The Mahalanobis-Taguchi system (MTS) is a diagnosis and forecasting method using multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and patterns that can be identified and analyzed with respect to a base or reference group. The MTS is of interest because of its reported accuracy in forecasting using 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 article presents the application of the MTS, its applicability in identifying a reduced set of useful variables in multidimensional systems, and a comparison of results with those obtained from a standard statistical approach to the problem.
The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.
High product quality, service reliability, and management of operations are key factors in business growth and sustainability. Analyzing “The Starbucks Experience” is a pedagogical approach to reinforcing the concepts of control and management of quality, service reliability, and efficient operations in action. The objective of this paper is to show how providing high-quality, reliable products and service at Starbucks have influenced its market share, productivity, and profitability. In turn, Starbucks has improved on these business measures by excelling in operations management. The approach taken was to research the early days at Starbucks to gain insight on what made Starbucks so successful and then to use observational research to assess the customer experience at a particular Starbucks store in a city in the state of Michigan, USA. Sitting in this Starbucks store in the city’s little downtown and observing its operations and customers contact offered the opportunity to observe customers waiting in line, the baristas serving the customers, examining the store’s layout, and listening to conversations that revealed what customers like and dislike about “The Starbucks Experience.” Recommendations are made to improve operations. These areas fall under operations management for a company that sells a product and provides a service. There are three reasons customers choose Starbucks: the coffee, the people serving the coffee, and the experience in the stores. By excelling in these three areas and improving operations management, Starbucks can regain its market share, and improve productivity and profitability.
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 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. 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 Mahalanobis-Taguchi System and its application to identify a reduced set of useful variables in multidimensional systems.
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