دانلود مقاله ISI انگلیسی شماره 391
عنوان فارسی مقاله

تعیین اولویت رتبه های نهایی نیازهای مشتریان در برنامه ریزی محصول MDBM و BSC

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
391 2012 13 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Determining the final priority ratings of customer requirements in product planning by MDBM and BSC
منبع

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

Journal : Expert Systems with Applications, Volume 39, Issue 1, January 2012, Pages 1243–1255

کلمات کلیدی
کارت امتیازی متوازن - استقرار عملکرد کیفیت - نیاز مشتری - رتبه بندی اولویت - اتاق برنامه ریزی کیفیت محصول - کارت امتیازی متوازن
پیش نمایش مقاله
پیش نمایش مقاله تعیین اولویت رتبه های نهایی نیازهای مشتریان در برنامه ریزی محصول MDBM و BSC

چکیده انگلیسی

Quality function deployment (QFD) has been widely used to translate customer requirements (CRs) into engineering characteristics (ECs) in product planning and improvement. Product planning house of quality (PPHOQ) is of fundamental and strategic importance in the QFD system. Correctly determining the final priority ratings of CRs is essential in the process of constructing PPHOQ, because it will largely affect the target value of ECs for product improvement. This paper present a systematic and operational method based on the integration of a minimal deviation based method (MDBM), balanced scorecard (BSC), analytic hierarchy process (AHP) and scale method to determine the final priority ratings of CRs. To exploit the competition and preference information of product improvement, the MDBM is developed to determine the CPRs of CRs. A concept of the total output of achieving the ITPE of a CR is introduced and analyzed by using the integration of BSC, AHP and scale method in a qualitative and quantitative way, and then the priority rating of achieving the ITPE of this CR is determined. Finally, a case study is provided to illustrate the effectiveness of the proposed method.

مقدمه انگلیسی

Manufacturing corporations are facing more and more intense competition in the global markets. They have realized that efficient design and manufacture of products preferred by customers at more competitive cost and shorter lead time over those offered by their competitors is crucial to their survival and development. The adoption of quality function deployment (QFD) is an extensive practice in manufacturing corporations to cope with competitive global competition. QFD is a planning and problem-solving methodology that translates customer requirements (CRs) into engineering characteristics (ECs) of a product (Akao & Mazur, 2003). When corporations direct their efforts towards meeting their CRs, internal conflicts are minimized, development cycle times are shortened, and market penetration is increased, owing to improve product quality, gain better customer satisfaction, and obtain larger revenues (Carnevalli and Miguel, 2008, Fung et al., 2002, Iranmanesh and Thomson, 2008, Kim et al., 2000, Kwong et al., 2008, Li et al., 2010, Shen et al., 2001, Shin et al., 1997 and Soota et al., 2008). Generally, QFD utilizes four sets of matrices called house of quality (HOQ) to translate CRs into ECs, subsequently, into parts characteristics, process plans, and production requirements (Chan and Wu, 2002a, Chen, 2009, Karsak, 2004, Kannan, 2008, Kim et al., 2007, Kumar et al., 2000, Kuo et al., 2008, Li et al., 2009, Min and Kim, 2008, Raharjo et al., 2006, Reich and Paz, 2008, Tang et al., 2002 and Xie et al., 2003). The first set of matrices is called product planning house of quality (PPHOQ), is of fundamental and strategic importance in the QFD system, since the customer requirements for the product are identified, and incorporating the producing corporation’s competitive priorities, converted into ECs, and then determine the target levels for ECs of improved products. In the complex decision process, determining the final priority rating (PRs) of CRs in PPHOQ is a crucial step. Since the final PRs of CRs are finally translated into the initial importance ratings of ECs which will largely affect the target value of ECs for product improvement, considerable efforts must be committed to properly acquire the final PRs of CRs in order to keep the corporation successful. Determining the final PRs of CRs generally consists of five steps, which are essentially the input of CRs in the construction of PPHOQ (Chan and Wu, 2002b and Chan and Wu, 2005). These proposed hierarchical steps are described as follows. Step 1. Identify customers and acquire their requirements. Step 2. Determine the fundamental PRs of CRs. Step 3. Conduct competitive analysis of CRs. Step 4. Set the proper improvement targets of CRs. Step 5. Determine the final PRs of CRs. Since CRs are the driving force in the PPHOQ, considerable efforts must be committed to properly capture those requirements to keep a corporation successful. There are many methods available to collect CR candidates, including focus group, individual interviews, listening and watching, complaints, natural field contact, warranty data, feedback, affinity diagram, and cluster analysis. Grouping related CR candidates into a category is helpful to analyzing these candidates. Affinity Diagram or cluster analysis can be used to organize CR candidates as a tree-like structure CRs for PPHOQ (Griffin and Hauser, 1993, Hauser and Clausing, 1988 and Kwong et al., 2008). As one of the key issues of PPHOQ construction, determining the fundamental PRs of CRs have been extensively researched and quite a number of approaches have been suggested in the QFD literatures. For example, the simplest method for determining the fundamental PRs is based on point scoring scale (Hauser & Clausing, 1988). Conjoint analysis method was attempted to determine the fundamental PRs (Griffin & Hauser, 1993). Some researchers described the use of analytic hierarchy process (AHP) for determining the fundamental PRs of CRs (Armacost et al., 1994, Chan et al., 1999, Lu et al., 1994, Wasserman, 1993 and Xie et al., 1998). Recently, Ho proposed that the combination of AHP and QFD is one of the most commonly used techniques (Ho, 2008). As AHP’s variants, Fuzzy AHP (Kwong and Bai, 2003 and Wang, 1999), analytic network process (ANP) (Ertay et al., 2005, Karsak et al., 2002, Partovi, 2006, Partovi, 2007, Partovi and Corredoira, 2002 and Raharjo et al., 2008) and fuzzy ANP (Büyüközkan et al., 2004 and Kahraman et al., 2006) have increasingly been used recently. Fuzzy weighted average (Chen et al., 2006 and Khoo and Ho, 1996) and grey model (Wu, 2006) have also been suggested for prioritizing CRs. To deal with incomplete, imprecise and ignorance information in CRs, the evidential reasoning based approach (Chin, Wang, Yang, & Poon, 2008), the rough set based approach (Zhai et al., 2007 and Zhai et al., 2009) and the rough set enhanced fuzzy approach (Zhai, Khoo, & Zhong, 2008) were separately proposed to acquire the fundamental PRs of CRs. Group decision-making technique was proposed to determine the fundamental PRs of CRs (Ho et al., 1999, Liu and Wu, 2007 and Liu and Wu, 2008). Many papers have proposed many mature methods on the topics of the first two steps. This paper will focus on the third step and the fourth one to acquire the final PRs of CRs. The competitive priority ratings of CRs can generally be obtained by analyzing relative performance estimations of a corporation and its competitors from selected customers’ perceptions, usually using the sales point concept. To analysis the corporations’ competitive performance estimations more objectively and convincingly, the traditional sales point concept was modified or other methods were applied. As a concept in information theory, entropy method measures the expected information content of a certain message and becomes an important concept in natural and social science. The use of entropy method to obtain the competitive priority ratings (CPRs) of CRs was proposed by Chan and Wu (2005). It assumes that when the corporations perform the same, it means there is a good opportunity to be excellent. In fact, each of selected customers has different preference for the products of the corporation and its competitors. The judgments of competitive performance estimations and preferences of these customers depend on their psychological aspects, such as experience, learning, states of mind, and so forth. Therefore, the consistencies between these preferences and competitive performance estimations are rarely satisfied. Therefore, this paper will propose a minimal deviation based method (MDBM) to determine the CPRs of CRs, based on the idea of minimal deviations to express these inconsistencies. The improvement ratio of performance estimation (IRPE) of a CR was integrated into customer competitive analyses (Chan and Wu, 2002b, Chan and Wu, 2005, Lai et al., 2008, Tan and Shen, 2000 and Tan et al., 1999). Nevertheless, the IRPE is one of the customer outputs of achieving the improvement target of performance estimation (ITPE) of a CR. The total output of achieving the ITPE of a CR must be estimated from the short- and long-term objectives which balanced scorecard (BSC) can be provided. Because achieving the ITPEs of CRs as a project is suitable for the use of BSC, the total output of achieving the ITPE of a CR will be determined from the five perspectives – the four original perspectives of BSC (financial, customer, internal-business processes, learning and growth) and a feasibility perspective in a qualitative and quantitative way, and then the priority rating of achieving the ITPE of this CR will be estimated. The above literature review clearly shows that quite a lot of efforts have been made to determine the final PRs of CRs. Nevertheless, these researches could not exploit competition and preference information, and estimate the short- and long-term outputs of achieving the ITPEs of CRs in a comprehensive and accurate way. The purpose of this paper is to develop a comprehensive and systematic method to determine the final priority rating of CRs, on the basis of the proposed MDBM for dealing with the competition and preference information of CRs, and the integration of BSC, AHP and scale method for estimating the priority ratings of achieving the ITPEs of CRs. It can be used to help the QFD team to rate the final PRs of CRs by the integration of the fundamental PR, the CPR, and the PR of achieving the ITPE of each CR. It is capable of exploiting the competition and preference information of product improvement, estimating the short- and long-term outputs of achieving the ITPEs of CRs, and using the proposed unified hierarchical steps in a reliable, systematic, comprehensive, and accurate way. The rest of the paper is organized as follows: in section 2, we summarize the processes of determining the fundamental PRs of CRs. Section 3 develops the MDBM for acquiring the CPRs of CRs. Section 4 analyzes the priority ratings of achieving the ITPEs of CRs by the integration of BSC, AHP and scale method. Section 5 specifies the method for determining the final PRs of CRs based on the integration of the fundamental PR, the CPR, and the priority rating of achieving the ITPE of each CR. Section 6 illustrates a case study of digital video to empirically verify the feasibility and effectiveness of the proposed method. The characteristics and limitations of the proposed method are discussed in Section 7.

نتیجه گیری انگلیسی

Determining the final priority ratings of customer requirements is a fundamental problem in constructing PPHOQ. In this paper, a systematic and operational method is proposed to determine the final priority ratings of customer requirements for exploiting competitive and preference information, and estimating the short- and long-term outputs of achieving the ITPEs of CRs. The proposed method is based on the MDBM for dealing with the competition and preference information of CRs, and the integration approach of BSC, AHP and scale method for analyzing the total output of achieving the ITPE of each CR. The proposed method can be used to help the QFD team to determine the final priority ratings of CRs with the integration of the fundamental PR, the CPR, and the priority rating of achieving the ITPE of each CR. It can exploit the competition and preference information using unified hierarchical steps in a reliable, systematic, comprehensive, and accurate way, and can also estimate the short- and long-term outputs of achieving the ITPEs of CRs in a comprehensive and accurate way. To fully illustrate the proposed method, a case study of product improvement of digital video is provided. Perhaps, a drawback to use the proposed method is its complexity. Nevertheless, such a drawback can be overcome when the proposed method is implemented recursively by using software Matlab.

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