دانلود مقاله ISI انگلیسی شماره 7061
عنوان فارسی مقاله

شاخص نیرومندی و اولویت بندی قوی در گسترش کارکرد کیفیت QFD

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
7061 2009 8 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Robustness indices and robust prioritization in QFD
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 36, Issue 2, Part 2, March 2009, Pages 2651–2658

کلمات کلیدی
- گسترش کارکرد کیفیت - اولویت بندی - استحکام - عدم قطعیت - تنوع
پیش نمایش مقاله
پیش نمایش مقاله شاخص نیرومندی و اولویت بندی قوی در گسترش کارکرد کیفیت QFD

چکیده انگلیسی

The prioritization of engineering characteristics (ECs) provides an important basis for decision-making in QFD. However, the prioritization results in the conventional QFD may be misleading since it does not consider the uncertainty of input information. This paper develops two robustness indices and proposes the notion of robust prioritization that ensures the EC prioritization to be robust against the uncertainty. The robustness indices consider robustness from two perspectives, namely, the absolute ranking of ECs and the priority relationship among ECs. Based on the two indices, robust prioritization seeks to identify a set of ECs or a priority relationship among ECs in such a way that the result of robust prioritization is stable despite the uncertainty. Finally, the proposed robustness indices and robust prioritization are demonstrated in a case study conducted on the ADSL-based high-speed internet service.

مقدمه انگلیسی

QFD is a mechanism for translating the ‘voice of customer’ into the ‘language of engineers’ through various stages of a new product development. Ideally, the translation uses a chart, called “house of quality (HOQ)”. A set of typical components of an HOQ include the customer attributes (CAs) and their relative weights, the engineering characteristics (ECs), the relationship matrix between CAs and ECs, the correlation matrix among ECs, the CA and EC benchmarking data, and the EC importance (ECI) values and their target levels. The basic intent of the QFD is to prioritize the ECs by utilizing the information given in the HOQ. Once the ECI values are computed, the ECs are prioritized simply by comparing the ECI values. The EC prioritization is used as the basis for making important decisions in a new product development such as the selection of some important ECs or the building of priority relationships among ECs (Chan & Wu, 2002). In the conventional QFD, such analyses are conducted under an assumption that all the input information is certain. However, since the focus of QFD is placed on the early stage of a new product development, uncertainty in the input information of QFD is inevitable (Kim et al., 2000 and Xie et al., 2003). The effect of uncertainty is propagated into ECI values. Hence, the EC prioritization can be misleading if uncertainty is neglected. The subsequent decisions based on improper prioritization will cause serious problems in a new product development. To avoid misleading EC prioritizations, uncertainty itself or the effect of uncertainty on prioritization decision should be reduced. The reduction of uncertainty itself is very difficult, if not impossible, and costs dearly. On the other hand, the reduction of the effect of uncertainty is a realizable solution. The reduction of the effect means that prioritization decisions should be made in a robust manner in order that the prioritization decision may be relatively stable despite the given uncertainty. This idea is analogous to that of a robust design in the Taguchi method (Taguchi, 1993). The quantification of the effect of uncertainty is the first step to be considered in reducing the effect. The effect of uncertainty can be measured by the stability of prioritization decision, called robustness. This paper proposes two robustness indices to measure the robustness from two different perspectives – the absolute ranking of ECs and the priority relationship among ECs. A high (low) value of robustness index indicates that the effect of uncertainty on the prioritization decision is low (high), respectively. In an effort to reduce the effect of uncertainty based on the robustness indices, this paper also proposes a methodology that prioritizes ECs to maximize the value of robustness indices, called ‘robust prioritization’. Robust prioritization is a kind of goal programming where the robustness indices are substituted for the objective functions to be maximized. Section 2 describes the limitation of EC prioritization in the conventional QFD. Section 3 proposes two robustness indices and robust prioritization. The robustness indices and robust prioritization are demonstrated through a case study in Section 4. Section 5 discusses additional issues related to robustness evaluation and robust prioritization of ECs. Finally, concluding remarks are given in Sections 6.

نتیجه گیری انگلیسی

This paper discussed the uncertainty in the input information of QFD and the effect of uncertainty on EC prioritization in QFD, showing that EC prioritization could be misleading if uncertainty is neglected. To avoid the misleading EC prioritization, we attempted to reduce the effect of uncertainty on the EC prioritization. We developed two robustness indices from two perspectives on the robustness – (i) absolute ranking of ECs and (ii) priority relationship among ECs. Then, we proposed the notion of robust prioritization that seeks to identify EC or V with the highest robustness under a given uncertainty. The robustness indices and robust prioritization were illustrated via a case study on a high-speed internet service. A future research is called for on the multiple sources and types of uncertainty. First, the input information of QFD other than CA weights and CA–EC relationship may have uncertainty. For instance, the correlation matrix is likely to have uncertainty in its assessed entries. Such uncertainty would affect the QFD analysis. Hence a systematic method is warranted to incorporate the uncertainty from multiple sources. Second, multiple types of uncertainties may be present at the same moment. For example, the CA weights may be fuzzy as well as vague or heterogeneous among customers. Such a case is often encountered in practice when developing highly innovative products. A more in-depth study on this interesting issue is necessary.

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