A comparison of quality function deployment and conjoint analysis in new product design

Abstract

In this work, we compare two product design approaches, quality function deployment (QFD) and conjoint analysis, by applying each to the design of a new all-purpose climbing harness for the beginning/intermediate ability climber that would complement a leading manufacturer's existing product line. While many of the optimal design features were the same under both approaches, the differences allow us to highlight the strengths of each approach. With conjoint analysis, it was easier to compare the most preferred features (i.e., ones that maximized sales) to profit maximizing features and also to develop designs that optimize product line sales or profits. On the other hand, QFD was able to highlight the fact that certain engineering characteristics or design features had both positive and negative aspects. This tradeoff could point the way to “out of the box” solutions. QFD also highlighted the importance of starting explicitly with customer needs, regardless of which method is used. Rather than competing, we view them as complementary approaches that should be conducted simultaneously; each providing feedback to the other. When the two approaches differed on the optimal level or importance of a feature, it appeared that conjoint analysis better captured customers' current preferences for product features while QFD captured what product developers thought would best satisfy customer needs. Looking at the problem through these different lenses provides a useful dialogue that should not be missed. QFD's ability to generate creative or novel solutions should be combined with conjoint analysis' ability to forecast market reaction to design changes. © 2002 Elsevier Science Inc. All rights reserved.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 143 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.