Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-25T13:21:08.795Z Has data issue: false hasContentIssue false
  • English
  • Français

A conceptual MCDA-based framework for machine learning algorithm selection in the early phase of product development

Published online by Cambridge University Press:  16 May 2024

Sebastian Sonntag ,
Erik Pohl ,
Janosch Luttmer ,
Jutta Geldermann  and
Arun Nagarajah
Sebastian Sonntag*
Affiliation:
University of Duisburg-Essen, Germany
Erik Pohl
Affiliation:
University of Duisburg-Essen, Germany
Janosch Luttmer
Affiliation:
University of Duisburg-Essen, Germany
Jutta Geldermann
Affiliation:
University of Duisburg-Essen, Germany
Arun Nagarajah
Affiliation:
University of Duisburg-Essen, Germany
*
[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Despite the potential to enhance efficiency and improve quality, AI methods are not widely adopted in the context of product development due to the need for specialized applications. The necessary identification of a suitable machine learning (ML) algorithm requires expert knowledge, often lacking in companies. Therefore, a concept based on a multi-criteria decision analysis is applied, enabling the identification of a suitable ML algorithm for tasks in the early phase of product development. The application and resulting advantages of the concept are presented through a practical example.

Keywords

artificial intelligence (AI)machine learningproduct developmentdecision makingmulti-criteria decision aiding (MCDA)
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2257 - 2266
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Abo, M.E.M., Idris, N., Mahmud, R., Qazi, A. (2021), “A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection”, Sustainability, Vol. 13 No. 18, p. 10018. https://doi.org/10.3390/su131810018CrossRefGoogle Scholar
Belton, V. and Stewart, T.J. (2002), Multiple Criteria Decision Analysis: An Integrated Approach, Springer US, Boston, MA, s.l. https://doi.org/10.1007/978-1-4615-1495-4_11CrossRefGoogle Scholar
Bender, B. and Gericke, K. (Eds.) (2021), Pahl/Beitz Konstruktionslehre: Methoden und Anwendung erfolgreicher Produktentwicklung, 9th ed. 2021, Springer, Berlin. https://doi.org/10.1007/978-3-662-57303-7CrossRefGoogle Scholar
Blagec, K., Ott, S. (2022), “A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks”, Scientific Data, Vol. 9 No. 1, p. 322. https://doi.org/10.1038/s41597-022-01435-xCrossRefGoogle ScholarPubMed
Bramer, M. (2020), Principles of Data Mining, Undergraduate Topics in Computer Science, 4th ed. 2020, Springer London. https://doi.org/10.1007/978-1-4471-7493-6CrossRefGoogle Scholar
Brans, J.-P. and Smet, Y. de (2016), “PROMETHEE Methods”, in Greco, S., Ehrgott, M. (Eds.), Multiple criteria decision analysis: State of the art surveys, International Series in Operations Research & Management Science, Vol. 233, Springer, New York, pp. 187–219. https://doi.org/10.1007/978-1-4939-3094-4CrossRefGoogle Scholar
Camburn, B., Arlitt, R., Anderson, D., Sanaei, R., Raviselam, S., Jensen, D. and Wood, K.L. (2020), “Computer-aided mind map generation via crowdsourcing and machine learning”, Research in Engineering Design, Vol. 31 No. 4, pp. 383–409. https://doi.org/10.1007/s00163-020-00341-wCrossRefGoogle Scholar
Christensen, K., Nørskov, S., Frederiksen, L. and Scholderer, J. (2017), “In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining”, Creativity and Innovation Management, Vol. 26 No. 1, pp. 17–30. https://doi.org/10.1111/caim.12202CrossRefGoogle Scholar
Diemer, J., Elmer, S., Gaertler, M. and Gamer, T. (2020), KI in der Industrie 4.0: Orientierung, Anwendungsbeispiele, Handlungsempfehlungen, Bundesministerium für Wirtschaft und Energie (BMWi)Google Scholar
Ehrlenspiel, K., Kiewert, A., Lindemann, U. and Mörtl, M. (2014), Kostengünstig Entwickeln und Konstruieren: Kostenmanagement bei der integrierten Produktentwicklung, VDI-Buch, 7. Aufl. 2014, Springer Berlin Heidelberg, Berlin. https://doi.org/10.1007/978-3-642-41959-1CrossRefGoogle Scholar
Figueira, J.R. and Roy, B. (2002), “Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure”, European Journal of Operational Research, Vol. 139 No. 2, pp. 317–326. https://doi.org/10.1016/S0377-2217(01)00370-8CrossRefGoogle Scholar
Gerschütz, B., Goetz, S. and Wartzack, S. (2023), “AI4PD—Towards a Standardized Interconnection of Artificial Intelligence Methods with Product Development Processes”, Applied Sciences, Vol. 13 No. 5, p. 3002. https://doi.org/10.3390/app13053002CrossRefGoogle Scholar
Gerschütz, B., Schleich, B. and Wartzack, S. (2021), “A semantic web approach for structuring data-driven methods in the product development process”, DS 111: Proceedings of the 32nd Symposium Design for X (DFX2021), 2021, pp. 1–10. https://doi.org/10.35199/dfx2021.15CrossRefGoogle Scholar
Greco, S., Ishizaka, A., Tasiou, M. and Torrisi, G. (2019), “On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness”, Social Indicators Research, Vol. 141 No. 1, pp. 61–94. https://doi.org/10.1007/s11205-017-1832-9CrossRefGoogle Scholar
Jung, A. (2022), Machine Learning: The Basics, Machine Learning, 1st ed. 2022, Springer Singapore, Singapore. https://doi.org/10.1007/978-981-16-8193-6CrossRefGoogle Scholar
Kotsiantis, S.B., Zaharakis, I.D. and Pintelas, P.E. (2006), “Machine learning: a review of classification and combining techniques”, Artificial Intelligence Review, Vol. 26 No. 3, pp. 159–190. https://doi.org/10.1007/s10462-007-9052-3CrossRefGoogle Scholar
Krause, D. (2018), Methodische Entwicklung modularer Produktfamilien: Hohe Produktvielfalt beherrschbar entwickeln, Springer Berlin Heidelberg, Berlin. https://doi.org/10.1007/978-3-662-53040-5CrossRefGoogle Scholar
Licen, S., Astel, A. and Tsakovski, S. (2023), “Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review”, The Science of the total environment, Vol. 878, p. 163084. https://doi.org/10.1016/j.scitotenv.2023.163084CrossRefGoogle ScholarPubMed
Lickert, H., Bilge, P. (2021), “Selection of Suitable Machine Learning Algorithms for Classification Tasks in Reverse Logistics”, Procedia CIRP, Vol. 96, pp. 272–277. https://doi.org/10.1016/j.procir.2021.01.086CrossRefGoogle Scholar
Luttmer, J., Prihodko, V., Ehring, D. and Nagarajah, A. (2023), “Requirements extraction from engineering standards – systematic evaluation of extraction techniques”, Procedia CIRP, Vol. 119, pp. 794–799. https://doi.org/10.1016/j.procir.2023.03.125CrossRefGoogle Scholar
Mareschal, B. (1988), “Weight stability intervals in multicriteria decision aid”, European Journal of Operational Research, Vol. 33 No. 1, pp. 54–64. https://doi.org/10.1016/0377-2217(88)90254-8CrossRefGoogle Scholar
Pohl, E. and Geldermann, J. (n.d.), “Supporting environmental decisions using PROMETHEE-Cloud: A web app to support multi-criteria decisions”, Submitted to Journal of Environmental Modelling & Software.Google Scholar
Reder, B. (2021), IDG Studie Machine Learning 2021. [online] Available at: https://www.lufthansa-industry-solutions.com/de-de/studien/idg-studie-machine-learning-2021 (accessed 14.11.2023).Google Scholar
Riesener, M., Doelle, C. (2020), “Identification of evaluation criteria for algorithms used within the context of product development”, Procedia CIRP, Vol. 91, pp. 508–515. https://doi.org/10.1016/j.procir.2020.02.207CrossRefGoogle Scholar
Sarang, P. (2023), Thinking Data Science: A Data Science Practitioner's Guide, The Springer Series in Applied Machine Learning, Springer International Publishing. https://doi.org/10.1007/978-3-031-02363-7CrossRefGoogle Scholar
Schmid, T., Hildesheim, W., Holoyad, T. (2021), “The AI Methods, Capabilities and Criticality Grid”, KI - Künstliche Intelligenz, Vol. 35 No. 3-4, pp. 425–440. https://doi.org/10.1007/s13218-021-00736-4CrossRefGoogle Scholar
Sonntag, S., Luttmer, J., Pluhnau, R., & Nagarajah, A. (2023). A PATTERN LANGUAGE APPROACH TO IDENTIFY APPROPRIATE MACHINE LEARNING ALGORITHMS IN THE CONTEXT OF PRODUCT DEVELOPMENT. Proceedings of the Design Society, 3, pp. 365-374. https://doi.org/10.1017/pds.2023.37CrossRefGoogle Scholar
Waring, J., Lindvall, C. and Umeton, R. (2020), “Automated machine learning: Review of the state-of-the-art and opportunities for healthcare”, Artificial Intelligence in Medicine, Vol. 104, p. 101822. https://doi.org/10.1016/j.artmed.2020.101822CrossRefGoogle ScholarPubMed
Zhang, D., Maslej, N., Brynjolfsson, E., Etchemendy, J., Lyons, T., Manyika, J. (2022), The AI Index 2022 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA.Google Scholar