Customer-oriented product design using an integrated neutrosophic AHP & DEMATEL & QFD methodology

Highlights

• A customer-oriented product design approach is developed and applied for car seats.

• A new methodology based on QFD, DEMATEL, AHP and neutrosophic sets is proposed.

• The proposed approach can handle uncertainty in customer expectations.

Abstract

With the increasing product variety, companies aim to become better than their competitors by providing a superior product developed with a customer-oriented product design approach and a quality strategy. In order to achieve this, companies should well understand customer expectations and quickly be able to convert these expectations to technical characteristics. Since the expectations consist of mostly subjective judgments, this evaluation process contains vagueness and impreciseness. A triplet represents the uncertainty in subjective judgments: the degrees of belongingness or Truthiness (T), non-belongingness or Falsity (F), and indeterminacy (I). For this reason, in this paper, a neutrosophic Quality Function Deployment (QFD) methodology based on neutrosophic AHP and neutrosophic DEMATEL is developed and applied to the design of a car seat. In this methodology, the weighting of customer requirements is performed by neutrosophic AHP. The relationships among the technical characteristics are determined by neutrosophic DEMATEL for the customer-oriented product design, considering both impreciseness in the data and indeterminacy of the decision-makers. In other words, the contribution of this paper is that the proposed methodology provides better integration of the voice of customers into technical characteristics through a practical fuzzy multi-criteria decision analysis. Based on the results, it is revealed that seat height is the most important technical characteristic, followed by vertical travel range and horizontal travel range. Moreover, validity and verification of the proposed methodology have been tested with other methods presented in the literature. Sensitivity analyses have been carried out to show the flexibility of the given decisions under different cases. Lastly, possible implications on theoretical and managerial aspects have been discussed.

Introduction

After the second half of the 19th century, enterprises tried to gain superiority against their competitors by developing their products based on the quality strategy because of the drastic increase in the competitive environment among enterprises. Today, enterprises try to gain superiority over their competitors in the market by making a significant difference in their products with a customer-oriented product development strategy as well as a quality strategy. In particular, the rapid developments and growth in technology have increased the speed of offering products to the market. Nevertheless, this shortened the life cycle of the products in the market. For this reason, businesses should provide their product to the market in a short time by understanding customer expectations well, and these expectations should be quickly transferred to the technical characteristics of the product during the new product development stages. QFD has been proposed to literature by Akao et al. [1], [2] in order to develop products by adapting customers’ requirements into the technical characteristics of the products [2]. QFD methodology is represented by a scheme called the house of quality (HoQ). The HoQ consists of six main parts, which define

• Customer Requirements (CRs)

• Technical Characteristics (TCs)

• Relationship matrix among TCs

• Relationship matrix between TCs and customer requirements

• Marketing matrix

• Design targets

Quality Function Deployment (QFD) is one of the most commonly used methods in order to provide a customer-oriented product design. In the literature, there are lots of studies that present applications of QFD on product development [3], [4], [5], [6], [7], [8], [9], [10], [11]. Furthermore, some researchers present some modifications and extensions to develop the performance of QFD by improving the data representation and reflecting parts of HoQ. When the mentioned extensions are reviewed, the reason for these new versions is mainly based on the different kinds of uncertainties in the data set. Since the data set may consist of vagueness, impreciseness, indeterminant and hesitant information, different types of fuzzy set extensions are used to handle the uncertainty. In the traditional set theory, an element can belong to a set or not; in optimization, a solution is either feasible or not; and in Boolean logic, a statement can be true or false, but nothing in between [12]. But in real-life conditions, almost nothing is precise and everything is a matter of degree and cannot be defined by the traditional logic. In order to deal with this kind of uncertainty, Zadeh introduced the fuzzy sets theory [13]. Since its first introduction in 1965, it has been extended in various forms. Type-2 fuzzy sets, again, were introduced by Zadeh to enhance the representation of impreciseness of the data in the mathematical operations [14]. Then, in 1986, Atanassov introduced intuitionistic fuzzy sets, which is a concept that corporates membership and non-membership degrees simultaneously [15]. Then, neutrosophic sets are presented by Smarandache, which offers a domain area that consists of three independent subsets to represent types of uncertainties [16]. Neutrosophic sets are defined as the sets where each element of the universe has a degree of truthiness, indeterminacy, and falsity, which are between 0 and 1, and these degrees are subsets of the neutrosophic sets which are independent of each other [16]. In the neutrosophic sets, impreciseness is represented as truth and falsity functions where degrees of belongingness and non-belongingness and distinguish of absoluteness and relativeness is represented by indeterminacy function. With this notation, neutrosophic sets handle the uncertainty of the system and reduce the indecisiveness of inconsistent information. Therefore, this capability can be said as the most important advantage of the neutrosophic sets among the other types of fuzzy extensions. Using these three functions, neutrosophic sets provide a domain area, which enables the conduct of different types of uncertainties in the mathematical operations independently.

Analytic Hierarchy Process (AHP), proposed by Saaty [17], is a well-known method for handling complex problems by dividing them into sub-problems and then putting the solutions of these sub-problems together. In this method, which is based on pairwise comparisons of experts, ensuring the consistency of the evaluations has an important place. DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method, introduced by Fontela and Gabus [18], is one of the widely used decision making methods when there is dependency among decision criteria. AHP and DEMATEL have been widely used in the literature as standard methods [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], so their application steps are not provided in this study. As different from the literature, a new methodology consisting of AHP, DEMATEL, and QFD extended by interval-valued neutrosophic sets is proposed. Even though customer requirements, TCs and competitor assessment are taken into account in classical QFD, to the best of our knowledge, there is no study calculating the weights of customer requirements and the coefficient of competitor assessment with neutrosophic AHP which can represent uncertainty of customers based on three parameters. Moreover, neutrosophic DEMATEL has not been used to model the relationship between TCs when considering uncertainty. With the proposed approach, uncertainty in the assessment of customer requirements and the relations between TCs and customer requirements, and the dependecies between TCs might be represented more precisely. In this way, it is expected to reflect the voice of customer into the design process by better associating the customers requirements with the TCs. The representation of the proposed methodology is given in Fig. 1.

As in Fig. 1, AHP is used to determine the weights of the customer requirements for the decision-making process with a hierarchical structure in Phase 1. For the direct comparison of the design with the competitor, AHP is conducted to find the differences in Phase 2. Moreover, the DEMATEL technique is conducted to determine the interrelations of the TCs based on the assigned linguistic terms in Phase 3. The assumption for these hierarchical structures is based on no dependency or feedback between the requirements. In Phase 4, TCs are evaluated by the decision-makers with respect to customer requirements. Finally, by aggregating the outputs of these phases, final weights and the importance of the TCs are obtained in Phase 5. For demonstrating robustness, a one-at-a-time sensitivity analysis based on different cases is conducted. Moreover, for the validation of the results and verification of the given decisions, comparative analyses are applied. Through the results of the application and analyses, theoretical and managerial implications have been discussed.

The rest of the study is organized as follows: Section 2 provides a literature analysis both for neutrosophic studies and emerging trends of the QFD method. Section 3 presents the proposed approach together with its preliminaries. Section 4 illustrates the application consisting of problem definitions, calculation and results, sensitivity, and comparative analyses for the design of automobile seat. Section 5 introduces the discussion about theoretical and managerial implications. Finally, in Section 6, concluding remarks and suggestions for further studies are given.