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Abstract
This paper deals with one of the quality planning tools of TQM which helps us to learn what customers wants and provides a systematic and quantifiable approach to determining what is of value to the customers and the ability of the competition to determine the same. This paper introduces a method and procedure for the development of products and services. The method is called Quality Function Deployment aka QFD and it starts with market analysis to clarify customer requirements and identifies important design requirements. The functional requirements on the product / service are analyzed and technical solutions are selected. The paper intends to present a detailed treatment of QFD for service functions and industries. Although a similarity between service industries and manufacturing processes exits, several features are specific to service industries. Service functions have an intangible component, and often cannot be stored. Moreover, customer satisfaction is influenced by behavioral factors, which are difficult to quantify. The paper provides a broad classification of service quality characteristics into four categories: human and behavioral factors, timeliness, service non-conformity, and facility related. The study is done with management education as the focal service point. All the broad steps needed to generate the QFD model are touched upon, albeit not in great detail but covers the scope of the article. A general model for service quality has been discussed, and the factors that influence customer perception have been addressed. The framework for the general model can be applied to construct the individual QFD Matrix for different service functions.
Suggested Citation
Jitendra Sharma, 2015.
"QFD - Can it be applied to Management Education,"
Proceedings of International Academic Conferences
2704698, International Institute of Social and Economic Sciences.
Handle:
RePEc:sek:iacpro:2704698
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JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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