Building fuzzy front-end decision support systems for new product information in global telecommunication markets: A measure theoretical approach
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PublisherThe University of Arizona.
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AbstractIn today's highly competitive business environment, innovation and new product introduction are recognized as the sustaining forces of corporate success. The early phases of new product development, collectively known as the 'front-end', are crucial to the success of new products. Building a fuzzy front-end decision support system, balancing the needs for analytical soundness and model robustness while incorporating decision-maker's subjectivity and adaptability to different business situations, is a challenging task. A process model and a structural model focusing on the different forms of uncertainties involved in new product introduction in a global telecommunication market are presented in this dissertation. Fuzzy measure theory and fuzzy set theory are used to build a quantitative model of the executive decision-process at the front-end. Solutions to the problem of exponential complexity in defining fuzzy measures are also proposed. The notion of constrained fiizzy integrals demonstrates how the fuzzy measure-theoretical model integrates resource allocation in the presence of project interactions. Forging links between business strategies and expert evaluations of critical success factors is attempted through fuzzy rule-based techniques in the framework of the proposed model. Interviews with new product managers of several American business firms have confirmed the need for building an intelligent front-end decision support system for new product development. The outline of a fuzzy systems development methodology and the design of a proof-of-concept prototype serve as significant contributions of this research work toward this end. In the context of executive decision making, a usability inspection of the prototype is carried out and results are discussed. A computational analysis, based upon methods of tactical systems simulation, measures the rank order consistency of the fuzzy measure theoretical approach in comparison with two competing fuzzy multiple attribute decision models under structural variations of the underlying models. The results demonstrate that (1) the modeling of the fuzzy numbers representing the linguistic variables, (2) the selection of the granularity of the linguistic scales, and (3) the selection of the model dimensions significantly affect the quality of the decisions suggested by the decision aid. A comprehensive plan for future validation of the decision aid is also presented.
Degree ProgramGraduate College