Risk-based decision analysis

Consequence Phase 3 Develop Detailed Quantitative Estimate of the impacts of the Accident Scenarios. Sometimes an accident scenario is not understood enough to make risk-based decisions without having a more quantitative estimation of the effects. Quantitative consequence analysis will vary according to the hazards of interest (e.g., toxic, flammable, or reactive materials), specific accident scenarios (e.g., releases, runaway reactions, fires, or explosions), and consequence type of interest (e.g., onsite impacts, offsite impacts, environmental releases). The general technique is to model release rates/quantities, dispersion of released materials, fires, and explosions, and then estimate the effects of these events on employees, the public, the facility, neighboring facilities, and the environment. 

Bohnenblust, H. and Slovic, P. 1998 Integrating Technical Analysis and Public Values in Risk-based Decision Making. Reliability Engineering and System Safety, 59, 151-159. 

Deciding which risk-reduction method to use maybe difficult. In many instances, appropriate decisions can be made without resorting to quantitative techniques. However, in some cases, particularly when the options are costly, quantitative risk analysis (QRA) and risk-based decision-making approaches may be an effective basis for measuring the improvement in safety arising from the proposed options. These approaches can also be used in prioritizing safety improvements and balancing cost and production issues. 

HAZARD IDENTIFICATION RISK ANALYSIS Identify Hazards Evaluate Risks Assess Risks Make Risk-based Decisions Follow Through on Assessment Results Maintain a Dependable Practice... 

For those issues or transportation scenarios requiring more detail and insight than a qualitative approach offers, the next level of analysis is a semi-quantitative evaluation. A semi-quantitative risk analysis includes some degree of quantification of consequence, likelihood, and/or risk level. Once evaluated, through the combination of consequence and likelihood, a risk-based decision can be made from the analysis or additional refinement in consequence or likelihood may be necessary. This refinement of the risk analysis may rely on additional information included as part of a semi-quantitative analysis or, as warranted, on some minimal level of quantitative techitiques described in the next chapter. 

Brandsseter (2002) describes its implementation and the use of risk analysis in the offshore industry using quantitative and qualitative approaches. Brandsseter (2002) work is a contribution to the EC-JCR International Workshop on Promotion of Technical Harmonization on Risk-Based Decision Making (2000) and its format is in response to a set of questions prepared by the organizers of the workshop, where both mentioned models are discussed. 

With this knowledge on hazards, efforts wUl be made to find what is hazard analysis Hazard analysis uncovers the hazards that exist in the workplace (in this case, industrial plant) focusing on the system or project. As discussed previously, in hazard analysis, risk based decisions are taken to develop means to quantify, track, develop mitigation means, and control hazards, foUow up action, verify effectiveness, and communicate. However at first it is necessary to define risk, so in the next clause same is discussed. 

Methods and techniques for measurement, sampling, and analysis Types, sources, and characteristics of hazards, threats, and vulnerabilities Hazard analysis, job safety analysis and task analysis methods Qualitative, quantitative, deductive, and inductive risk assessment methods Risk-based decision-making Risk-based decision-making tools... 

Dillon, R.L., Liebe, R. and Bestafka, T. (2009), Risk-based decision making for terrorism applications, Risk Analysis, 29(3), pp. 321-335. 

In the simplest terms, a fault-tree for risk analysis requires the following information probabiUty of detection of a particular anomaly for an NDE system, repair or replacement decision for an item judged defective, probabiUty of failure of the anomaly, cost of failure, cost of inspection, and cost of repair. Implementation of a risk-based inspection system should lead to an overall improvement in the inspection costs as well as in the safety in operation for a plant, component, or a system. Unless the database is well estabUshed, however, costs may fluctuate considerably. 

In a tiny fraction of cases, a quick formula can be used. For most cases, the analysis uses an options tree, with one leaf per possible outcome. However, this falls prey to the curse of dimensionality —the number of leaves on the tree grows exponentially in the number of risk and decision dimensions considered. Thus only a limited, simple set of situations can be optimized in this way because one has to severely limit the decisions and risks that are considered. Tools available to help automate and simplify options analysis, widely used in pharmaceutical project evaluation, include Excel addons such as R1SK [11] and more graphically based solutions such as DPL [12]. Both of these support the creation and evaluation of decision trees and of influence diagrams Figure 11.2 shows a simple example of each of these. A primer in applied decision theory is Clemen s book Making Hard Decisions, other sources may be found in the website of James Vornov, Director of Clinical Research at Guildford Pharmaceuticals, a recent convert to decision theory for options analysis [13]. 

Byrd, D. M. Ill and Cothern, C. R. (2000). Introduction to Risk Analysis a Systematic Approach to Science-Based Decision Making. Dallas, TX Government Institutes. 

Assessment of process capability and statistical process control brings the ability to distinguish between a stable and un-stable process and provides a means to distinguish between different causes of variability, e.g., common cause, special cause, structural (e.g., seasonal), and tampering (e.g., deliberate or unintentional). Process understanding, quality by design and capability analysis can facilitate risk-based regulatory decisions for continuous improvements ... 

Therefore, risk management decisions are currently based on common sense, a thorough analysis of the food production process, and the risk of the allergen as perceived by the company. Driven by fear for (legal) claims there may be a tendency to use a disclaimer (for instance may contain ) on the label when the chance of cross-contamination is realistic (Hefle et al. 2007). These disclaimers are not always based on sound risk analysis. 

A technical-based approach follows the view that risks should be reduced to achieve an adequate level of safety, but does not require a socio-economic analysis of alternative risk management options. Standards are set according to available technology to ensure that certain margins of safety are not exceeded. A risk-benefit approach bases decisions on the results of socio-economic and stakeholder consultation. 

ASTM s risk-based corrective action (RBCA) uses a tiered approach to data collection and analysis in supporting decisions on site assessment and response to petroleum. The RBCA procedure begins with the assessment of the site (see Figure 5-2 for RBCA process flow-chart). 

Analysis of the Risk Assessment. In answering the question of whether the risk assessment is adequate for basing a risk management decision, a number of issues arise. Some are listed below. 

Tervonen T, van Valkenhoef G, Buskens E, Hillege HL, Postmus D. A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis. Statistics in Medicine 2011 30(12) 1419-1428. 

ABSTRACT The paper presents a decision support system (DSS) for evaluation of risk pipeline risk. The system is able to support risk assessment and risk ranking of sections of natural gas pipelines. The DSS uses an architecture based on a data base, a model base and user interface. The model base has been built based on Multi-Attribute Utility Theory. The multi-attribute approach analysis risks in three dimensions of impact. These dimensions are human, financial and environmental impact. The way in which the model translates decisionmakers preferences into risk management decisions is highlighted. The paper presents the DSS, including some dialogue modules of the system based on real appUcations. 

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