| Quality function deployment (QFD) is a customer-driven system of product planning and design in a widely used way. In a general, QFD system utilizes product planning house of quality, parts deployment house of quality, process planning house of quality, and manufacturing deployment house of quality. When a lot of corporations use QFD for product planning and design, their internal conflicts are minimized, their development cycle time are shortened, their market penetration are increased, their product quality are improved, and their customer satisfaction are increases, and then their higher revenues are obtained.Product planning house of quality is the first and fundamental phase in the QFD system. The process of building product planning house of quality must tackle multi-format group fuzzy information as following:the multi-format group fuzzy information of customer requirements (CRs), the group fuzzy competitive evaluation information of technical characteristics (TCs), the group fuzzy information of integrated correlation influences, the group multi-format fuzzy preference information of the competitive products, the group fuzzy information of improvement targets of TCs. According to multi-format group fuzzy information, the paper investigated a group multi-format fuzzy information-based approach of building product planning house of quality and it’s application of a3PL corporation using fuzzy set, group decision, and uncertain theory.The major contents are summarized as following:(1) Based on the multi-format group fuzzy information of CRs, a multi-format group fuzzy information-based approach of determining the final importance ratings of CRs is proposed as following:firstly, based on the group fuzzy importance evaluations of customer requirements for the selected customers, the normalized asymmetric triangular fuzzy importance evaluations are processed to determine the fundamental importance ratings of CRs by using the expected value operator of fuzzy variable; secondly, according to the fuzzy competitive evaluation of CRs, the model of determining the competitive importance ratings of CRs was built based on the principle of fuzzy minimal deviation, and resolved by using Lagrange function; thirdly, based on fuzzy improvement targets of CRs corresponding to each one of the selected customers, the fuzzy improvement ratios of CRs corresponding to each one of the selected customers are determined, and then the accurate improvement ratios of CRs are acquired by using the operator of fuzzy expected value; lastly, based on the combination of the fundamental importance rating, the competitive one, and accurate improvement ratio of each CR, the final importance ratings of CRs were determined.(2) According to the fuzzy competitive evaluation of CRs and the group fuzzy competitive evaluations of TCs, a group fuzzy information-based approach of estimating relationships measures is proposed as following:according to these competitive evaluations, a model for determining the importance ratings of ECs corresponding to every CR is built by using the principle of maximizing the similarity degrees between triangle fuzzy numbers, and this models was resolved by using Lagrange function. A concept of type gene of a functional relationship is introduced to express to the effect of the relationship type on the relationship measure. The relationship measures are estimated by the combination of the relative magnitude and type gene of each relationship.(3) According to the group fuzzy competitive evaluation information of TCs and the group fuzzy information of integrated correlation influences, a group fuzzy information-based approach of estimating correlation measures is proposed as following:firstly, according to these competitive evaluations, a model corresponding to every EC is built by using the principle of maximizing the similarity degrees between triangle fuzzy numbers, and is resolved for determining the relative size of the correlation measures of this TC on the other ECs; secondly, a concept of the category factor of a correlation and a concept of the threshold of the correlation measures of an TC on the other TCs are introduced; lastly, based on the combination of the relative size of a correlation measure, the category factor of the corresponding correlation, and the threshold of the corresponding correlation measures, the correlation measures among TCs are determined.(4) According to the group fuzzy competitive evaluation information of TCs, the group multi-format fuzzy preference information of the competitive products, and the group fuzzy information of improvement targets of TCs, a group multi-format fuzzy preference-based approach of determining the integrated priority ratings of TCs is proposed as following: firstly, the members in a QFD team are classified into three categories based on the types of their preference; three types of models corresponding to the three categories are built, is resolved for determining the competitive priority ratings of TCs corresponding to each member; based on the integration of the competitive priority ratings of TCs and the relative weights corresponding to each member, the technical points of TCs are determined. Secondly, based on fuzzy improvement targets of TCs corresponding to each one of the selected customers, the fuzzy improvement ratios of TCs corresponding to each one of the QFD members are determined, and then the accurate improvement ratios of TCs are acquired by using the operator of fuzzy expected value; lastly, based on the combination of the initial priority rating, the technical point, and the accurate improvement ratio of each TC, the integrated priority ratings of TCs are determined.Finally, the exactness and feasibility of group multi-format fuzzy information-based approach of building product planning house of quality of building the product planning house of quality was validated through an application of a3PL corporation. |
PDF Full Text Request