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The concept of complexity is an essential constituent of axiomatic design and axiomatic quality. A reciprocal transformation is used to compute quality loss for larger-the-better performance characteristics in Taguchi’s methodology. El-Haik’s work on connecting robust design and axiomatic design with the help of mathematical relationships also considers the reciprocal methodology for larger-the-better functional requirements. Because of reciprocal transformation axiomatic complexity formulation for larger-the-better performance characteristics is different from that for smaller-the-better and nominal-the-best characteristics. Second, the signal-to-noise ratio based on complexity is also different for the same reason. This paper shows that a simple linear transformation in which a finite target is assumed can give more appropriate and comparable signal-to-noise ratio based on complexity for larger-the-better characteristics.

Another methodology has been proposed by Sharma and Ragsdell to bring about similarity among the three cases smaller-the-better, nominal-the-best, and larger-the-btter by introducing a term called the “target-mean ratio” and proposing a unified formula for quality loss. The new methodology has some implications that need to be addressed. This paper attempts to study the implications and effects of the new methodology on the field of quality engineering. This paper presents an implied classification of LTB characteristics according to Taguchi on the basis of a target value at infinity and also discusses the classification of LTB characteristics based on the new methodology. A new concept of “Complementary Characteristic” is also suggested. It is suggested that whether a given LTB characteristic or its complementary characteristic is considered for one and the same case, the quality loss must be equal for both the characteristics.

Consumers judge quality and performance at the system level, but important cost-effective decisions at the sub-system or component level must be made by the producer in order to economically satisfy consumer's needs by providing affordable and high quality products. Mahalanobis distance (MD) is a distance measure that is based on correlations between variables and the different patterns that can be identified and analyzed with respect to a reference population. MD is a discriminant analysis tool, which will be used to predict warranty cost using multiple characteristics at all levels of a hardware set. The Mahalanobis-Taguchi System is a diagnosis and forecasting method for multivariate data. The Mahalanobis-Taguchi System 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.

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.