Search Results

Quality Function Deployment (QFD) is a process to translate the Voice of the Customer (VOC) into the technical will of the enterprise. QFD is an integral step in the flowdown of customer requirements into the product system, subsystem(s), components and manufacturing processes. The House of Quality (HOQ) is a fundamental element of the QFD process, which provides a framework to relate customer attributes to engineering characteristics at the product architecture level. This paper presents the advantages and limitations of the symbols and weighting schemes used in constructing the relationship matrix in the classical House of Quality of the Quality Function Deployment process. The selection of the weighting scheme for function flowdown during the early design phase impacts the prioritization of engineering characteristics. A simple example using an nxn matrix is provided to illustrate the impact.

The Mahalanobis Taguchi System (MTS) is a diagnosis and forecasting method for multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. MTS is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This paper presents the application of the Adjoint Matrix Approach to MTS for vehicle braking to identify a reduced set of useful variables in multidimensional systems.

This paper presents a comparison of methods for the identification of a reduced set of useful variables using a multidimensional system. The Mahalanobis-Taguchi System and a standard statistical technique are used reduce the dimensionality of vehicle ride based on consumer satisfaction ratings. The Mahalanobis-Taguchi System and cluster analysis are applied to vehicle ride. The research considers 67 vehicle data sets for the 6 vehicle ride parameters. This paper applies the Mahalanobis-Taguchi System to forecast consumer satisfaction and provides a comparison of results with those obtained from a standard statistical approach to the problem.