یک رویکرد فازی AHP برای اولویت بندی ویژگی های CS در برنامه ریزی هدف برای توسعه محصول صنعت خودرو

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® so that the industry can easily adopt it with limited amount of training and at no additional software cost.

The automotive industry is striving hard to continuously develop higher quality products and improve business effectiveness. The industry uses various customer satisfaction attributes to improve the design of a vehicle. J.D. Power and Associates index is perhaps the most popular customer satisfaction survey used in automotive world (Power & Associates, 2007). They consider 77 vehicle attributes to measure customer satisfaction (CS). Both industry and customers consider these vehicle attributes as critical vehicle performance indicators and therefore important purchasing decision factors. Therefore, the auto industry uses them as one of the quantifiable measures to assess the vehicle performance, to identify potential improvement areas in CS and set future targets for further improvement. Generally, the customer satisfaction targets for vehicle attributes are set at the corporate level based on business and market consideration. Realistically, it is not feasible to address all the potential attributes at once due to such practical constraints as the availability of budget and time, corporate strategic planning, product differentiation strategy, competitive product features, to name a few. Moreover, not all auto companies give equal importance to each attribute because every individual company tries to compete on different product features and attributes. This necessitates the prioritization of potential improvement opportunities (or vehicle attributes) while taking into consideration the existing gap and other practical consideration as mentioned above. However, the challenge is that most of these practical considerations are imprecise (or fuzzy), lacking quantitative measures, and often conflicting in nature. The top management always deliberates these issues in target planning process; however, there is no structured methodology available in public domain that provides a mechanism to capture these considerations in attribute prioritization and CS target setting. The determination of correct relative importance of CS (vehicle) attributes is extremely important in order to achieve total alignment of continuous improvement efforts with corporate (business) strategy. Kano model (Kano, Seraku, Takahashi, & Tsuji, 1984) has been widely used by the design community to identify and prioritize those few attributes that have more potential to achieve higher CS (CQM, 1993 and Yadav and Goel, 2008). Although various methods have been proposed to assign weights to the identified customer requirements, not much has been reported on the prioritization of vehicle attributes. Ho, Lai, and Chang (1999) propose a group decision-making technique for obtaining the importance weights for the customer requirements. Analytic hierarchy process (AHP) developed by Saaty (1980) has been widely used in weighting customer requirements. Gustafsson and Gustafsson (1994) use a conjoint analysis method to determine the relative importance of the customer requirements. All these methods employ pair-wise comparisons of customer requirements to determine their relative importance. Interestingly, the pair-wise comparison methods are based on crisp real number. However, in reality the expert’s assessment in pair-wise comparison is always subjective and imprecise (Chan, Kao, Ng, & Wu, 1999). In order to deal with this deficiency, Kwong and Bai, 2002 and Kwong and Bai, 2003 propose a fuzzy-AHP with an extent analysis approach to determine the importance weights for the customer requirements in quality function deployment (QFD). Another recent application of the integrated fuzzy-AHP model is proposed by Sun, Ma, Fan, and Wang (2008) in the selection of experts for evaluating R&D projects. However, the prioritization of CS vehicle attributes for target planning presents different and rather unique challenge of ensuring complete alignment of CS driven quality improvement efforts with corporate business strategy. The failure to do so will result in mismatch between corporate level business strategy and product development initiatives. Therefore, our intent in this research is to address the need for a comprehensive methodology for prioritization of CS attributes by dealing with subjective and imprecise assessments and ensuring proper alignment between corporate strategy and quality improvement initiatives in PD process. The objective of this paper is to present a fuzzy-AHP framework for determining the relative importance of customer satisfaction attributes in target planning decisions to improve the functionality and performance of a product. With the AHP component, we determine the relative importance of product CS attributes more rationally by synthesizing all available information about the decision in a system-wide and systematic manner. The model further helps us to rank order the attributes by considering multiple factors according to the preference of decision makers. However, AHP’s pair-wise comparison process involves semantic judgment and linguistic comparisons and uses ratings scale like “highly important than”, “moderately important than” etc. which are “fuzzy” in nature. This is especially the case when the CS attributes are set at the corporate level. In order to analyze this subjective information, we propose a fuzzy logic based approach and perform sensitivity analysis of designer’s confidence level on human judgment versus CS attributes prioritization decisions. Unlike Kwong and Bai (2002) application of fuzzy-AHP in QFD, our framework incorporates broader strategic factors (than just engineering) such as marketing, and long term strategic related criteria in target planning. Thereby, our framework integrates the corporate level business strategy with the product development initiatives. Another advantage of our approach is that the whole framework is implemented on MS Excel® which facilitates the adoption process in industry without incurring any additional cost for the software. While this paper discusses automotive case example to demonstrate the methodology, the proposed framework can be applied to any prioritization decision making setting involving multiple factors with limited information and dealing with semantic comparisons. Section 2 describes the fuzzy-AHP methodology for prioritization of customer satisfaction attributes for target planning; Section 3 presents a case example from automotive application; in Section 4, we discuss results, sensitivity analysis and its utility in target planning; and finally Section 5 summarizes the contribution of the paper with some concluding remarks and a direction for future work.

This paper has presented a fuzzy-AHP framework to determine the prioritization weights of CS attributes to facilitate the target planning decision in order to improve vehicle design. In contrast to the traditional AHP approach, the advantage of fuzzy based AHP allows the design community to have freedom of estimation as the judgment can vary from most optimistic to most pessimistic at various level of uncertainty. Further, the fuzzy theory provides a scientific approach to deal with semantic values of information that is generated during the pairwise comparisons. The entire framework has been implemented on MS Excel® to facilitate the adoption process in industry without incurring any additional cost for the software. Further, unlike the prior QFD literature, we consider a broader and strategic approach of prioritization problem and extend it to the target planning. The paper has also showed that the gap in customer satisfaction level can be incorporated to further refine the prioritization ranking of CS attributes provided company wants to given some consideration to current CS level. The results obtained from this analysis provide an in-depth insight of the real problem facing the auto industry. A sensitivity analysis is performed to investigate the impact of confidence level of decision maker’s on subjective judgment on the prioritization of CS attributes. The results from the automotive case study show that instead of focusing on small improvements on product functionality, the company needs to make a strong strategic decision to produce more fuel economical cars with a reasonable engine power to compete in the niche market. In overall, the proposed framework provides design engineers with a hands-on analytical tool to formulate an order wining strategy while considering any undertaking for product improvement. Furthermore, the proposed framework provides a structured decision making process, which can be repeated in any other similar problem setting beyond automotive involving multiple criteria and semantic judgments. While there are many advantages of fuzzy-AHP methodology presented in the paper such as analytical basis for decision making and usability of tools when there is subjective or incomplete data, it may not be suitable for every problem due to time and complexity of the data collection process. The challenge may arise in terms of getting a consensus value for weights especially if we involve more than one expert in the judgment process. It is highly recommended that the experts have an agreement on the relative importance of each criterion with respect to its contribution to higher level objective. Following three scenarios are plausible in group decision making process. – All the experts will have concurrence on their judgment. In this case, there is no dispute and we can use the consensus score for each pairwise comparison. – Experts may differ on their judgment. In such case, one should try to achieve the agreement on the relative importance of the elements by using the technique such as Delphi method (Handfield et al., 2002). The experts are provided with the judgments of other group members and asked for re-evaluation. As a result, there is a possibility that the experts will agree on the consensus value. – Experts may differ and Delphi or any other techniques to achieve consensus on the relative importance of elements fail. Although rarely but such situations do occur in the real world. In such cases, the weights for each element are separately based on each individual’s judgment. At the end, an average weight for each element is calculated by combining the individual weights assigned by different experts for the element under question. Further, the current approach does not incorporate the interaction between the prioritization criteria. For example, the fuel economy may be considered under multiple sub-criteria such as the “alignment with corporate business strategy” and the “product improvement opportunity”. Such issues should be incorporated into the future decision analysis model for determining weights of CS attributes in target planning.