Tailoring Machine Price In Chennai


GFD method mainly includes two models: ASI and Goal/QPC. In accordance with the characters of the sewing machine, the article takes the ASI model. The house of quality (HOQ) is the basis and tools of the ASI model. The ASI model divides the VOC into four periods such as: product planning, parts planning, process planning and production planning. A HOQ is built according to each period. The study mainly focuses on the infant stage of the sewing machine development, so, the study is carried out based on the furher computation and construction of the product planning HOQ. The brief HOQ of the sewing machine is shown as follows:

as research objects, CRi stands for the customer requirement of typical customer i. (2) Weighting coefficient: W is the relative weight of customer’s demand. In this paper, the number of typical customers selected is allowed to determine the metrics of every customer’s requirement according to their individual interests and concerns, w˄i i=1,2ˈˈĂˈg˅is the relative weight of CRi determined by the ist typical customer. And the sum of wi is 15. (3) The improved measurement is adopted to satisfy user’s requirement, and belongs to product quality characteristic (PQC), presented as ECj. (4) Autocorrelative matrix (P): Pjk is the autocorrelation coefficient of ECj and ECk. And “blank, ƾ, Ʒ, Ƶ, ƽ” represents “irrelative, weakly relative, medium relative, strongly relative, self-relative”, and was assigned with the values “0,1,3,5,9” correspondingly. The symbol is Assigned a negative one, if it’s inverse correlation. P is a symmetric matrix. (5) Relationship matrix(R): the mapping relationship between customer’s requirements and improvement measures, reflecting the degree of relationship between them. Usually is shown as R. R=[rij]ˈi=1,2,…,m; j=1,2,…,n; ˄1˅ m: the number of customer’s requirement items; n: the number of improvement measure items; The degree of correlation˄rij˅, “irrelative, weakly relative, medium relative, strongly relative, self-relative”, are replaced by “blank, ƾ, Ʒ, Ƶ, ƽ”. Each symbol was assigned with the value “0, 1, 3, 5, 9” correspondingly in calculation. (6) The importance degree of the improvement measures is shown as T=[tj],˄j=1,2,Ă,n˅. Essentially, tj also implies the contribution of customer’s satisfaction level when the improvement measure ECj is carried out. Thus, in the paper, the customer’s satisfaction level is simply described as tj.  ˄˅   m i j i ij t wr 1 wi: the importance degree of customer requirement i˗ rij: the correlation degree between customer’s requirement i and improvement measure jDŽ (7) cost restriction: the total cost that enterprise has intended for the product improvement, shown as C, Cj. III. OPTIMIZING DECISION-MAKING MODEL IN IMPROVING DESIGN Supposed, the sewing enterprise plan to spend cost C in improving their products. Via VOC reports, g typical customers act as research objects. n improvement measures is to meet the m customer’s requirements. A. Customer’s satisfaction level model of improvement measures T is customer’s satisfaction level of each improvement measures. It is achieved As follows: T WTR ˄˅ Considering the Autocorrelation between ECj, the satisfaction level is revised as follows: T  TP WTRP ˄˅ Column Vector X(xj) is the Planning matrix of improvement measure EC. xj˄xj=0,1 j=1,2,Ă,n˅ is control variables, showing whether or not to carry out the ECj. T  TX ˄ ˅ T  (ti  )ˈi 1,2, ,m ˄ ˅ B. Optimizing design decision-making model Because of the different knowledge structure, preference, life-styles of the typical customers, they usually give various values of importance degree (weighting) to the different CRi. the purpose of the study is how to get the highest score of the all typical customer’s satisfaction level. It also can simply discuss how to maximize the sum of all typical customer’s satisfaction valuation. M is defined as the maximum of the sum, then the mode is:  ˄ ˅    m i i M t 1 max  St.       n j j j j c x C x 1 0,1 xj=0: the improvement measure not to carry out; xj=1: the improvement measure to carry out. Solving those equations, the Optimized group of improvement measure is obtained, and it is confirmed which group leads the maximization of the total typical customer’s satisfaction level under the cost restriction. Therefore, the improvement direction of the sewing machine is clearly defined.

The study mainly focuses on helping enterprises to select suitable measure group to meet the requirement of VOC under the cost restriction. Though the paper takes the example of sewing enterprises, the research concept and methods can also be applied to other industrial enterprises.