Expert systems and their applications in production and operations management

Recently, the world competitive environment has forced organizations to reexamine and reevaluate their manufacturing strategies. Large capital investments in machine tools and management control systems are being made. The application of expert systems in the production environment has been steadily increasing. This paper examines the concepts of expert systems and reviews the literature pertinent to the areas of application in production management such as scheduling, layout planning and tools used in applying these concepts such as simulation and optimization. It has been concluded that due to increasing automation requirements in manufacturing and the intensified organizational competitive environment, the decision making process itself will have to be automated. It has been suggested that expert systems could be a vehicle in achieving this. This paper examines the applications of expert systems in production and operations management.The technology of expert systems is still in its infancy and many researchers see learning as a major problem. As expert systems become more readily available, the management issues of safety, validity and reliability will become more crucial. The compensation on transferring of knowledge is unclear. Also, the fear of replacement of white collar jobs is growing.     

The infusion of expert systems in banking: An exploratory study

Numerous studies can be found on expert systems (ES) as a specific IT in the literature. However, their focus has been mainly on system development from a technical perspective. Based on an empirical study on expert systems diffusion in 20 British banking organizations, this paper reports the findings from the study. The study finds that ES infusion concentrated in specific processes that require extensive knowledge in banking. There is a strong organizational perspective towards expert systems development in these banking organizations rather than the technical perspective. ES diffusion and top management commitment are closely associated. The existence of IT strategy aligned with business strategy (and/or with an A1 element within it) is not a good predictor for ES adoptions. Three cases of expert systems applications in banking are given in the paper. These results are useful for practitioners in managing their intelligent systems projects and researchers for further studies in this area.     

Expert systems and organizations: Predictions and evidence

Expert systems have recently become more commonplace in the United States workplace. Although little is known about the impacts of expert systems in the workplace, many predictions about the nature of these impacts, and the effects of expert systems generally, have been made. We examine some of these predictions, noting that they are very similar to predictions made about the effects of conventional data processing in organizations. We examine how most of the predictions about data processing have been shown to be unfounded, based on empirical research. We also show, using the limited empirical evidence available relating to expert systems and their organizational effects, that most of the predictions made about them are also unfounded.     

Expert systems in organizations: Legal and ethical considerations

Information technology is not only driving productivity worldwide, but also is creating many structural and processing changes within organizations. Alterations in people's work habits and in their relationships in the workplace create many questions, which may become critical organizational problems in the future if they are not identified early. These questions are ethical or legal in nature, and their understanding is vitally important Within the realm of artificial intelligence systems, a significant amount of attention is received by the domain of expert systems, which enhance or replace existing organizational decision‐making processes. This article highlights some of the basic considerations arising within the organizational setting as a result of expert systems utilization.     

Operationalising the implementation puzzle: an argument for eclecticism in research and in practice

Efforts to implement organizational information systems (IS) have evolved through the years from purely technical processes to multi-faceted organizational change activities. Largely in reaction to implementation failures, numerous research models have been developed to prescribe appropriate implementation strategies and tactics. A multiple of such contributions has made the theory quite diverse and somewhat chaotic. This paper presents a survey of implementation models, research approaches, and investigation methods that have surfaced in the literature of the field. Using an implementation framework of Swanson (1988), an eclectic perspective of IS research, and an expert knowledge integration approach, a perspective is proposed that demonstrates the possibility of a unified view of IS implementation theory.     

Expert systems adoption. An analytical study of managerial issues and concerns

The experience of introducing expert systems technology into six automotive component manufacturing organizations in the mid-western United States is described. The domain of expert system application as well as the procedural and organizational hurdles that were encountered are also documented. An overview of relevant literature shows the shortage of studies of this kind. A synthesis is provided of the study observations and implications for better management of expert systems adoption in manufacturing.     

Expert Recommender: Designing for a Network Organization

Recent knowledge management initiatives focus on expertise sharing within formal organizational units and informal communities of practice. Expert recommender systems seem to be a promising tool in support of these initiatives. This paper presents experiences in designing an expert recommender system for a knowledge-intensive organization, namely the National Industry Association (NIA). Field study results provide a set of specific design requirements. Based on these requirements, we have designed an expert recommender system which is integrated into the specific software infrastructure of the organizational setting. The organizational setting is, as we will show, specific for historical, political, and economic reasons. These particularities influence the employees’ organizational and (inter-)personal needs within this setting. The paper connects empirical findings of a long-term case study with design experiences of an expertise recommender system.     

Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective

Expert systems are an evolving technology with the potential to make human expertise widely and cheaply available. The literature describing the development of expert systems generally assumes that experts willingly give up their knowledge. This is unrealistic and may be a reason why most expert system projects fail. This paper explores the problem of unwilling experts from the perspective of a knowledge engineer building an expert system. The link between knowledge and organizational power is established and human motivation theories are discussed. Finally, a new motivational approach is introduced to help the knowledge engineer deal with unwilling experts.     

Decision making,computer attitudes and expert systems: What is our direction?

Expert systems have been concerned with applications dealing with medical diagnosis, mineral exploration, and computer configuration, with some efforts relatively successful in achieving results at least as good as human experts. Today, much is being written about these systems and managerial decision-making activities in organizations and the positive impact that they can have in these situations. However, it appears that expert systems could become somewhat of a panacea for some organizational ailments as research, development, and marketing of them continues at a fast pace. What may be forgotten in this technological thrust is the individual decision maker and his/her unique style of decision making which could affect acceptance and use of these systems. Another important consideration is the attitude people have toward computers and computer systems, which along with decision-making styles could certainly affect expert system effectiveness and ultimate success in organizations. This paper provides a discussion of potential problems that could occur regarding individual decision making and attitudes and their relationships to these systems.     

Knowledge organization and its role in representation and interpretation for time-varying data: the ALVEN system

The so-called "first generation" expert systems were rule-based and offered a successful framework for building applications systems for certain kinds of tasks. Spatial, temporal, and causal reasoning, knowledge abstractions, and structuring are among topics of research for "second generation" expert systems. It is proposed that one of the keys for such research is knowledge organization. Knowledge organization determines control structure design, explanation and evaluation capabilities for the resultant knowledge base, and has strong influence on system performance. We are exploring a framework for expert system design that focuses on knowledge organization, for a specific class of input data, namely, continuous, time-varying data (image sequences or other signal forms). Such data are rich in temporal relationships as well as temporal changes of spatial relations, and are thus a very appropriate testbed for studies involving spatio-temporal reasoning. In particular, the representation formalism specifies the semantics of the organization of knowledge classes along the relationships of generalization/specialization, decomposition/aggregation, temporal precedence, instantiation, and expectation-activated similarity. Á hypothesize-and-test control structure is driven by the class organizational principles, and includes several interacting dimensions of search (data-driven, model-driven, goal-driven temporal, and failure-driven search). The hypothesis ranking scheme is based on temporal cooperative computation, with hypothesis "fields of influence" being defined by the hypothesis' organizational relationships. This control structure has proven to be robust enough to handle a variety of interpretation tasks for continuous temporal data. A particular incarnation, the ALVEN system, for left ventricular performance assessment from X-ray image sequences, will be summarized in this paper.     

Accounting information systems and organization learning: A simulation

Accounting Information Systems may facilitate or impede organizational learning. Critical attributes of accounting systems that have the potential to affect organizational learning include: (1) characteristics of the information environment, whether uniform, dispersed or clustered importance weights; (2) information distribution, whether overlapping or segregated information; and (3) information coordination mechanisms, whether expert teams, majority voting teams or hierarchies. Organizational learning and performance was simulated in the following manner: (i) the organization was faced with a continuous sequence of repetitive but not identical problems; (ii) the organizational task was subdivided between analysts; and (iii) analysts learned by basing their decisions on the relationship found between information available to them and organizational outcomes. Simulation results indicated that learning in flatter (team) organizations is generally more accurate than in hierarchical organizations. Learning is also faster with majority teams than hierarchies, but slower with expert teams. Overlapping accounting information transmission between agents was found to offer only limited benefits. These findings have implications for the design of accounting information systems in organizations.     

Expert systems on microcomputers*

Abstract: The increasing popularity of expert systems has led to a demand to apply expert systems technology in a wide variety of computing environments. As a result, various efforts have been made to implement expert systems on microcomputers. This article reviews some of the ongoing work on tools for the development of microcomputer-based expert systems. Some specific application areas are noted, and a brief discussion of the advantages and disadvantages of implementing expert systems on microcomputers is presented.     

A knowledge-based approach to design for manufacturability

In the light of growing global competition, organizations around the world today are constantly under pressure to produce high-quality products at an economical price. The integration of design and manufacturing activities into one common engineering effort has been recognized as a key strategy for survival and growth. Design for manufacturability (DFM) is an approach to design that fosters the simultaneous involvement of product design and process design. The implementation of the DFM approach requires the collaboration of both the design and manufacturing functions within an organization. Many reasons can be cited for the inability to implement the DFM approach effectively, including: lack of interdisciplinary expertise of designers; inflexibility in organizational structure, which hinders interaction between design and manufacturing functions; lack of manufacturing cost information at the design phase; and absence of integrated engineering effort intended to maximize functional and manufacturability objectives. The purpose of this research is to show how expert systems methodology could be used to provide manufacturability expertise during the design phase of a product. An object- and rule-based expert system has been developed that has the capability: (1) to make process selection decisions based on a set of design and production parameters to achieve cost-effective manufacture; and (2) to estimate manufacturing cost based on the identified processes. The expertise for primary process selection is developed for casting and forging processes. The specialized processes considered are die casting, investment casting, sand casting, precision forging, open die forging and conventional die forging. The processes considered for secondary process selection are end milling and drilling. The cost estimation expertise is developed for the die casting process, the milling and drilling operations, and the manual assembly operations. The results obtained from the application of the expert system suggest that the use of expert systems methodology is a feasible method for implementing the DFM approach.     

Three levels of benefits in expert systems

Abstract: This paper is not about knowledge representation, nor knowledge structures, nor knowledge elicitation, nor even expert systems methodology. It is about the use of expert systems in their working context. Little has been written about the usage of expert systems from a theoretical point of view. What has been written from a practitioner's point of view often lacks structure, and thus can make only minor contributions to understanding and predicting the usage of expert systems.
In an attempt to fill this gap, this paper proposes a model of benefits of expert systems. The model has three levels and benefits of various kinds are classified into three types. The paper presents a deductive argument for the model, rather than an inductive one, since full empirical studies have yet to be carried out. However, a case study is outlined that indicates the applicability of the model.     

Using human factor guidelines for developing expert systems

Abstract: Although MIS professionals had predicted that expert systems would improve productivity enormously, and a number of expert system application success stories have been reported, the expert system revolution has not yet happened. Moreover, there have been many cases, less well publicised, where expert systems have failed. Most problems concerning expert systems failure stem from non-technical issues such as cognitive and psychological problems, rather than such purely technical issues as an inference engine and an expert system shell. Here, the major reasons for expert systems failure and the need to consider human factors are discussed. We then propose human factor principles that can help designers handle most of these non-technical problems and many technical ones elegantly, improving the performance and acceptance of expert systems. Finally, some human factor guidelines for expert systems are presented so that these considerations may be incorporated into expert systems development in a clear and comprehensible manner.     

Expert systems for knowledge management: crossing the chasm between information processing and sense making

Based on insights from research in information systems, information science, business strategy and organization science, this paper develops the bases for advancing the paradigm of AI and expert systems technologies to account for two related issues: (a) dynamic radical discontinuous change impacting organizational performance; and (b) human sense-making processes that can complement the machine learning capabilities for designing and implementing more effective knowledge management systems.     

ADO在故障诊断专家系统中的应用

作为新型的数据库访问技术，简要介绍了ADO技术及其特点，重点介绍了ADO技术在专家系统中的应用，利用ADPO技术实现了对两个数据源的访问，为建立在自动测试系统上的故障诊断专家系统提供了入口参数，最后用Visual C 语言实现了编程。     

Expert systems designed for maintenance

Constraints for knowledge are proposed as a tool for constructing maintainable expert systems. A simple taxonomy of knowledge constraints is given. It is shown that a special class of knowledge constraints, coupled with the referential integrity constraint, together provide the necessary foundation for a mechanism to substantially automate the expert systems maintenance process provided that the knowledge has been normalised. The normalisation of knowledge is described and its inherent value to the maintenance process is illustrated. An experimental expert systems design and maintenance tool based on this approach has been built.     

Expert systems in manufacturing: Promises and perils

Expert systems play a key role as a tool to enhance productivity, improve quality, increase profits and capture expertise in many business and industrial environments. Although the potential of expert systems is well-understood, it has been a challenging, and often elusive, task to make an expert system operational in the “real-world” environment. One of the major reasons why many expert systems fail to be integrated into the operational environment is a lack of knowledge and techniques on how to test an expert system. The implications of an inadequately-tested expert system in a manufacturing environment are serious. In this paper, we take a look at the many applications of expert systems in manufacturing and utilize a three-pronged approach to develop robust and reliable expert systems.