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Concept for enhanced intuition in development management through exploratory data analysis using an extended factor analysis of mixed data

Published online by Cambridge University Press:  16 May 2024

Michael Riesener,
Maximilian Kuhn,
Benjamin Nils Johannes Lender and
Günther Schuh
Michael Riesener
Affiliation:
RWTH Aachen University, Germany
Maximilian Kuhn
Affiliation:
RWTH Aachen University, Germany
Benjamin Nils Johannes Lender*
Affiliation:
RWTH Aachen University, Germany
Günther Schuh
Affiliation:
RWTH Aachen University, Germany
*
b.lender@wzl.rwth-aachen.de

Abstract

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With the shift from mechanical value delivery to mechatronic value delivery, development environments are becoming more complex. Intuitive decision-making in development management is becoming increasingly challenging. Meanwhile, the use project management software is spreading, bringing about a new level of project data for development projects, holding to potential to enhance human decision making. To this end, the paper presents an extension to factor analysis of mixed data, which can facilitate usage of exploratory data analysis to improve decision-making in development project planning.

Keywords

factor analysis of mixed data (FAMD)decision makingdevelopment managementexploratory data analysisproject management

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024, pp. 2227 - 2236
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

Bender, B. and Gericke, K. (Eds.) (2021), Pahl/Beitz Konstruktionslehre, Springer Berlin Heidelberg, Berlin, Heidelberg.CrossRefGoogle Scholar
Cowan, N. (2001), “The magical number 4 in short-term memory. a reconsideration of mental storage capacity”, The Behavioral and brain sciences, Vol. 24 No. 1, 87–114; discussion 114-85. https://dx.doi.org/10.1017/s0140525x01003922.CrossRefGoogle ScholarPubMed
Davidow, M. and Matteson, D.S. (2020), Factor Analysis of Mixed Data for Anomaly Detection, available at: http://arxiv.org/pdf/2005.12129v1.Google Scholar
Domschke, W., Drexl, A., Klein, R. and Scholl, A. (2015), Einführung in Operations Research, 9. Auflage, Springer Berlin Heidelberg, Berlin, Heidelberg.CrossRefGoogle Scholar
Dunteman, G.H. (1989), Principal components analysis, Sage University papers Quantitative applications in the social sciences, Vol. 69, SAGE Publications, Newbury Park, Calif. https://dx.doi.org/10.4135/9781412985475.Google Scholar
Escofier, B. (1979), “Traitement simultané de variables qualitatives et quantitatives en analyse factorielle”, Les cahiers de l'analyse des données, Vol. 4 No. 2, pp. 137–146.Google Scholar
Fricke, G. and Lohse, G. (1997), Entwicklungsmanagement, Springer Berlin Heidelberg, Berlin, Heidelberg. https://dx.doi.org/10.1007/978-3-642-59075-7.CrossRefGoogle Scholar
Gower, J.C. (2011), “Multiple Correspondence Analysis”, in Gower, J.C., Le Roux, N. and Lubbe, S. (Eds.), Understanding biplots, Wiley-Blackwell, Oxford, pp. 365–403. https://dx.doi.org/10.1002/9780470973196.ch8.CrossRefGoogle Scholar
Greenacre, M.J. (1991), “Interpreting multiple correspondence analysis”, Applied Stochastic Models and Data Analysis, Vol. 7 No. 2, pp. 195–210. https://dx.doi.org/10.1002/asm.3150070208.CrossRefGoogle Scholar
Greenacre, M.J. (2006), Multiple Correspondence Analysis and Related Methods, Chapman and Hall/CRC Statistics in the Social and Behavioral Sciences Ser, 1. Auflage, CRC Press LLC, London.Google Scholar
Hahn, A., Häusler, S. and große Austing, S. (2013), Quantitatives Entwicklungsmanagement, Springer Berlin Heidelberg, Berlin, Heidelberg. https://dx.doi.org/10.1007/978-3-642-34510-4.CrossRefGoogle Scholar
Han, L., Shen, P., Yan, J., Huang, Y., Ba, X., Lin, W., Wang, H., Huang, Y., Qin, K., Wang, Y., Chen, Z. and Tu, S. (2021), “Exploring the Clinical Characteristics of COVID-19 Clusters Identified Using Factor Analysis of Mixed Data-Based Cluster Analysis”, Frontiers in medicine, Vol. 8. https://dx.doi.org/10.3389/fmed.2021.644724.CrossRefGoogle ScholarPubMed
Hodgkinson, G.P., Langan-Fox, J. and Sadler-Smith, E. (2008), “Intuition: a fundamental bridging construct in the behavioural sciences”, British journal of psychology (London, England 1953), Vol. 99 No. 1, pp. 1–27. https://dx.doi.org/10.1348/000712607X216666.Google ScholarPubMed
Holzbaur, U. (2007), Entwicklungsmanagement: Mit hervorragenden Produkten zum Markterfolg, Springer Berlin Heidelberg, Berlin, Heidelberg.CrossRefGoogle Scholar
Kramer, O. (2013), Dimensionality Reduction with Unsupervised Nearest Neighbors, Intelligent Systems Reference Library, v.51, 1. Auflage, Springer Berlin Heidelberg, Berlin, Heidelberg.CrossRefGoogle Scholar
Krzywinski, J. and Wölfel, C. (2021), “Industriedesign und nutzerzentrierte Produktentwicklung”, in Bender, B. and Gericke, K. (Eds.), Pahl/Beitz Konstruktionslehre, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 684–703.Google Scholar
Lauth, E. and Scholz, S.G. (2023), “Introduction and Evaluation of a Project Management Software Tool in the Context of the Administration of Science and Research Projects”, in Scholz, S.G., Howlett, R.J. and Setchi, R. (Eds.), Sustainable Design and Manufacturing, Smart Innovation, Systems and Technologies, Vol. 338, Springer Nature Singapore, Singapore, pp. 11–20. https://dx.doi.org/10.1007/978-981-19-9205-6_2.CrossRefGoogle Scholar
Menger, K. (1932), “Das Botenproblem”, Ergebnisse eines Mathematischen Kolloquiums, No. 2, pp. 11–12.Google Scholar
Michailidis, G. (2007), “Principle Component Analysis”, in Salkind, N.J. and Rasmussen, K. (Eds.), Encyclopedia of measurement and statistics, SAGE Publications, Thousand Oaks, Calif., pp. 783–786.Google Scholar
Oluyisola, O.E., Bhalla, S., Sgarbossa, F. and Strandhagen, J.O. (2022), “Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study”, Journal of Intelligent Manufacturing, Vol. 33 No. 1, pp. 311–332. https://dx.doi.org/10.1007/s10845-021-01808-w.CrossRefGoogle Scholar
Ophey, L. (2006), Entwicklungsmanagement, VDI-Buch, Springer, Dordrecht.Google Scholar
Pagès, J. (2004), “Analyse factorielle de données mixtes”, Revue de statistique appliquée, Vol. 52 No. 4, pp. 93–111.Google Scholar
Pagès, J. (2015), Multiple factor analysis by example using R, Chapman & Hall/CRC the R series, CRC Press, Boca Raton, FL. https://dx.doi.org/10.1201/b17700.Google Scholar
Pearson, K. (1901), “LIII. On lines and planes of closest fit to systems of points in space”, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Vol. 2 No. 11, pp. 559–572. https://dx.doi.org/10.1080/14786440109462720.CrossRefGoogle Scholar
Riesener, M., Dölle, C., Keuper, A. and Schuh, G. (2021a), “Understand and Control Complexity in Cyber-Physical Systems by Analyzing Complexity Drivers”, in 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS): Online, 10-13 May, 2021, 5/10/2021 - 5/12/2021, Victoria, BC, Canada, IEEE, Piscataway, NJ, pp. 255–260. https://dx.doi.org/10.1109/ICPS49255.2021.9468255.CrossRefGoogle Scholar
Riesener, M., Dölle, C., Perau, S., Lossie, P. and Schuh, G. (2021b), “Methodology for iterative system modeling in agile product development”, Procedia CIRP, Vol. 100, pp. 439–444. https://dx.doi.org/10.1016/j.procir.2021.05.101.CrossRefGoogle Scholar
Riesener, M., Kuhn, M., Keuper, A., Lender, B. and Schuh, G. (2022a), “Framework for FAMD-Based Identification of RCPSP-Constraints for Improved Project Scheduling”, Proceedings of the Design Society, Vol. 2, pp. 253–262. https://dx.doi.org/10.1017/pds.2022.27.CrossRefGoogle Scholar
Riesener, M., Kuhn, M., Lender, B. and Schuh, G. (2022b), “Concept for Databased Identification of Heuristics for Development Management using FAMD”, in IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 07.-10.12., Kuala Lumpur, Malaysia, IEEE, pp. 1281–1285. https://dx.doi.org/10.1109/IEEM55944.2022.9989972.CrossRefGoogle Scholar
Rowan, R. (1986), The intuitive manager, 1. Auflage, Little Brown, Boston, MA.Google Scholar
Sabadka, D., Molnár, V. and Fedorko, G. (2019), “Shortening of Life Cycle and Complexity Impact on the Automotive Industry”, TEM Journal, Vol. 8 No. 4, pp. 1295–1301. https://dx.doi.org/10.18421/TEM84-27.CrossRefGoogle Scholar
Saporta, G. (1990), “Simultaneous Analysis of Qualitative and Quantitative Data”, Atti della XXXV riunione scientifica; società italiana di Statistica, pp. 63–72.Google Scholar
Schuh, G. and Dölle, C. (2021), Sustainable Innovation, Springer Berlin Heidelberg, Berlin, Heidelberg. https://dx.doi.org/10.1007/978-3-662-61910-0.CrossRefGoogle Scholar
Schuh, G., Dölle, C., Breunig, S. and Becker, A. (2019), “Gestaltungsmodell zur Steigerung der Wirtschaftlichkeit von Baukästen mechatronischer Produkte”, Zeitschrift für wirtschaftlichen Fabrikbetrieb, Vol. 114 No. 6, pp. 367–371. https://dx.doi.org/10.3139/104.112095.CrossRefGoogle Scholar
Shirley, D.A. and Langan-Fox, J. (1996), “Intuition: A Review of the Literature”, Psychological Reports, Vol. 79 No. 2, pp. 563–584. https://dx.doi.org/10.2466/pr0.1996.79.2.563.CrossRefGoogle Scholar
Wedel, M. and Kamakura, W.A. (2001), “Factor analysis with (mixed) observed and latent variables in the exponential family”, Psychometrika, Vol. 66 No. 4, pp. 515–530. https://dx.doi.org/10.1007/BF02296193.CrossRefGoogle Scholar