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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.

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.

The quality loss function developed by Dr. Genichi Taguchi considers three cases including nominal-the-best, smaller-the-better, and larger-the-better. The methodology used to deal with the larger-the-better case is slightly different from that for the smaller-the-better and nominal-the-better cases. This paper attempts to bring about similarity among the three cases by introducing a term called the “target-mean ratio” and proposing a common formula for all three cases. The “target-mean ratio” can take different values to represent all three cases to bring about consistency and simplify the model. Also, it eliminates the assumption of target performance at infinite level and brings the model closer to reality. Characteristics such as efficiency, coefficient of performance (COP), and percent nondefective are presently not larger-the-better characteristics due to the assumption of target performance at infinity and the subsequent necessary derivation of the formulae.

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.

This paper re-visits the concept of Design Stability proposed earlier. It further develops a new metric called the Design Stability Coefficient for use in the concept selection stage during early phases of new product development. Design Stability Coefficient is analyzed qualitatively as well as quantitatively and developed as an objective criterion for selecting the best alternative for realizing high value for the end-user of a product. A Wheatstone Bridge is used as an example to indicate a procedure for computing the Design Stability Coefficient. Calculated values are compared with the Nominal the Best signal to noise ratio.

The concept of product or system function is considered as described in the Taguchi System of Quality Engineering. The importance of transfer functions is also discussed and a review of conventional value analysis techniques is given. This paper proposes a combination of the principles of robust design and value methodology to enable on-target functionality and direct cost allocation early in the product development process. The discussion on integration of value analysis principles in robust design methodology is provided considering the six sigma environment.

The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance 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. The issue of multicollinearity is not adequately addressed in the MTS method. In cases where strong relationships exist between variables, the correlation matrix becomes almost singular and the inverse matrix is not accurate. Multicollinearity can be handled by utilizing the adjoint matrix of the correlation matrix and Gram-Schmidt orthogonalization. This paper presents a case study of the MTS methodology with the application of the adjoint matrix to avoid some effects of multicollinearity.