A systematic methodology to deal with the dynamics of customer needs in Quality Function Deployment
Research highlights
► When the cost of not producing a product that the customer wants is tremendously large, it is reasonable to make extra efforts to monitor and follow the customer preference change over time. ► A timely update of customer needs information provides a company a better ground to formulate strategies to meet the future needs of its customer. ► How precisely one can model or learn from the past data may critically determine how precisely one may estimate or understand the future.
Introduction
Quality Function Deployment (QFD) has been widely known to be one of the most useful tools in customer-driven products or services development (Bergman and Klefsjö, 2003, Chan and Wu, 2002, Raharjo et al., 2008, Xie et al., 2003). It has been applied successfully in various fields. Some recent examples of its applications are in ERP system selection (Karsak & Özogul, 2009), shipping investment decision making (Celik, Cebi, Kahraman, & Er, 2009), semiconductor system-on-a-chip product design planning (Hung, Kao, & Juang, 2008), and processes selection (Chakraborty and Dey, 2007, Nagahanumaiah et al., 2008).
The most prominent strength of QFD is the focus on customer needs and the coherent translation of those needs into each phase of product development process. Since it is used primarily in early stage of product or service development process, it therefore involves quite a lot of subjectivity and uncertainty (Raharjo et al., 2008). With respect to the uncertainty involved, Kim, Kim, and Min (2007) suggested a very useful classification. They divided the source of the uncertainty into four types, namely, fuzziness, incompleteness heterogeneity, and fluctuation. Some most recent examples of research of each type are as follows, ‘fuzziness’ (Liu, 2009, Wang, 2009, Zhang and Chu, 2009), ‘incompleteness’ (Chin et al., 2009, Han et al., 2004), ‘heterogeneity’ (Kim and Kim, 2009, Kim et al., 2007), and ‘fluctuation’ (Min and Kim, 2008, Raharjo et al., 2006, Wu et al., 2005, Wu and Shieh, 2006).
This paper will focus on dealing with the last type of uncertainty, that is, ‘fluctuation’ in the customer needs over time. Almost all previous research which deal with such uncertainty have not adequately addressed the issue of how to estimate and manage future uncertainty of customer needs in QFD decision making analysis. Raharjo et al. (2006) briefly mentioned the use of interval estimate, as opposed to a point estimate, as a better measure for future customer needs in QFD. In line with their work, this paper will elaborate more extensively on how one may, in a more systematic fashion, estimate the future uncertainty and eventually use an optimization technique with respect to it.
The aim of the paper, in general, is to propose a novel systematic methodology to deal with the customer needs’ dynamics in QFD. The term ‘dynamics’ here is interpreted as the change of customer needs’ relative weights over time. Specifically, it will extend the existing research in three directions. First, it proposes the use of a newly developed short-term forecasting technique (Raharjo, Xie, & Brombacher, 2009) which is effective to model the dynamics of Analytic Hierarchy Process (AHP) based importance rating. This is owing to the fact that the AHP has been applied very extensively in the QFD (Ho, 2008), and there is almost no tool to model the dynamics. Second, it describes more comprehensively on how future uncertainty in the weights of customer needs may be estimated and transmitted to the design attributes. Third, it proposes the use a quantitative approach that takes into account the decision maker’s attitude towards risk to optimize the QFD decision making analysis.
The paper is organized as follows. In the next section (Section 2), the notion of dynamic QFD (DQFD) will be described in terms of its significance, model, and tools used. Section 3 will elaborate the proposed systematic methodology to deal with the customer needs’ dynamics along with their future uncertainty. An example based on a real-world application of QFD (Raharjo, Xie, Goh, & Brombacher, 2007) will be provided to illustrate how the proposed methodology works in practice (Section 4). Section 5 will discuss the issue of forecasting technique’s selection and a possible implication of the methodology to development of innovative products. Finally, a summary of the main contribution and possible future works are provided in Section 6.
List of acronyms
Section snippets
The dynamic QFD
This section describes the notion of dynamic QFD (DQFD) that can be considered as an extension of the standard QFD (Cohen, 1995) since it takes into account the change over time. In this paper, the emphasis is placed on the need to deal with the dynamics in the relative weights of customer needs. Those weights are commonly referred to as ‘importance rating’ in the House of Quality (HoQ). The following subsections will first explain why it is important to consider such change. Afterwards, how to
The proposed methodology
This section consists of two subsections. Section 3.1 describes a systematic step-by-step procedure of using the proposed approaches described in Section 2, namely, the forecasting technique (Raharjo et al., 2009), the estimation of future uncertainty, and the stochastic dominance approach. Section 3.2 describes a simple optimization model, which is based on Xie et al. (2003), to be used with the proposed methodology. It is certainly possible to use some other alternative optimization models
An example
This section provides an example of how one may apply the proposed methodology by following the step-by-step procedure described in Section 3. The example is based on a real-world case study of a QFD application in improving education quality in a university (Raharjo et al., 2007). The customer for the QFD application is divided into internal customer (lecturers and students) and external customer (employers of graduates). For simplicity, only the HoQ, which was built for the employers of
Selection of forecasting technique
There are at least two reasons why the CDES method (Raharjo et al., 2009) is selected. First, it is suitable for the situation when there is only a minimal number of historical data. Second, it is relatively simple and time-efficient compared to other time series methods, especially for modeling the dynamics of AHP-based priorities. It is possible that the CDES method may end up with errors which do not follow Gaussian white noise process. In such case, other forecasting techniques, such as
Conclusions
The aim of this paper was to propose a novel systematic methodology to deal with the customer needs’ dynamics in QFD. Specifically, the contribution of this work can be summarized into three points. First, it has provided a way to model the AHP-based priorities’ change over time in the QFD. This is owing to the fact that the AHP has been applied very extensively in the QFD (Carnevalli and Miguel, 2008, Ho, 2008), and yet there is almost no tool to model the dynamics. Second, it has elaborated
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