Quantifying the Impact of Product Changes on Manufacturing Performance
Year: 2022
Editor: Harold (Mike) Stowe; Tyson R. Browning; Steven D. Eppinger; Jakob Trauer; Pascal Etman, Sjoerd Knippenberg
Author: Dooper, Tjomme (1); Etman, Pascal, L. F. P. (2); Alblas, Alex, A. (2)
Series: DSM
Institution: 1: FruitPunch AI; 2: Eindhoven University of Technology
Page(s): 38-47
DOI number: 10.35199/dsm2022.05
Abstract
Every adjustment to a physical product disrupts the manufacturing organization, requiring adaptation in tools and processes. The resulting disruption to manufacturing performance is poorly understood. We use design structure matrices and a complexity metric to quantify the complexity and change of product architecture in an explorative, small-scale experiment. Based on the results we develop two propositions to guide further research into the factors that affect the shape of consecutive learning curves upon product changes. The first proposition is that after product change, the complexity of the novel part of product architecture is responsible for the initial decrease in manufacturing performance. Second, we propose that the asymptote of a learning curve and the complexity of a product’s architecture are inversely related.
Keywords: design structure matrix, complexity, engineering change, organizational learning, learning curve