Using the “Voice of the Customer” to Establish Product Specifications

World Café method integrated with QFD for obtaining the Voice of the Customer

Production ◽

10.1590/0103-6513.20170097 ◽

2018 ◽

Vol 28 (0) ◽

Cited By ~ 1

Author(s):

Jair Gustavo de Mello Torres ◽

Pedro Luiz de Oliveira Costa Neto

Keyword(s):

World Cafe ◽

The Voice ◽

Voice Of The Customer

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Voice of the Customer Oriented New Product Synthesis Over Knowledge Graphs

Volume 1A: 38th Computers and Information in Engineering Conference ◽

10.1115/detc2018-85909 ◽

2018 ◽

Author(s):

Feiwei Qin ◽

Hairui Xu ◽

Weicheng Zhang ◽

Lin Yuan ◽

Ming Li ◽

...

Keyword(s):

Language Processing ◽

Online Shopping ◽

Specific Aspect ◽

Product Reviews ◽

Product Knowledge ◽

Online Data ◽

Challenges And Opportunities ◽

Knowledge Graphs ◽

The Voice ◽

Voice Of The Customer

The online shopping has been much easier and popular, and meanwhile brings new challenges and opportunities to the field of product design and marketing sale. On one hand, product manufacturers find it challenging to produce new popularly accepted products to meet the customers’ needs; on the other hand, end customers usually feel it difficult to buy ideal goods that they really want, even if navigating a huge amount of commodities. There are indeed a ‘communication gap’ between the customers and manufacturers. As an effort to partially resolve the issue, this paper proposes a novel product synthesis approach from ‘voice of the customer’ over product knowledge graphs. Here the voice of customers mainly refer to the buyers’ product reviews from online shopping platforms or blogs, while the product knowledge graph is constructed containing professional hierarchical product knowledge on its properties based on ontological models. Using the technologies of natural language processing, we first extract the customs’ polarities on each specific aspect of a product, which are then transited to design requirements on the product’s design components. Based on the requirement extractions, and the pre-built product knowledge, semantic web and reasoning techniques are utilized to synthesize a novel product that meets more customer needs. Typical case studies on mobile phones from raw online data demonstrate the proposed approach’s performance.

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QFD: Echoing the Voice of the Customer

AT&T Technical Journal ◽

10.1002/j.1538-7305.1991.tb00342.x ◽

1991 ◽

Vol 70 (2) ◽

pp. 18-32 ◽

Cited By ~ 39

Author(s):

Patrick G. Brown

Keyword(s):

The Voice ◽

Voice Of The Customer

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

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-11-2016-0205 ◽

2018 ◽

Vol 35 (3) ◽

pp. 762-778 ◽

Cited By ~ 2

Author(s):

Katerina Gotzamani ◽

Andreas Georgiou ◽

Andreas Andronikidis ◽

Konstantina Kamvysi

Keyword(s):

Quality Function Deployment ◽

Fuzzy Ahp ◽

Markov Modeling ◽

Design Decisions ◽

Content Type ◽

Customer Preferences ◽

First Time ◽

The Voice ◽

Product Specifications

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.

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