Youngohc Yoon

Exploring the factors associated with expert systems success

As the widespread use and company dependency on expert systems (ES) incease, so does the need to assess their value and to ensure implementation success. This study identifies and empirically tests eight major variables proposed in the literature as determinants of ES success, in this case measured in terms of user satisfactions. IBMs Corporate Manufacturing Expert Systems Project Center collected information from 69 project managers to support the study. The results clearly support the hypothesized relationships and suggest the need for ES project managers to pay special attention to these determinants of ES implementation success. ES success is directily related to the quality of developers and the ES shells used, end-user characteristcs, and degree of user involvement in ES development, as each has been defined in this study. For exploratory purposes, the component items for each of these major variables were correlated with the components of user satisfaction. Based on the results, several recommendations are proposed for ES project managers to enhance the likelihood of project success, including: adding problem difficulty as a criteriaon for ES application selection; increasing ES developer training to improve people skills; having the ability to model and use a systems approach in solving business problems; sharping end-user attitudes and expectations regarding ES; improving the selction of domain experts; more thoroughly understanding the ES impact on end-user jobs; restricting the acquistion of ES shells based on a rpopsoed set of criteria; and ensuring a proper match of ES development techniques and tools to the business problem at hand.

Predicting stock price performance: a neural network approach

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. A neural network method may be able to enhance an investors forecasting ability. The purpose of this paper is to examine the capability of a neural network method and compares its predictive power with that of multiple discriminant analysis methods.<<ETX>>

Integrating artificial neural networks with rule-based expert systems

Abstract The Rule-Based (RB) and the Artificial Neural Network (ANN) approaches to expert systems development have each demonstrated some specific advantages and disadvantages. These two approaches can be integrated to exploit the advantages and minimize the disadvantages of each method used alone. An RB/ANN integrated approach is proposed to facilitate the development of an expert system which provides a “high-performance” knowledge-based network, an explanation facility, and an input/output facility. In this case study an expert system designed to assist managers in forecasting the performance of stock prices is developed to demonstrate the advantages of this integrated approach and how it can enhance support for managerial decision making.

Selection of a good expert system shell for instructional purposes in business

Abstract Expert systems (ES) have become very important tools in making decisions in business. In order to meet the rising demand for both technical and managerial skills for ES, many business schools have started to use ES shells to teach students the concepts and skills necessary to develop various ES applications, but little research has been done in evaluating expert shells for instructional purposes. This paper develops a model for selecting the most appropriate expert shell as an instructional tool for an ES course. The instructional objectives of incorporating expert shells in a learning environment are discussed and a set of evaluation criteria are developed. Then an evaluation model is presented; it is to be used to select the best shell under different class environments. An illustrative example is also presented.

Exploring expert system success factors for business process reengineering

Abstract Business process reengineering (BPR) has become the buzzword representing dramatic changes to the business processes of organizations trying to quickly preempt or react to market opportunities and competition. Much of the changes are enabled by computer-based technology such as expert systems (ES) providing a unique opportunity to study significant implementations of the technology within a relatively short time. Eight ES implementation success factors proposed in the literature were empirically tested in this study in terms of their direct and indirect importance to the benefits from using ES in BPR. Sixty-two ES applications within E.I. Dupont de Nemours dealing with business process changes significant enough to be called BPR were used. Despite the relatively small sample size, four of the eight success factors were corroborated: user satisfaction with the ES, the difficulty of the business problem addressed, the degree of user involvement in the ES implementation process, and characteristics of the ES shells.

Selecting expert system development techniques

Abstract The widespread development of Expert System applications with a wide variety of characteristics raises a risk that development techniques and application types are not properly matched. This could lead to ES development and maintenance problems, and unnecessary costs for the organization. Four widely used ES development techniques are discussed in terms of basic characteristics and their strength and limitations. The appropriate application of each technique is prescribed and the commercially available tools supporting the techniques are presented. IS managers and ES developers can use the information acquired and properly match ES development tools to applications targeted for ES use in their development.

Empirically testing ES success factors in business process reengineering

Abstract Previously proposed success factors for Expert Systems implementation are field tested in the context of business process reengineering (BPR) projects. Due to its nature mimicking human expert behavior, ES technology applications in BPR provide a unique opportunity to study major organization changes within a relatively short time. Using Pearson correlations and multivariate regression analysis, eight ES implementation success factors proposed in the literature were empirically tested in this study in terms of their importance to the BPR benefits derived from the application. Sixty-two ES applications within E.I. Dupont de Nemours and Company dealing with business process changes significant enough to be called BPR were used. Despite the relatively small sample size, six of the eight success factors were corroborated: user satisfaction with the ES, the importance and difficulty of the business problem addressed, user attitudes toward ES technology and the particular ES project, the degree of user involvement in the ES implementation process, and the ES developer(s) skills.

Artificial neural networks: an emerging new technique

The artificial neural network is at the heart of an emerging technique, which many academicians and practitioners are using very productively due to its high performance in addressing complex problems. Although the ANN has not yet reached its full potential, the technique has demonstrated the capability of enhancing performance in a broad range of problems. This article presents an overview of the artificial neural network, a taxonomy of its learning paradigm, and its application areas.

Success factors for manufacturing expert system development

Abstract As a testimony to their success, the use of Expert Systems (ES) has increased dramatically within and among manufacturing organizations. As the investment in this important area grows, so does the need for an assessment of their value. IBMs Corporate Manufacturing Expert Systems Project Center collected information from 55 project managers regarding the level of user satisfaction and six of the important factors leading to ES development success. Based on the existing literature, six hypotheses regarding the determinants of ES development success as measured by user satisfaction were tested. The results clearly support the proposed relationships between the variables and suggest the need for ES project managers to pay special attention to these determinants of user satisfaction with ES. The report provides recommendations to ES project managers for enhancing the likelihood of project success.

Development of a case-based expert system: Application to a service coordination problem

Abstract Researchers have recently begun utilizing the case-based reasoning (CBR) technique in the construction of expert systems. Instead of developing a knowledge base that contains explicit rules, CBR involves developing a case with prior cases or example. Retrieving prior cases relevant to the current problem and deciding on a solution on the basis of the outcome of previous cases constitute the use of such an expert system. A major advantage of CBR is that in domains that already have much of the required knowledge in the form of cases a case-based expert system can be easily developed. The purpose of this article is to illustrate the CBR technique as applied to the problem of emulating the decision process of service coordinators. This article describes the architecture of a case-based expert system and the detailed methodology of applying the CBR technique. The benefits and pitfalls of applying the technique in the development of an expert system are also discussed.