The Characteristics of Industrial Safety Risk Management

2020 ◽  
pp. 47-52
Author(s):  
Gyula Szabó
Keyword(s):  
Risk Management ◽  
Industrial Safety ◽  
Safety Risk

Value-based dentistry: Putting the patient first

2016 ◽  
Vol 7 (3) ◽  
pp. 126-129 ◽  
Author(s):  
Sreenivas Koka ◽  
Galya Raz
Keyword(s):  
Risk Management ◽  
Quality Improvement ◽  
Quality Of Care ◽  
Dental Care ◽  
Time Cost ◽  
Safety Risk ◽  
The Cost ◽  
Over Time

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.

Enhancing Safety Risk Management with Quantitative Measures

2017 ◽  
Vol 2603 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Boris Claros ◽  
Carlos Sun ◽  
Praveen Edara
Keyword(s):  
Risk Management ◽  
Management System ◽  
Goodness Of Fit ◽  
Safety Management ◽  
Multinomial Distribution ◽  
Safety Management System ◽  
Safety Risk ◽  
Safety Risks ◽  
Safety Models ◽  
Runway Incursion

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.

scholarly journals Automated Risk Management Systems

2020 ◽  
pp. 11-15
Author(s):  
Amanulla Kadyrov ◽  
◽  
Aziza Kadyrova ◽  
Keyword(s):  
Risk Management ◽  
Maximum Level ◽  
Industrial Safety ◽  
Management Systems ◽  
Risk Modeling ◽  
Logical Scheme ◽  
Industrial Enterprises ◽  
Level Of Automation ◽  
Hazardous Production ◽  
Production Facilities

Creating an industrial safety management system for enterprises requires the development and application of a set of mechanisms that ensure the prevention of accidents and incidents. Risk management systems are one of the most effective mechanisms for industrial safety. However, very little attention has been paid to automation of risk management. This article is devoted to the formation of the conceptual framework for creating automated risk management systems for industrial enterprises with hazardous production facilities. The paper analyzes the structural and logical scheme of risk management. It is shown that one of the most “risk-bearing” components of hazardous production facilities are technical devices. At the same time, the maximum level of automation is possible for technical devices in relation to their maintenance, repair, and risk management tasks. Analysis of the tasks related to risk management shows their complexity and diversity. The solution of these problems is possible through the use of modern information technologies. At the same time, it is necessary to create a unified database of hazardous production facilities, a comprehensive database of regulatory, technical and legal information, automation of risk modeling and analysis of control processes, and decision-making and implementation of processes that minimize risks. At the same time, the maximum level of automation is possible for technical devices in relation to their maintenance, repair, and risk management tasks.

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