Introducing multivariate Markov modeling within QFD to anticipate future customer preferences in product design

Abstract

Purpose The purpose of this paper is to provide an enhanced version of quality function deployment (QFD) that captures customers’ present and future preferences, accurately prioritizes product specifications and eventually translates them into desirable quality products. Under rapidly changing environments, customer requirements and preferences are constantly changing and evolving, rendering essential the realization of the dynamic role of the “Voice of the Customer (VoC)” in the design and development of products.

Design/methodology/approach The proposed methodological framework incorporates a Multivariate Markov Chain (MMC) model to describe the pattern of changes in customer preferences over time, the Fuzzy AHP method to accommodate the uncertainty and subjectivity of the “VoC” and the LP-GW-AHP to discover the most important product specifications in order to structure a robust QFD method. This enhanced QFD framework (MMC-QFD-LP-GW-Fuzzy AHP) takes into consideration the dynamic nature of the “VoC” captures the actual customers’ preferences (WHATs) and interprets them into design decisions (HOWs).

Findings The integration of MMC models into the QFD helps to handle the sequences of customers’ preferences as categorical data sequences and to consider the multiple interdependencies among them.

Originality/value In this study, a MMC model is introduced for the first time within QFD, in an effort to extend the concept of listening to further anticipating to customer wants. Gaining a deeper understanding of current and future customers’ preferences could help organizations to design products and plan strategies that more effectively and efficiently satisfy them.