DEX is a qualitative multi-attribute decision modeling methodology that integrates multi-criteria decision modeling with rule-based expert systems. The method was conceived in 1979. Since, it has been continuously developed and implemented in a wide range of computer programs that have been applied in hundreds of practical decision-making studies. Here we present its main methodological concepts, contributions to the theory and practice of decision support, and outline a history of its development and evolution.
F.02 Acquisition of new scientific knowledge
COBISS.SI-ID: 26664999The purpose of this research is to track and reduce risks so as to prevent errors within the process of health care. The aim is to design an organizational information model using error revention methods for risk assessment In order to assess the risk of errors, the Health Care Failure Mode and Effect Analysis is used. To determine the causes of the errors, the Root Cause Analysis is used. Results of the process analysis following corrective measures shows that the risk assessment of individual error causes reduced by73.6 percent. Re-evaluation of the risks to the whole process shows that the overall risk score was decreased by 45.5 percent. The proposed model has a significant impact on professional attention, communication and information, critical thinking, experience and knowledge. The average impact of information communication technology on the reduction of medication administration errors is 56 percent. These findings represent an increase in the quality of care. The results of our research are theoretically and practically useful and verifiable in other environments, if the level of the organizational culture and the culture of recording errors in combination with the precise recording of data to assess the risk of errors in the process. The model provides a standardized data format that can be used for the purpose of defining factors for the occurrence of errors, for developing a base of knowledge for learning from mistakes and for continuous verification and adaptation to changes in the environment in order to prevent errors.
F.02 Acquisition of new scientific knowledge
COBISS.SI-ID: 2963941In today's highly competitive environment, maintenance, quality, and productivity are essentially related components and very important operational issues for a modern, successful, economic, and profitable production system. The focal point in this paper emerges from the lack of understanding how various quality management approaches and practices can contribute to the overall maintenance performance. The aim of this study is therefore, to define the impact of quality management practices on maintenance performance. The questionnaire survey was carried out among Slovenian organisations in order to address the research problem. Several statistical analysis methods including correlation analysis as well as regression analysis are utilised to accomplish the objective of this study. Results of the study indicate that quality management practices incorporated into maintenance processes have positive impact on maintenance performance. We conclude that these results can benefit to organisations seeking for an approach how to improve maintenance performance. This study also contributes to the literature by providing an insight into deployment of quality management practices into maintenance processes.
F.02 Acquisition of new scientific knowledge
COBISS.SI-ID: 7243027