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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.

Purpose -The purpose of this paper is to demonstrate how the quality function deployment (QFD) process can be applied to capture and translate spoken and unspoken customer requirements into actionable service features in a hotel landscaping design case. Design/methodology/approach -This study was undertaken with the aim of showing how the QFD methodology could be used to design hotel landscaping. The methodology is a customer-driven process which integrates customer requirements into every aspect of the design and delivery of products and services. Understanding what the customer desires from a product or service is crucial to the successful design and development of new products and services. Findings -This research illustrates that quality improvement projects can benefit from the QFD process to connect customer requirements to the internal procedures of the organization to exceed customer expectations and create a brand identity. This paper can be used as a case study to demonstrate how the QFD process can be effectively applied in the design of hotel landscaping or similar cases in other services. Originality/value -The literature regarding the application of the QFD process in the hotel and hospitality industry is limited, let alone the application of this process in hotel landscaping design. Hence, the shortage of QFD application in the hotel landscaping design has motivated this unique study of applying the QFD process to landscaping design.

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.

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