JITA

JITA Journal of Information Technology and Applications

Vol. 4 No. 1 (2014): JITA - APEIRON

Bojan Ivetić, Tonćo Marušić, Dragica Radosav

Customer Satisfaction as a Significant Measure of Successful ERP Implementation

Original scientific paper

DOI: https://doi.org/10.7251/JIT1401031I

Abstract

The measuring of implemented ERP system’s efficiency is in any case multidimensional. Various researchers dedicated a lot of attention trying to find the best way to measure the success or the effectiveness of ERP solution. „Customer satisfaction“ as a measure represents the crucial point in creating the model for Measuring the success of implemented ERP systems and therefore it is the subject of this work. In this work we shall see what effect the other measurements will have on the „customer satisfaction“, respecting the correlation between particular crucial categories in creating the model of implemented ERP system’s success.

Keywords: ERP, informational systems, success measurement, customer satisfaction.

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.