نوع مقاله : یادداشت فنی
نویسندگان
دانشکدهی مهندسی صنایع و مدیریت، دانشگاه صنعتی شاهرود
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Quality Function Deployment (QFD) is a customer-oriented tool that aims to meet customer requirements in a better way. Indeed, this function can be described as a methodology that makes every process of product development transparent, starting from understanding the qualities demanded by customers all the way down to establishing quality planning and determining design quality. For achieving the first step of QFD, namely understanding the qualities demanded by customers, this process used House of Quality (HOQ). To that end, major elements of the process are vagueness of the customers assessments and their different level of experience. Since every customer has different knowledge level and experience about the subject under consideration, they use different fuzzy sets with different cardinalities fitting their levels. With regard to this situation, there would appear multi-granularity in information for which we need a procedure to make data homogeneous for solving this problem. In the real world, there exist problems that need to assess their variables with linguistic term sets that are not uniformly and symmetrically distributed; we call this type of linguistic term sets unbalanced linguistic term sets. In some cases, the unbalanced linguistic information appears as a consequence of the nature of the linguistic variables that participate in the problem as it happens usually for showing the attitude of their users. So, for dealing with this situation, we need a process to make information uniformly distributed. Between choosing different Brands, customers usually have some risks. So, for building an effective HOQ, we need a weighting model to consider this issue. Considering the whole of these limitations, we use Hierarchical Ordinal Model which makes the data homogeneous and balanced without any loss of information. Moreover, for avoiding any loss of information during conversion process, this model uses 2-tuple representation model. For considering the risk factor, we also propose the OWA weighting model that has numerous benefits. In the next step, the balanced homogenous information is used in 9-step HOQ building procedure, and the paper is concluded with the ranking of HOWs based on ccustomers assessments.
کلیدواژهها [English]
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