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Vol. 5 No. 2 (2018): JITA - APEIRON

Meltem Ozturan, Birgul Basarir-Ozel, Ezgi Akar

A Review on Methods for the Assessment of Information System Projects

Original scientific paper

DOI: https://doi.org/10.7251/JIT1502117O

Abstract

Recently, it is inevitable that businesses invest in many information system (IS) projects in order to gain a competitive advantage within the internal industry and global environment. The important point is the selection of the appropriate IS environment, hence the optimal IS investment methods with respect to changing technological needs. In this respect, both empirical and conceptual studies are reviewed to identify the relevant IS/IT investment methods. After an extensive literature review, 51 relevant articles are identified. The IS/IT investment methods studied in these articles are classified and examined within the three categories: financial, non-financial, and hybrid. The results reveal that most of studies focus on a mixed usage of financial and non-financial methods called hybrid methods, whereas financial methods are used more frequently when compared to non-financial methods during the selected research period. On the other hand, the usage of pure financial methods decreases in recent years, while the usage of hybrid and non-financial methods increases in the same period.

Keywords: IS/IT investment methods, IS/IT investment, fi nancial methods, non-fi nancial methods, hybrid methods

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.