Kioumars Paryani

QFD Application in the Hospitality Industry: A Hotel Case Study

Quality function deployment (QFD) is a methodology for capturing and translating the voice of the customer (VOC) into engineering characteristics of products or services. In addition, the process prioritizes and deploys these customer-driven characteristics throughout the product or service development to meet the VOC (that is, customer needs, wants, and expectations). QFD determines effective development targets for the prioritized product and service characteristics. The QFD process has been used and documented extensively in product development. The service industry, however, lacks in the application of this process. The purpose of this paper is to show practitioners and researchers how this process, in its entirety, can be used as a planning process to link customer requirements and service characteristics in the hospitality industry. A case study was developed focusing on a specific hotel to illustrate the application of the QFD process in a five-star hotel.

Identifying Useful Variables for Vehicle Braking Using the Adjoint Matrix Approach to the Mahalanobis-Taguchi System

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.

Forecasting consumer satisfaction for vehicle ride using a multivariate measurement system

Consumers perceive quality and performance at the vehicle level. Consumers evaluate vehicle attributes such as ride, handling, roominess, braking and acceleration. Vehicle level attributes are influenced by factors at all levels of the vehicle architecture, and these factors are often correlated. The goal of this research is to efficiently forecast consumer satisfaction measured as a function of vehicle level performance data by developing a multivariate measurement system using the Mahalanobis-Taguchi Gram-Schmidt approach. The Mahalanobis-Taguchi Gram-Schmidt technique is applied to construct the measurement scale and identify a reduced set of useful variables for vehicle ride sufficient to make effective predictions.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

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.Copyright

Applying the Mahalanobis-Taguchi System to Vehicle Ride

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.Copyright

Applying the Mahalanobis-Taguchi System to Vehicle Handling

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.

Forecasting Consumer Satisfaction for Vehicle Ride Using the Mahalanobis-Taguchi Gram-Schmidt Technique

Abstract: Consumers assess and perceive quality and performance at the vehicle 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 needs by providing affordable products. Consumers evaluate vehicle attributes such as ride, handling, roominess, braking, and acceleration. These vehicle level attributes are influenced by factors at all levels of the vehicle architecture, and these factors are often correlated. The goal of this research is to efficiently forecast consumer satisfaction measured as a function of available vehicle level performance data. This article presents the application of the Mahalanobis-Taguchi Gram-Schmidt technique to identify a reduced set of useful variables for vehicle ride.