medium-sized enterprises (SMEs) are particularly affected due to cost pressure and the shortage of skilled workers. Adequate process models are needed to manage data analytics projects (DAP) efficiently and effectively in the face of a steadily growing amount of data. However, existing methodologies in the literature are not widely used in SMEs mainly because they are not addressing their specific needs. In this paper we present a Simplified Reference Model (SRM) for early-stage DAPs and compare it to the well-known Cross-Industry Standard Process for Data Mining (CRISP-DM). Three practical scenarios were used to evaluate the applicability of the SRM and identify weaknesses in the execution of DAPs in manufacturing SMEs. Based on our exploration, the main issues are data availability, insufficient data consistency, and inability to understand complex technical environments. Additionally, the paper highlights the need to develop SME-specific operational guidelines and identify potential barriers to the adaption of advanced technologies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fasel, D., Meier, A.: Was versteht man unter Big Data und NoSQL? In: Fasel, D., Meier, A. (eds.) Big Data. EH, pp. 3–16. Springer, Wiesbaden (2016). https://doi.org/10.1007/978-3-658-11589-0_1
Xu, L., He, W., Li, S.: Internet of things in industries: a survey. IEEE Trans. Industr. Inform. 10, 2233–2243 (2014). https://doi.org/10.1109/TII.2014.2300753
Huber, S., Seiger, R., Kühnert, A., Theodorou, V., Schlegel, T.: Goal-based semantic queries for dynamic processes in the Internet of Things. Int. J. Semant. Comput. 10, 269–293 (2016). https://doi.org/10.1142/S1793351X16400109
Seiter, M.: Business Analytics. Vahlen, München (2019). https://doi.org/10.15358/9783800658725
Gluchowski, P., Schieder, C., Chamoni, P.: Methoden des data mining für big data analytics. In: D’Onofrio, S., Meier, A. (eds.) Big Data Analytics. EH, pp. 25–48. Springer, Wiesbaden (2021). https://doi.org/10.1007/978-3-658-32236-6_2
Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, pp. 29–40 (2000)
Choudhary, A.K., Harding, J.A., Popplewell, K.: Knowledge discovery for moderating collaborative projects. In: 2006 4th IEEE International Conference on Industrial Informatics, pp. 519–524. IEEE Computer Society, Singapore (2006). https://doi.org/10.1109/INDIN.2006.275610
Masood, T., Sonntag, P.: Industry 4.0: adoption challenges and benefits for SMEs. Comput. Ind. 121 (2020). https://doi.org/10.1016/J.COMPIND.2020.103261
Otte, R., Wippermann, B., Otte, V.: Das theoretische und mathematische Konzept der technischen Datenauswertung. In: Otte, W. et al. (ed.) Von Data Mining bis Big Data, pp. 33–190. Carl Hanser Verlag GmbH & Co. KG, München (2020). https://doi.org/10.3139/9783446457171.003
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). https://doi.org/10.1017/S0269888910000032
Atzmüller, M., et al.: Implementierung und Betrieb von Big-Data-Anwendungen in der produzierenden Industrie (2022). https://www.vdi.de/richtlinien/details/vdivde-3714-blatt-1-implementierung-und-betrieb-von-big-data-anwendungen-in-der-produzierenden-industrie-durchfuehrung-von-big-data-projekten
Saltz, J.: CRISP-DM is Still the Most Popular Framework for Executing Data Science Projects - Data Science Process Alliance. https://www.datascience-pm.com/crisp-dm-still-most-popular/. Accessed 17 Feb 2023
Piatetsky, G.: CRISP-DM, still the top methodology for analytics, data mining, or data science projects – Kdnuggets. https://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html. Accessed 23 Feb 2023
Schröer, C., Kruse, F., Gómez, J.M.: A systematic literature review on applying CRISP-DM process model. Procedia Comput. Sci. 181, 526–534 (2021). https://doi.org/10.1016/J.PROCS.2021.01.199
Chapman, P., et al.: CRISP-DM 1.0: Step-by-step data mining guide (2000). https://www.kde.cs.uni-kassel.de/wp-content/uploads/lehre/ws2016-17/kdd/files/CRISPWP-0800.pdf
Leineweber, S., Wienbruch, T., Kuhlenkötter, B.: Konzept zur Unterstützung der Digitalen Transformation von Kleinen und Mittelständischen Unternehmen. In: KMU 4.0 - Digitale Transformation in kleinen und mittelständischen Unternehmen, pp. 20–39. GITO Verlag (2018). https://doi.org/10.30844/WGAB_2018_02
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rösl, S., Auer, T., Schieder, C. (2023). Addressing the Data Challenge in Manufacturing SMEs: A Comparative Study of Data Analytics Applications with a Simplified Reference Model. In: Elstermann, M., Dittmar, A., Lederer, M. (eds) Subject-Oriented Business Process Management. Models for Designing Digital Transformations. S-BPM ONE 2023. Communications in Computer and Information Science, vol 1867. Springer, Cham. https://doi.org/10.1007/978-3-031-40213-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-40213-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40212-8
Online ISBN: 978-3-031-40213-5
eBook Packages: Computer ScienceComputer Science (R0)