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Purpose – This paper aims to deal with the development of a risk management application for the modelling, optimal adaptation and implementation of an ERP system.Design/methodology/approach – This paper presented a risk management application for the modeling, optimal adaptation and implementation of an ERP system. The application was tested with the operations and capabilities of the ERP commercial package “SINGULAR Enterprise (SEn)” of the Greek Software House DELTA‐SINGULAR S.A.Findings – The functional result of this application was proved to support considerably the management of risk within the implementation of the ERP system.Originality/value – To the best knowledge of the authors there is no other current generic research in this technological field concerning small or medium‐sized enterprises. With the development of this application, the goals mentioned in the conclusions were achieved.

PurposeTo control and reduce the time and the cost of implementation and to achieve optimal adaptation of the processes and structures of the enterprise in an ERP environment.Design/methodology/approachThis paper presents a fundamental methodology based on goal directed project management (GDPM) principles which supports the complicated process of implementation and optimal adaptation (IOA) of an ERP system.FindingsBy implementing this methodology it was found that it is able to control and reduce the time and the cost of implementation and to achieve optimal adaptation of the processes and structures of the enterprise in an ERP environment.Research limitations/implicationsBased on the above methodology a system must be developed, which will aim at providing the project manager with the ability to create all the necessary elements for the management and realization of the project and to lead the consultant during the IOA process of ERP systems.Practical implicationsThis methodology was connected to, and tested with the operations and capabilities of an ERP commercial package in one of the leading software houses in Greece. The functional result of this methodology was proved to support considerably the process of IOA of SEn in the companies‐customers.Originality/valueThis paper provides a generic research in this technological field concerning small or medium‐sized enterprises based on the GDPM principles which aims at the facilitation and effective guidance for the installation process IOA in all stages and in all levels of analysis.

Environmental legislation and its enforcement has undoubtedly forced the textile industry to be rather cautious in selecting the appropriate processes and equipment. The most efficient, economic and minimal environmental pollution processing methods will be increasingly demanded throughout the 1990s. However, the majority of the textile industry consists of small and medium enterprises, where the lack of expertise on the use of best available techniques leads to levels of operation far away from the optimal. An expert system for a priori economic evaluation of potential interventions in the textile manufacturing processes and equipment is necessary to support decision making in the management level. This paper presents such an expert system, designed and implemented in four stages: formal description of the key factors that affect the dyeing process in the textile industry, development of models for the representation of relevant information, development of models for the representation of knowledge and integration of the above‐mentioned models in a unified information system that supports the decision‐making process in the management of textile enterprises.

Some analysts have claimed that the volatility of an asset is caused solely by the random arrival of new information about the future returns from the asset. Others have claimed that volatility is mainly caused by trading. In any case it is a common belief that volatility is of great importance in finance and it is the factor that plays the most important role in determining option prices. This paper discusses the development of a decision support system (D‐TIFIS) for options trading based on volatility forecasting. In order to evaluate the system, data were used from the Greek FTSE/ASE 20 stock index as well as at the money call and put prices on the specific index.

Knowledge management (KM) has recently received considerable attention in the computer information systems community and is continuously gaining interest by industry, enterprises and government. Decision support and KM processes are interdependent activities in many organizations. In all cases, decision makers always combine different types of data and knowledge available in various forms in the organization. One of the key – but also criticized – building blocks for advancing this field of knowledge management and consequently supporting the decision making is artificial intelligence (AI). In this framework, this paper aims to improve understanding of AI towards knowledge management. It examines and discusses both the potential and the limitations of basic AI technologies in terms of their capability to support the KM process and shares thoughts and estimations on further research on the development of the next generation decision support environments.

A great challenge for today’s companies is not only how to adapt to the changing business environment but also how to gain a competitive advantage from the way in which they choose to do so. As a basis for achieving such advantages, companies have started to seek to improve the performance of various operations. Forecasting is one of them; it is important to firms because it can help ensure that effective use of resources is made. In the market there are a number of off‐the‐shelf system products, which provide forecasts. The new trend, of moving traditional software packages to Web services, has pushed forecasting to a new dimension, named by the authors as “e‐forecasting”. In this paper, a first approach to e‐forecasting is made by throwing light on several aspects and a survey is presented which aims at identifying existing Web forecasting services.

Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems (DSSs) within the organization is critical. Considering the imprecise or fuzzy nature of the data in real‐world problems, it becomes obvious that the ability to manage uncertainty turns out to be a crucial issue for a DSS. In this framework, this paper discusses the key role of fuzzy logic (FL) in the DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research.

Knowledge management has recently received considerable attention in the computer information systems community and is continuously gaining interest from industry, enterprises and government. As we move towards building knowledge organizations, knowledge management in combination with information management will play a fundamental role towards the success of transforming individual knowledge into organizational knowledge. In this framework, this paper discusses the key concepts of human‐computer interaction in knowledge management, identifies new challenges of knowledge management for Web‐based business and proposes a “user agent architecture” for knowledge management in e‐learning environments. User agents use artificial neural network technology and can be used in various e‐learning or e‐training environments, in order to provide them with means of managing information stored, filtering content and enabling better knowledge adoption on behalf of their users.

Presents the NUMASS Web‐based system, which was developed for the needs of the European Commission (designed to be used mainly by AIDCO‐A5). Its main objective is to improve the management and monitoring of Tacis Nuclear Safety Programme, by showing in a graphical way the progress of projects/works and prompting for corrective actions.

In this paper we consider the use of ontologies as the basis for structuring and simplifying the process of constructing real‐time problem‐solving tools, focusing specifically on the task of production scheduling. In spite of the commonality in production scheduling system requirements and design, different scheduling environments invariably present different challenges (e.g. different constraints, different objectives, different domain structure, etc.). The proposed methodology for building ontologies used for production scheduling systems represents a synthesis of extensive work in developing constraint‐based scheduling models for a wide range of applications in manufacturing and production planning. Since the effective modeling is one of the most important and difficult steps in the development of reliable information systems, and taking into consideration the fact that the general problem of the production scheduling in the industries is very difficult and still unsolved, one can easily estimate the merit of this methodology.

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating advanced systems which would simulate human meditation and reasoning. Of all the modern technological quests, this research to create artificially intelligent computer systems has been one of the most ambitious and fascinating. Although attempts had been made more than 30 years ago to develop and apply such systems to the medical sciences, the field languished for decades. In this framework, this paper aims to share thoughts and estimations on the important role of expert systems in medicine and address their future as well as the trends that are foreseen in this area.

Within the last decade many projects have been implemented in order to provide technical assistance (TA) to countries which have initiated their transition from centrally planned to market economies (mainly countries of Eastern Europe being previously under the Soviet regime). The “monitoring and evaluation” function (M&E) helps in seeing whether each TA project is being developed as per its design and achieves its objectives by providing management information on the project’s implementation. In this framework, the necessity for the creation of an overall M&E database system emerged, with the main objective to improve not only the management of TA projects but also the design and planning of future TA projects.