Linking design process to customer satisfaction through virtual design of experiments

Abstract

This work seeks to better understand how design processes affect design outcomes. Design process data were collected from journals kept as a part of mechanical engineering capstone design projects at Montana State University. Student processes were characterized by time coding journal entries using a 3 × 4 matrix of process variables. The data were modeled using a principal components artificial neural network, and the model used in a virtual designed experiment to obtain estimates for design process factors that significantly affect client satisfaction. Results indicate that greater client satisfaction is achieved through: greater problem definition (PD) activity and idea generation at conceptual design levels, and PD and engineering analysis activities at the system design level. Whereas, design activity at the detailed level associates with lower client satisfaction. These results support some aspects of existing models of “good” design process, and suggest adaptations of the models for novice designers.