Integrating conjoint analysis with quality function deployment to carry out customer-driven concept development for ultrabooks
Introduction
In an era of global customization, owing to dynamically changing customer desires coupled with rapid advances in manufacturing technologies, today's marketplaces are full of various product offerings. Back to 2011, Ultrabooks are designed to feature reduced size (less than 2.1 cm thick) and weight (less than 1.5 kg) without compromising system performance and battery life. Thus, low-power Intel processors with integrated graphics and unibody chassis are used to fit larger batteries into smaller cases. Different from past products like netbooks and notebooks, ultrabooks would be very thin, quite slight, and could also accommodate tablet features such as a touch screen and long battery life [29], [30]. Obviously, the Ultrabook directly competes against Apple's MacBook Air, which has similar product specifications, but runs the kernels of Apple OS (and is capable of running Microsoft Windows). In order to avoid fatal mistakes before implementing practical product strategies, companies need to deliberately understand what customers want and desire for capturing customer preferences or customer perceptions.
In practice, new product development defined as a process of transforming an identified market opportunity into profitable product(s) for sale [4], [26], usually consists of a sequence of steps in which an enterprise could employ it to accomplish the goal of commercialization. Typically, the NPD process consists of the following six phases, such as initial planning, concept development, system-level design, detail design, testing and refinement, and production ramp-up [24]. Among them, the phase of concept development is of critical importance because it does not only impact on downstream activities of the whole process, but also influence NPD's overall success, significantly. In particular, the process of concept development includes a couple of representative activities: (1) identifying customer needs, (2) concept generation, (3) concept selection, (4) cost analysis, (5) prototype testing, and (6) benchmarking analysis [14], [15]. In this paper, we particularly focus on two critical activities, namely, concept generation and concept selection. Needless to say, product development without incorporating customer involvement into the process of concept development is doomed to failure since huge gaps might exist between perceived customer requirements (CRs) and configured functional attributes (FAs).
Specifically, five main schemes are commonly adopted for concept evaluation, including utility theory, analytical hierarchy/network process (AHP/ANP), graphical methods, fuzzy logic approaches, and QFD matrices [2], [3]. Apparently, most of the above methods are fully reliant on subjective human assessment or experts' domain knowledge. For instance, a pairwise comparison among two alternatives is often applied to a respondent by asking the following question: How much degree is concept A preferred to concept B with respect to a specific dimension? Apparently, due to lack of concrete product features, the AHP [21] seems to be quite ambiguous in practice. Suppose there are n criteria at a hierarchy, we need to complete times of pairwise comparisons for deriving their importance [20]. Obviously, when the number of criteria or competitive alternatives is over seven, its feasibility is highly doubtful for respondents to reach a consistent result. Thus, instead of using the AHP/ANP based schemes, this study integrates the conventional QFD with conjoint analysis (CA) to incorporate customer preference and utility into the decision-making process of concept development. In addition, artificial neural networks (ANNs) and clustering techniques have been sorely utilized or fused together to help firms achieve product conceptualization, product definition and product customization [7], [28].
For clarity, Table 1 briefly compares our proposed approach with other past studies. Unfortunately, most of the previous studies rarely explore the impacts of FAs on CRs and hence it is quite challenging for them to assess product alternatives in a customer-driven way. Followed by [5], [10], [14], [15], [22], [23], [25], a market-oriented approach is presented and several crucial issues are addressed below:
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Based on the QFD platform, product features are characterized by perceived customer requirements (CRs) and configurable functional attributes (FAs) and a systematic approach is offered to identify the causal impacts of FAs on CRs,
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With respect to distinct segments, CA is employed to extract customer utilities of FAs for generating design concepts in a customer-driven way,
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With consideration of manufacturing costs, prototype alternatives are prioritized in terms of market-oriented CRs for offering managerial implications.
In particular, two fundamental design phases are emphasized in this study: phase 1 for concept generation and phase 2 for prototype evaluation. The rest of this paper is structured as follows. Section 2 briefly overviews conjoint analysis and quality function deployment. Section 3 presents the proposed framework. An industrial example regarding configuring varieties of ultrabooks is illustrated in Section 4. Concluding remarks are finally drawn in Section 5.
Section snippets
Overview of quality function deployment and conjoint analysis
In response to customer desire and much shorter product life cycle than ever, launching attractive products faster than competitors can assist firms in not only acquiring larger market share but also reducing development lead time, significantly. In practice, however, manufacturing companies are often struggling with the dilemma of increasing product variety or controlling manufacturing complexity [15], [25]. In other words, to survive in a wide range of market segments, companies are now more
Proposed techniques
Referring to Fig. 3, several techniques including CA (conjoint analysis), DEMATEL (decision making and trial laboratory) and TOPSIS (technique for order preference by similarity to ideal solution) are well fused into the QFD to perform market-oriented concept generation and prototype evaluation. For convenience, their details are operated and described as follows:
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Initially, the QFD is employed to separate product features into either perceived CRs (customer requirements) or configurable FAs
An illustrative example of assessing various prototypes of ultrabooks
At the Intel Developer Forum in 2011, four Taiwan ODMs showed prototype ultrabooks that used Intel's Ivy Bridge processors which only consume 17 W default thermal power. Meanwhile, Intel tries to enhance the slumping PC markets against rising competition from tablet computers such as the iPad, which are typically powered by the ARM-based architectures [29], [30]. Originally, for fast stimulating market sales, Intel plans to set a “below $1000” price for ultrabooks. However, the presidents of
Concluding remarks and future research
Today, manufacturing companies are inevitably to face the trade-offs between enhancing product varieties and controlling manufacturing costs. Despite that many studies have been presented to address this issue, however, most of them are fully reliant on experts' assessments without tacking customer preferences or customer utilities into account. In order to overcome the above-mentioned shortcoming, this paper presents a hybrid framework which integrates QFD (quality function deployment) with CA
Acknowledgments
The authors would particularly thank two anonymous referees' helpful comments. This paper is financially supported by Taiwan National Science Council under Grant NSC-101-2410-H-009-002.
Chih-Hsuan Wang is an assistant professor in the Department of Industrial Engineering & Management, National Chiao Tung University (NCTU), Taiwan. Prior to joining NCTU, he has been a faculty member in the Department of Marketing, National Chung Hsing University and an adjunct assistant professor at the National Taiwan University. During 2003 to 2005, he has been a research scholar at the University of Tennessee and Texas A&M University, respectively. He has published several SCI papers in IIE
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Chih-Hsuan Wang is an assistant professor in the Department of Industrial Engineering & Management, National Chiao Tung University (NCTU), Taiwan. Prior to joining NCTU, he has been a faculty member in the Department of Marketing, National Chung Hsing University and an adjunct assistant professor at the National Taiwan University. During 2003 to 2005, he has been a research scholar at the University of Tennessee and Texas A&M University, respectively. He has published several SCI papers in IIE Transactions, IJPR, C&IE, CS&I, JIM, and ESWA. His research interests include product development, operation management, service science, and business intelligence. Since 2006, he also served a session chair for the IEEM and APIEMS international conferences.
Chih-Wen Shih is currently a PhD candidate at the Department of Industrial Engineering and Management, National Chiao Tung University, Taiwan. His research interests are human–computer interaction, ambient intelligence, service science, business intelligence and supply chain management, etc. For the past 5 years he has been working most of the time in the ICT enabled applications and services. Currently, he is the project manager at Institute for Information Industry in Taiwan.