A framework for the establishment of organizational risk indicators

Øien, Knut

doi:10.1016/s0951-8320(01)00068-0

Cited by 129 publications

(39 citation statements)

References 19 publications

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“…Lagging indicators are measures of adverse outcomes such as injuries, accidents, accidents avoided, deviations from the permissible limits of the process, release of chemicals, highlevel alarms, improperly performed procedures, failures of components, and lack of equipment [23]. Leading indicators determine the quality of activities that prevent adverse outcomes [15].…”

Section: Safety Factors Performance and Indicatorsmentioning

confidence: 99%

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Selection and ranking of occupational safety indicators based on fuzzy AHP: A case study in road construction companies

Janaćković

Savić

Stanković

2013

sajie

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This paper presents the factors, performance, and indicators of occupational safety, as well as a method to select and rank occupational safety indicators based on the expert evaluation method and the fuzzy analytic hierarchy process (fuzzy AHP). A case study is done on road construction companies in Serbia. The key safety performance indicators for the road construction industry are identified and ranked according to the results of a survey that included experts who assessed occupational safety risks in these companies. The case study confirmed that organisational factors have a dominant effect on the quality of the occupational health and safety management system in Serbian road construction companies. OPSOMMINGHierdie artikel toon die faktore, prestasies en aanwysers van beroepsveiligheid en 'n metode om beroepsveiligheid aanwysers te kies en te rangskik. Die rangskikking van beroepsveiligheid aanwysers is gegrond op die deskundige evaluasie metode en die wasige analitiese hiërargie proses. 'n Gevalle studie is geloods op padkonstruksie maatskappye in Serwië. Die sleutel veiligheidsprestasieaanwysers is geïdentifiseer en rangskik volgens die resultate van 'n opname onder deskundiges wat die beroepsveiligheid risiko's in hierdie maatskappye assesseer. Die gevalle studie het bevestig dat organisatoriesefaktore 'n daadwerklike effek op die gehalte van die beroepsveiligheid bestuurstelsel in Serwiese padkonstruksie maatskappye het.

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Section: Safety Factors Performance and Indicatorsmentioning

confidence: 99%

“…Based on the literature review and the authors' own research [1][2][3]11,15,16,21,[23][24][25][26][27], the authors propose a structure for factors, performance, and safety indicators (Table 1). This structure is neither complete nor final.…”

Section: Safety Factors Performance and Indicatorsmentioning

confidence: 99%

Selection and ranking of occupational safety indicators based on fuzzy AHP: A case study in road construction companies

Janaćković

Savić

Stanković

2013

sajie

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“…Another representative example is the previously mentioned "Risk Barometer", which was developed within the IO Centre. This tool is influenced by previous analogous methods, such as the ORIM (Organizational Risk Influence Model) [31] and Risk OMT (risk modellingintegration of organisational, human and technical factors) [32].…”

Section: Inheritance From the Centre For Integrate Operations In The mentioning

confidence: 99%

“…For the development of this model, both the Risk Influencing Factor (RIF) approach [31,43] and the Dynamic Risk Analysis approach suggested by Paltrinieri and Khan [44] were taken as a reference. Such approach is also inspired to the aforementioned Risk Barometer [22][23][24] and TEC2O [25].…”

Section: Loss Of Containmentmentioning

confidence: 99%

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

Paltrinieri

Landucci

Rossi

2017

Volume 3B: Structures, Safety and Reliability

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Recent major accidents in the offshore oil and gas (O&G) industry have showed inadequate assessment of system risk and demonstrated the need to improve risk analysis. While direct causes often differ, the failure to update risk evaluation on the basis of system changes/modifications has been a recurring problem. Risk is traditionally defined as a measure of the accident likelihood and the magnitude of loss, usually assessed as damage to people, to the environment, and/or economic loss. Recent revisions of such definition include also aspects of uncertainty. However, Quantitative Risk Assessment (QRA) in the offshore O&G industry is based on consolidated procedures and methods, where periodic evaluation and update of risk is not commonly carried out. Several methodologies were recently developed for dynamic risk analysis of the offshore O&G industry. Dynamic fault trees, Markov chain models for the lifecycle analysis, and Weibull failure analysis may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available. However, the mentioned techniques are not widely applied in the common O&G offshore practice due to several reasons, among which their complexity has a primary role. More intuitive approaches focusing on a selected number of critical factors have also been suggested, such as the Risk Barometer or the TEC2O. Such techniques are based on the evaluation of technical, operational and organizational factors. The methodology allows supporting periodic update of QRA by collecting and aggregating a set of indicators. However, their effectiveness relies on continuous monitoring activity and realtime data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in or around and offshore O&G system. The inheritance from the Centre for Integrated Operations in the Petroleum Industries represents the basis of such study. Such approach may be beneficial for several cases in which (quasi) real-time risk evaluation may support critical operations. Two representative cases have been described: i) erosion and corrosion issues due to sand production; and ii) oil production in environmental sensitive areas. In both the cases, dynamic risk analysis may employ real-time data provided by sand, corrosion and leak detectors. A simulation of dynamic risk analysis has demonstrated how the variation of such data can affect the overall risk picture. In fact, this risk assessment approach has not only the capability to continuously iterate and outline improved system risk pictures, but it can also compare its results with sensor-measured data and allow for calibration. This can potentially guarantee progressive improvement of the method reliability for appropriate support to safety-critical decisions. Direct causes of such events often differed, but...

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“…DOI Several models and methods for incorporating organisational factors in QRAs or probabilistic risk assessments (PRA) have been proposed. Among these are Manager [10], MACHINE (Model of Accident Causation using Hierarchical Influence Network) [11], ISM (Integrated Safety Method) [12], WPAM (The Work Process Analysis Model) [13,14], I-RISK (Integrated Risk) [15][16][17], the -factor model [18], SAM (System Action Management) [19,20], ORIM (Organisational Risk Influence Model) [21,22], and ARAMIS [23]. These models/methods have been developed and described in the literature in the last 15 years.…”

Section: Introductionmentioning

confidence: 99%