Safety Risk Management in Complex Systems

Author(s):  
Gulsum Kubra Kaya
2016 ◽  
Vol 7 (3) ◽  
pp. 126-129 ◽  
Author(s):  
Sreenivas Koka ◽  
Galya Raz

What does ‘value’ mean? In the context of dental care, it can be defined as the quality of care received by a patient divided by the cost to the patient of receiving that care. In other words: V =Q/C, where Q equals the quality improvement over time, which most patients view in the context of the outcome, the service provided and safety/risk management, and C equals the financial, biological and time cost to the patient. Here, the need for, and implications of, value-based density for clinicians and patients alike are explored.


Author(s):  
M. Kiwan ◽  
D.V. Berezkin ◽  
M. Raad ◽  
B. Rasheed

Statement of a problem. One of the main tasks today is to prevent accidents in complex systems, which requires determining their cause. In this regard, several theories and models of the causality of accidents are being developed. Traditional approaches to accident modeling are not sufficient for the analysis of accidents occurring in complex environments such as socio-technical systems, since an accident is not the result of individual component failure or human error. Therefore, we need more systematic methods for the investigation and modeling of accidents. Purpose. Conduct a comparative analysis of accident models in complex systems, identify the strengths and weaknesses of each of these models, and study the feasibility of their use in risk management in socio-technical systems. The paper analyzes the main approaches of accident modeling and their limitations in determining the cause-and-effect relationships and dynamics of modern complex systems. the methodologies to safety and accident models in sociotechnical systems based on systems theory are discussed. The complexity of sociotechnical systems requires new methodologies for modeling the development of emergency management. At the same time, it is necessary to take into account the socio-technical system as a whole and to focus on the simultaneous consideration of the social and technical aspects of the systems. When modeling accidents, it is necessary to take into account the social structures and processes of social interaction, the cultural environment, individual characteristics of a person, such as their abilities and motivation, as well as the engineering design and technical aspects of systems. Practical importance. Based on analyzing various techniques for modeling accidents, as well as studying the examples used in modeling several previous accidents and review the results of this modeling, it is concluded that it is necessary to improve the modeling techniques. The result was the appearance of hybrid models of risk management in socio-technical systems, which we will consider in detail in our next work.


Author(s):  
Boris Claros ◽  
Carlos Sun ◽  
Praveen Edara

At the airfield in hub airports, many activities occur that involve a range of participants, including various-size aircraft, ground vehicles, and workers. The safety management system is FAA's approach for systematically managing aviation safety. A major component of the safety management system is safety risk management (SRM), which entails analysis, assessment, and control of safety risks, including risks on the airfield. Current SRM has few specific safety models to estimate the likelihood or frequency of risks. This paper presents an example for development and incorporation of safety models into SRM. Specifically, it discusses safety models for runway incursion that use the following variables: total and general aviation operations, length of runway by type, number of taxiway intersections, snowfall, precipitation, number of hot spots, and construction activity. Categorization and processing of data were significant because each variable used could take on multiple forms, and some types of data involved review of airfield diagrams. The data used were from 137 U.S. hub airports for 2009 through 2014. For modeling, the negative multinomial distribution was used because it proved suitable for representing overdispersed data such as runway incursion frequency. Performance of the models was assessed through the goodness-of-fit measures of log likelihood, overdispersion, and cumulative residual plots. Models were developed for five severity categories of runway incursions and three types of surface events. The safety modeling approach presented here can serve as a foundation for development of other safety models that can be integrated into SRM to enable quantitative analysis of safety risks.


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