Toward a framework for developing knowledge-based decision support systems for customer satisfaction assessment: An application in new product development

Abstract Customer-oriented product development has become increasingly necessary for competitive reasons. This paper describes a framework and a methodology for the design, development, and implementation of knowledge-based decision support systems for customer satisfaction assessment. A generic approach is presented that integrates knowledge-based systems with both a well-known and accepted modeling technique (scoring models), and several decision support techniques (such as the analytic hierarchy process and discriminant analysis). In addition to the fexibility and developmental advantages of knowledge-based systems, additional benefits of this approach include reduced information processing and gathering time, improved communications with senior management, and better management of scarce development resources. To simplify the exposition, we illustrate the framework and methodology within the context of a successful system implementation. The resulting system, known as the Customer Satisfaction Assessment System (CSAS), is designed to provide the decision support necessary to evaluate whether or not full-scale development of a candidate product should proceed. The system assesses and estimates the extent to which a potential new product will meet the expectations of the customer. CSAS incorporates market research findings, as well as strategic evaluation factors and their interrelationships. It can function as a stand-alone system or in conjunction with other evaluation systems (e.g., those providingfinancial, technological, manufacturing, and marketing evaluations) to provide a complete assessment of the product under consideration. Since its implementation, the experts' and other users' expressions of complete satisfaction and commitment to the system has been an indication of its value as an important decision support tool. The paper concludes with a discussion of the lessons learned for future implementations and some important extensions of this research.

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