Quality function deployment (QFD) is a customer-driven approach in processing new product developments in order to maximize customer satisfaction. Determining the fulfillment levels of design requirements (DRs) and parts characteristics (PCs) is an important decision problem during QFD activity processes for new product development. Unlike the existing literature, which mainly focuses on the determination of DRs, this paper proposes fuzzy linear programming models to determine the fulfillment levels of PCs under the requirement to achieve the determined contribution levels of DRs for customer satisfaction. In addition, considering the design risk, this paper incorporates failure modes and effect analysis (FMEA) into QFD processes, which is treated as the constraint in the models. To cope with the vague nature of product development processes, fuzzy approaches are used for both FMEA and QFD. The illustration of the proposed models is performed with a numerical example to demonstrate the applicability in practice.
Quality function deployment (QFD) is a useful tool for maximising customer satisfaction in the new product planning (NPP) process. When applying QFD, recognising the importance levels of customer requirements (CRs) is a vital issue, since they have great influences on the values of decision variables, i.e. the fulfilment levels of design requirements (DRs), for NPP. Unlike existing researches, this study evaluates the importance scores of CRs from three different perspectives: the customer, competition and value perspectives. To cope with the vague and uncertain nature at the NPP stage, various fuzzy approaches are adopted for measuring the importance scores of CRs from different perspectives. The three measures are aggregated as the evaluated importance score of each CR. In addition, different to prior studies, this paper modifies Chen and Weng's model to develop a new normalised relationship between CRs and DRs to improve the existing models' drawbacks. A fuzzy linear programming model is established to determine the optimal fulfilment levels of DRs to maximally satisfy CRs. Based on an illustrative example, the proposed method achieves greater total customer satisfaction than previous methods. Furthermore, this paper also proposed a normalised relationship formulation with conventional way to reasonablely modify Wasserman's model.
Abstract. The aim of this study was to identify tumor suppressor genes (TSGs) in oral squamous cell carcinoma (OSCC) using whole-genome analysis of microarray technology and real-time quantitative polymerase chain reaction (QPCR). We applied whole-genome analysis of TSGs in the specimens from 3 patients of OSCC by microarray technology. A total of 11 genes, CRISP3, SCGB3A1, AGR2, PIP, C20orf114, TFF1, STATH, AZGP1, MUC7, DMBT1 and LOC389429, were found to be down-regulated, and 2, matrix metallopeptidase (MMP) 1 and MMP3, were found to be up-regulated in the 3 OSCC patients using microarray technology. In this study, we selected the CRISP3 gene. CRISP3 belongs to the cystein-rich secretary protein gene family in chromosome 6p12.3. CRISP3 has been found in the salivary gland, spleen and prostate gland and is a prominent biomarker in the gene expression of prostate cancer. Down-regulation of this gene was previously observed in OSCC. No studies examining the DNA copy number of CRISP3 in detail exist. We analyzed the DNA copy number of CRISP3 in 5 OSCCderived cell lines (SAS, KON, HSC2 and HSC4) and 60 OSCC tissues by real-time QPCR. The DNA copy number loss of CRISP3 was observed in 2 of the 5 OSCC-derived cell lines (SAS, HSC2) and in 24 of 60 patients (40.0%) using real-time QPCR. A significant statistical correlation between the copy number loss and gender and T classification was observed. These results indicate that the inactivation of CRISP3 is an early event in OSCC, since the T1/T2 classification is correlated with DNA copy number loss of CRISP3, whereas T3/T4 classification is not. We conclude that CRISP3 may be involved in the carcinogenesis of OSCC.
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