Information Sciences: an International Journal
Developing the index for product design communication and evaluation from emotional perspectives
Expert Systems with Applications: An International Journal
A rough set approach for estimating correlation measures in quality function deployment
Information Sciences: an International Journal
Rough set-based approach for modeling relationship measures in product planning
Information Sciences: an International Journal
Linking objective and subjective modeling in engineering design through arc-elastic dominance
Expert Systems with Applications: An International Journal
Calibrated fuzzy AHP for current bank account selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Understanding customer requirements and incorporating them into the conceptual vehicle design is the first step of automotive product development (PD). However, lack of quantitative data and undefined relationships between the attributes makes it difficult to develop a quantitative model for analyzing subjective customer satisfaction (CS) attributes. While researchers and practitioners have accomplished a significant success in terms of developing tool such as quality function deployment (QFD) to capture the voice of customers, and mathematical models for selecting engineering design alternatives, there is limited precedence in terms of prior works on customer satisfaction driven quality improvement target planning and prioritization of customer satisfaction attributes for target planning. This paper presents a fuzzy set theory based analytic hierarchy process (fuzzy-AHP) framework for prioritizing CS attributes in target planning. Furthermore, unlike prior QFD papers, we consider a broad range of strategic and tactical factors for determining the weights. These weights are then incorporated into target planning by identifying the gap in the current CS level. A case example from automotive industry is presented to demonstrate efficacy of the proposed methodology. The framework has been implemented on MS Excel(R) so that the industry can easily adopt it with limited amount of training and at no additional software cost.