The Development Manager's Advisory System: A Knowledge‐Based DSS Tool for Project Assessment*

Department of Business Information Systems and Operations Management, University of North Carolina at Charlotte, Charlotte, North Carolina, United States
Decision Sciences (Impact Factor: 1.36). 06/2007; 24(5):953 - 976. DOI: 10.1111/j.1540-5915.1993.tb00498.x


This research investigates whether the knowledge-based decision support system (KBDSS) paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS to support a product development manager's decision concerning full-scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge-based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi-expert system which was implemented at the strategic level.

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Available from: Matthew J. Liberatore, Mar 14, 2014
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    • "If effective a PQKB will improve individual and organizational learning via its ability for increased storage and transmission capacity, and will tend to lead to more timely and better quality decision making (Huber, 1990). These positive effects are contingent upon the presence of appropriate planning and control mechanisms (Liberatore and Stylianou, 1993), and effective and strategically important implementation (ref). {better lit review, esp. of TQM-specific studies} We propose that the presence of a PQKB will enhance the quality performance of both the targeted, focal process, as well as other processes that are similar to the focal process. "
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    ABSTRACT: While organizations have focused increasing attention towards the acquisition and use of process knowledge for the purposes of quality performance, significant advances in information systems have occurred. The emergence of these two forces creates an opportunity for organizations to benefit from the integration of information systems and quality systems. The basic purpose of an information system is to acquire and represent knowledge. The basic purposes of a quality system are quality planning, quality control, and quality improvement. We therefore define a process quality knowledge base (PQKB) as an information system that acquires process knowledge and represents it for the purposes of quality planning, quality control, and quality improvement. This article proposes that the presence of a PQKB can improve the quality performance of the focal process, as well as the quality performance of processes similar to the focal process. We argue that the degree to which a PQKB can effect quality performance is moderated by the richness of the process description encapsulated inside the PQKB, and the diversity of knowledge sources that are engaged during the construction and use of the PQKB. Managerial and research implications are highlighted.
    Journal of Quality Management 03/1999; 4(2):207–224. DOI:10.1016/S1084-8568(99)00013-9
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    • "Such systems will tend to improve learning via increased storage and transmission capacity, and sometimes lead to more timely and better quality decision making [12]. These positive effects are contingent upon the presence of such factors as user participation, senior management commitment, project management, system performance, training, and validation [35]. Thus the effective implementation of a PKB depends on careful attention to its design, implementation, and support. "
    Human Systems Management 01/1998; 17:281-298.
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    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.
    Expert Systems with Applications 01/1995; 8(1-8):213-228. DOI:10.1016/0957-4174(94)E0011-I · 2.24 Impact Factor
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