ترکیب هزینه و عوامل محیطی در گسترش کارکرد کیفیت با استفاده از تحلیل پوششی داده ها
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7071||2009||13 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Omega, Volume 37, Issue 3, June 2009, Pages 711–723
Quality function deployment (QFD) is an important tool available to organizations for efficient product design and development. Traditionally, QFD rates the design requirements (DRs) with respect to customer needs, and aggregates the ratings to get relative importance scores of DRs. An increasing number of studies stress on the need to incorporate additional factors, such as cost and environmental impact, while calculating the relative importance of DRs. However, there is a paucity of methodologies for deriving the relative importance of DRs when several additional factors are considered. In this paper, data envelopment analysis (DEA) is suggested for the purpose. It is proved that the relative importance values computed by DEA coincide with traditional QFD calculations when only the ratings of DRs with respect to customer needs are considered, and when only one additional factor, namely cost, is considered. DEA provides a general framework facilitating QFD computations when more factors need to be considered. The calculations are explained using a step-by-step procedure and illustrations. The proposed QFD–DEA methodology is applied to the design of security fasteners for a Chinese company. Though traditional QFD calculations consider the ratings as cardinal numbers, DEA has the flexibility to treat the ratings as qualitative variables. This aspect is illustrated in a separate section.
Organizations that pay attention to quality and customer requirements (CRs) stay ahead of competition and survive in the modern competitive market place. A variety of tools are available to organizations in order to help them achieve this goal. Quality function deployment (QFD) is one such extremely important quality management tool that is useful in product design and development and for benchmarking. When QFD is used for designing a product, the expectations of customers are related to the main design characteristics of the product through a matrix generally known as the “house of quality” (HOQ). The HOQ matrix contains many numerical entries, including the importance of CRs, the relationships between CRs and design requirements (DRs) and the correlations between different DRs. Some of them are elicited using a semantic scale, which are later converted to numerical values. Normally, a simple weighted arithmetic aggregation procedure is employed to aggregate the ratings of DRs with respect to CRs. The resulting weights of DRs can be interpreted as in the proportion of their importance in meeting the CRs. The above QFD process does not explicitly incorporate cost and financial factors , ,  and . These factors are normally incorporated in further analysis. For example, a limited budget may be allocated for the development of DRs on the basis of their relative importance . Given the green considerations in developing QFD matrices , DRs resulting in adverse environmental impacts could be discouraged. Some studies have considered the level of difficulty of implementing DRs for adjusting the relative importances of DRs . However, in general, studies have considered only one extra factor in their analysis ,  and . In this paper, the data envelopment analysis (DEA)  and  is proposed to obtain the relative importance of DRs when several factors have to be considered simultaneously. Since DEA and QFD are not new to the field of operations management, this paper provides only a brief description of these techniques in the next section. Interested readers are referred to some prominent articles. The use of DEA in computing an aggregate weighted score of DRs is explained in Section 3. It is proved that the relative importance of DRs calculated by DEA agrees with traditional methods of aggregation employed in QFD when no factors have been considered or when only one factor is considered. Use of DEA when many additional factors are considered is illustrated using a numerical example. Finally, the proposed QFD–DEA procedure is applied for the design of security fasteners for a Chinese company in Section 4.