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As the world approaches a new millennium, more and more industrial and manufacturing processes are being computerized and rapid retrieval and use of necessary information is vital if an organization is to remain competitive. The present work develops a computerized decision support system to assist in maintenance planning. The system design and analysis, and the decision support system design and development are all developed in an object‐oriented environment. The support system is driven by maintenance performance indices. Both object‐oriented databases and relational databases are used, for transient and permanent entities respectively. A knowledge base with if‐then rules is developed for fault diagnosis and repair. The methodology was applied to a powder coating plant to develop its maintenance decision support system.

Using a decision support system (DSS) delays the decision‐making process and commits the user to the cost of invoking the system. The existing configurations of decision support systems do not guarantee the profitability of the DSS. If the DSS generates messages that the decision maker can anticipate, then the cost and waiting time as a result of invoking the DSS will not be justified. Proposes a decision support system equipped with a knowledge‐based model that tells the decision maker, prior to invoking the DSS, whether or not it is profitable to invoke the DSS; if invoking the DSS is not profitable, then the decision maker will have to base the decision on pure managerial subjective judgement. Uses a numerical example to illustrate the work of the proposed DSS.

The purpose of this paper is to investigate the benefits and the barriers of agent based decision support (ABDS) systems in the supply chain context.

Two ABDS systems have been developed and evaluated. The first system concerns a manufacturing supply chain while the second concerns a service supply chain. The systems are based on actual case companies.

This research shows that the benefits of ABDS systems in the supply chain context include the possibility to increase versatility of system architecture, to improve supply chain visibility, to conduct experiments and what‐if analyses, to improve the understanding of the real system, and the possibility to improve communication within and between organizations in the supply chain. The barriers of ABDS systems in the supply chain context include the difficulty to access data from partners in the supply chain, the difficulty to access data on a higher level of granularity, and the difficulty to retrieve data from other information systems.

The research is explorative in nature therefore empirical data from similar and other research settings should be gathered to reinforce the validity of the findings.

This research provides knowledge and insights on how ABDS systems may be developed and used in the supply chain context and demonstrates its main benefits and barriers.

This research expands the current research of benefits of ABDS systems to the supply chain domain and also addresses the barriers of ABDS systems to a larger extent than previous research. Comparisons to other simulation based decision support systems are also given.

Taking the discipline construction in colleges and universities as the application background, based on the research on data mining technology and decision support system technology, the data generated by university management information system are effectively utilized. The paper aims to discuss these issues.

Based on the Beijing Key Discipline Information Platform as the data source, the decision tree algorithm of data mining is studied. On the basis of decision tree C4.5, the Bayesian theory is applied to the post-pruning operation of the decision tree.

A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with browser/server (B/S) structure is completed and implemented.

A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with B/S structure is completed and implemented.

A decision support system (DSS) is a flexible, interactive, computerized approach intended to support administrators in their decision making activities and which is capable of providing direct, personal support for complex, managerial decisions. This paper presents an overview of DSS's major characteristics which can integrate the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. Following a discussion of its capabilities, the various components of a DSS (database, model base, hardware and user‐system interface) are examined as well as the development tools needed. Examples of the applications of DSSs in two universities provide insight into the benefits a DSS can bring to educational administration. Finally, the paper considers various development and implementation issues pertaining to a decision support system in academic administration.

Presents a foundation for the development of marketing decision support systems (MDSS). Argues that traditional management information systems typically follow a sequential flow through analysis, design, and implementation; MDSS involves a developmental approach emphasizing iteration and evaluation through the various stages. Surmises that MDSS offers marketing managers the advantages of an interactive system to supply them with useful information to help improve decision making.

Management information for decision making is an essential tool for library managers. This paper outlines the European Commission Libraries Programme decision support projects that were brought together under the CAMILE concerted action. The three common themes to emerge from the CAMILE projects are then discussed. The themes are: information for decision making, systems for decision support, and the future challenges of measuring impact and developing performance measures for the elec‐tronic library. To conclude, recommendations for future research in the field of decision support in European libraries are considered.

The purpose of this paper is to explore the applicability of the Decision Making Grid (DMG) and its usefulness, in practice, in the maintenance of rolling stock in the railway industry. The Class 319 fleet operated by First Capital Connect (FCC) is used to demonstrate the application of the DMG.

The level of use of the data recorded in the CMMS, in the decision making process for reviewing, and updating the maintenance policy, was established through discussions with senior management at FCC. Available decision support systems were then researched, with emphasis on the DMG concept. The advantages of using the DMG and its application in the real world using data for the Class 319 fleet were then explored and are discussed in this paper.

This paper discusses the value in applying the DMG concept in the decision‐making process for prioritising systems and the work that should be done to ensure the maintenance policy takes into account the performance of the units of rolling stock against the most important criteria for FCC. Through the research carried out, it was established that the existing CMMS already records the data required for the application of the DMG, although in itself, the CMMS does not have any decision support capabilities.

Although a number of different CMMSs are used in the railway industry, few or none of these are capable of providing decision support for maintenance. This paper explores the use of the DMG concept to demonstrate the use of data recorded in the CMMS to develop a more effective maintenance policy and to determine exactly which maintenance activities need to be carried out in order to remedy the worst performing systems in terms of the most important criteria as identified by the business.

The pressure on libraries to deliver defined services to a high level of efficiency have never been greater. Libraries are feeling the impact of increased use, restricted resources and more accountability. These are overlaid with the development of new technologies which are beginning to provide alternative delivery mechanisms on the one hand and the first real competition to libraries as organisations on the other. The basic premise that libraries are ‘a good thing’ is now coming under question as potential users see new possibilities for information, education and leisure. To meet these challenges and to set new directions requires a range of skills, one of which is the development of improved decision‐making.

With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels.

This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation.

Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper.

The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.