Estimating good design attributes from generalized drivers to provide early assistance to design requirements analysis

Lei Zhao (Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada)
Theodor Freiheit (Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada)

Journal of Engineering, Design and Technology

ISSN: 1726-0531

Publication date: 5 June 2017

Abstract

Purpose

This paper aims to examine the perceptions of good design attributes and propose a model to estimate their relative importance through fundamental drivers. Design activities must understand and meet customer and producer expectations and deliver products in a profitable manner. Requirements analysis is conducted to understand customer expectations, but in new product development, this information can be available too late in the development cycle. Moreover, customer needs are often unclear to designers at early stages of design, with customers often unable to articulate their requirements or unaware of how a new product may solve problems or create complications. Evaluating non-product-specific drivers to generalized good product design attributes can help designers estimate important factors in early requirements analysis.

Design/methodology/approach

Quantification of the weight designers place in their mental models of what makes up a good product is determined from linear regression modeling, providing a more concrete evaluation of inherently subjective perceptions. A survey is deployed using Mechanical TurkTM to collect perceptions of good product attributes and drivers through product case studies. Data are analyzed using a utility theory framework and importance of attributes is estimated from the importance of drivers.

Findings

A generalized model that estimates good design attributes from drivers is presented. This study also demonstrates that non-product-specific attribute importance can be extracted from specific product cases. An application example demonstrating the relative importance of good design attributes is given for different types of watches.

Research limitations/implications

The approach is intended to supplement ordinary product design and development processes, and is not intended to replace market research and concept testing activities. Model coefficient weights are dependent on the quality of the data that was collected, which has limitations. While the current study included confounding variables, introducing interactions into the model could make attribute importance prediction more accurate.

Practical implications

While design requirements analysis is now central to modern design practice, these estimates can be available too late in the development cycle, especially when customers have no experience with the product type. The developed model quantifies design attributes that consumers, manufacturers and society as a whole use to distinguish if a product will be considered well designed. Product designers can better focus their development resources toward good design attributes based on guidance generated from generalized drivers.

Originality/value

Historically, requirements analysis is undertaken specific to the product being designed. This paper provides a model to give designers early guidance in a non-product-specific framework. The framework also considers good design attributes as holistic, including societal and producer concerns. Although all of the proposed good design attributes can be associated with a well-engineered product, it is unnecessary to design a product that performs exceptionally on every attribute. This model provides identification of the handful of attributes that can make the most significant difference for design success.

Keywords

Citation

Zhao, L. and Freiheit, T. (2017), "Estimating good design attributes from generalized drivers to provide early assistance to design requirements analysis", Journal of Engineering, Design and Technology, Vol. 15 No. 03, pp. 317-340. https://doi.org/10.1108/JEDT-06-2016-0033

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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