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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
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.

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