Development Of A Virtual Experiment To Validate The Relationship Between Engineering Changes And Learning Curves Using Design Structure Matrices

DS 126: Proceedings of the 25th International DSM Conference (DSM 2023), Gothenburg, Sweden, October, 03 - 05, 2023

Year: 2023
Editor: Harold (Mike) Stowe; Tyson R. Browning; Steven D. Eppinger; Jakob Trauer; Christopher Langner; Matthias Kreimeyer; Ola Isaksson; Massimo Panarotto; Arindam Brahma
Author: Cas M.G. Verstappen, Alex Alblas, Pascal Etman
Series: DSM
Institution: Eindhoven University of Technology, The Netherlands
Page(s): 077-086
DOI number: 10.35199/dsm2023.09


The increased demand for high-performing engineered products pushes manufacturing firms to introduce novel and more complex products at an ever-faster pace. This invokes major engineering changes in these firms. The resulting disruptions to manufacturing performance and efficiency losses are poorly understood. This paper proposes a virtual experiment that validates the relationship between engineering changes and the manufacturing cycle time learning curve. Product complexity and product commonality are identified as parameters of engineering changes that affect the learning curve. These parameters are quantified using design structure matrix methods. The effects of product complexity and commonality on the manufacturing learning curve are tested using an experiment. The results of this study can be used by management professionals to mitigate risks and improve resource allocation after engineering changes.

Keywords: learning curve, product complexity, design structure matrix, product commonality, manufacturing cycle time


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