Robust Risk Analysis Research Articles

Herbal Pharma Inc.: Conducting an Effective Group Audit*

ABSTRACTAs the relative weight of global economic activity continues to shift toward non‐OECD countries (OECD 2018), audit firms are more likely to encounter clients with significant business operations in foreign jurisdictions. The associated need to engage and oversee local component auditors in these jurisdictions can lead to challenges arising from different business cultures and the resulting intra‐audit miscommunications. Audit deficiencies related to these challenges have been detected by regulators (PCAOB 2011, 2010; CPAB 2012, 2015). Standard setters such as the IAASB and the Auditing and Assurance Standards Board (AASB) have responded by issuing an exposure draft proposing revisions to ISA 600 (IAASB 2020) and CAS 600 (AASB 2020) to strengthen the auditor's approach and provide enhanced guidance to practitioners. In light of this evolving area of assurance, this case was developed to deepen students' understanding of both group and component audits in an international context. The case takes the perspective of the group auditor and features an audit senior in a specialized role overseeing the component audit of a client's increasingly material Chinese subsidiary. Deficiencies in the prior year component audit, along with a change in the component auditor, further underlines the importance of robust risk analysis for the upcoming engagement.

Co-firing의 위험분석 및 완화방안: 인도네시아 사례로부터

The co-firing program initiative in Indonesia involves mixing a portion of biomass into the coal that is being used for the operation of coal power plants. It is expected that biomass will replace 10% of the coal that currently is being used by 2025. Through robust risk analysis and appropriate mitigation plans, all of the challenges and obstacles that are encountered should be overcome, thereby achieving the desired goals of the renewable policy. This research assesses the risk of co-firing based on the empirical data of the coal powered plants in Indonesia. The two extreme risk factors of co-firing are identified as the discontinuous supply of the required volume of biomass and the low quality of the available biomass. The remaining risk factors, which are classified as moderate, are the technical efficiency, reliability, the cost of the fuel, environmental impact, the management of the Fly Ash Bottom Ash (FABA), and the decreasing quality of the biomass. We found that the greatest challenge in implementing co-firing power plants is not their technical readiness; rather, it is the development of the supply channel, using the advantage of the abundant local biomass potential. As such, risk mitigation measures are needed to sustain the supply of biomass for the long term and to ensure that the biomass is available at a competitive price to ensure that electricity can be supplied at an acceptable price.

Quantitative risk analysis for operational transfer processes of maritime pilots

ABSTRACT Maritime pilots assist in manoeuvring ships arriving at or departing from a port. The most perilous part of their work is embarking and disembarking the vessel. The operational process of transferring maritime pilots is dangerous because the nature of the work involves risks such as falling from the pilot ladder into the water. Therefore, detailed risk analyses that consider potential operational failures are essential to improve pilot transfer safety. This paper presents a comprehensive risk analysis for the operational transfer processes of maritime pilots utilizing fuzzy extended fault tree analysis (FFTA). FFTA is a robust risk analysis method commonly used in the maritime industry. It is capable of predicting risk quantitatively by addressing the potential basic events of a system. In this study, the risk of encountering an accident during the transfer processes of maritime pilots is found to be 3.95E-03. The outcomes of this paper will provide valuable contributions to the safety of maritime pilots, who have suffered from substandard transfer process. The paper also highlights the failures and deficiencies with respect to operational processes in embarking/disembarking with the aim of minimizing the associated risks.

Generalized mean p-values for combining dependent tests: comparison of generalized central limit theorem and robust risk analysis

The test statistics underpinning several methods for combining p-values are special cases of generalized mean p-value (GMP), including the minimum (Bonferroni procedure), harmonic mean and geometric mean. A key assumption influencing the practical performance of such methods concerns the dependence between p-values. Approaches that do not require specific knowledge of the dependence structure are practically convenient. Vovk and Wang derived significance thresholds for GMPs under the worst-case scenario of arbitrary dependence using results from Robust Risk Analysis (RRA). Here I calculate significance thresholds and closed testing procedures using Generalized Central Limit Theorem (GCLT). GCLT formally assumes independence, but enjoys a degree of robustness to dependence. The GCLT thresholds are less stringent than RRA thresholds, with the disparity increasing as the exponent of the GMP ( r) increases. I motivate a model of p-value dependence based on a Wishart-Multivariate-Gamma distribution for the underlying log-likelihood ratios. In simulations under this model, the RRA thresholds produced tests that were usually less powerful than Bonferroni, while the GCLT thresholds produced tests more powerful than Bonferroni, for all r> − ∞. Above r> − 1, the GCLT thresholds suffered pronounced false positive rates. Above r> − 1/2, standard central limit theorem applied and the GCLT thresholds no longer possessed any useful robustness to dependence. I consider the implications of these results in the context of various interpretations of GMPs, and conclude that the GCLT-based harmonic mean p-value procedure and Simes' (1986) test represent good compromises in power-robustness trade-off for combining dependent tests.

Application of fuzzy logic to fault tree and event tree analysis of the risk for cargo liquefaction on board ship

Risk analysis is of paramount importance in maritime transportation due to the nature of work. The IMO (International Maritime Organization) adopted FSA (Formal Safety Assessment) as guidance to address risk analysis on-board ship. However, it does not suggest a specific approach on how to assess the risks. Therefore, safety researchers are seeking robust risk analysis approaches in maritime transportation. This paper performs comprehensive risk analysis by using bow-tie method within a fuzzy logic environment. While the bow-tie method is analysing potential causes and consequences of failures, the fuzzy logic deals with vagueness and imprecision of expert judgements. The case of cargo liquefaction on-board ship is selected as a case study since the consequences of cargo liquefaction are extremely dangerous for crew, ship and environment. Besides its theoretical insight, the paper supports maritime professionals to enhance safety awareness about the cargo liquefaction phenomenon.

Generalized mean p-values for combining dependent tests: comparison of generalized central limit theorem and robust risk analysis.

The test statistics underpinning several methods for combining p-values are special cases of generalized mean p-value (GMP), including the minimum (Bonferroni procedure), harmonic mean and geometric mean. A key assumption influencing the practical performance of such methods concerns the dependence between p-values. Approaches that do not require specific knowledge of the dependence structure are practically convenient. Vovk and Wang derived significance thresholds for GMPs under the worst-case scenario of arbitrary dependence using results from Robust Risk Analysis (RRA). Here I calculate significance thresholds and closed testing procedures using Generalized Central Limit Theorem (GCLT). GCLT formally assumes independence, but enjoys a degree of robustness to dependence. The GCLT thresholds are less stringent than RRA thresholds, with the disparity increasing as the exponent of the GMP ( r) increases. I motivate a model of p-value dependence based on a Wishart-Multivariate-Gamma distribution for the underlying log-likelihood ratios. In simulations under this model, the RRA thresholds produced tests that were usually less powerful than Bonferroni, while the GCLT thresholds produced tests more powerful than Bonferroni, for all r> - ∞. Above r> - 1, the GCLT thresholds suffered pronounced false positive rates. Above r> - 1/2, standard central limit theorem applied and the GCLT thresholds no longer possessed any useful robustness to dependence. I consider the implications of these results in the context of various interpretations of GMPs, and conclude that the GCLT-based harmonic mean p-value procedure and Simes' (1986) test represent good compromises in power-robustness trade-off for combining dependent tests.

Hydrological data uncertainty and its implications

Hydrologic data are at the core of our understanding of physical hydrologic processes, our simulation models and forecasts of water resources and hazards, and our monitoring of water quantity and quality. However, hydrologic data are subject to multiple sources of uncertainty that can introduce bias and error into our analyses and decision‐making if not properly accounted for. In this article, we summarize five categories of data uncertainty: measurement uncertainty, derived data uncertainty, interpolation uncertainty, scaling uncertainty, and data management uncertainty. Hydrologic data uncertainty magnitudes are typically in the range 10–40%. To quantify data uncertainty, hydrologists should first construct a perceptual model of uncertainty that itemizes uncertainty sources. The magnitude of each source can then be estimated using replicates (repeated, nested or subsampled measurements), or information from the literature (in‐depth uncertainty results from experimental catchments, colocated gauges or method comparisons). Multiple uncertainty sources can be combined using Monte Carlo methods to determine total uncertainty. Data uncertainty analysis improves hydrologic process understanding by enabling robust hypothesis testing and identification of spatial and temporal patterns that relate to true process differences rather than data uncertainty. By quantifying uncertainty in data used for input or evaluation of hydrologic models, we can prevent parameter bias, exclude disinformative data, and enhance model performance evaluation. In water management applications, quantifying data uncertainty can lead to robust risk analysis, reduced costs, and transparent results that improve the trust of the public and water managers.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods Engineering Water > Planning Water

An operational risk analysis tool to analyze marine transportation in Arctic waters

The Arctic Ocean has drawn major attention in recent years due to its rich natural resources and shorter navigational routes. Arctic development and transportation involve significant risk caused by the unique features of this region, such as ice, severe operating conditions, unpredictable climatic changes, and remoteness. Considering the high degree of uncertainty in the performance of vessel operating systems and humans, robust risk analysis and management tools are required to provide decision-support to prevent accidents and ensure safety at sea. This paper proposes an Object-Oriented Bayesian Network model to dynamically predict ship-ice collision probability based on navigational and operational system states, weather and ice conditions, and human error. The model, when integrated with potential consequences, may help estimate risk. A case study related to oil tanker navigation on the Northern Sea Route (NSR) is used to show the application of the proposed model to predict oil tanker collision with sea ice.

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Abstract. Compound events (CEs) are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint – dependent – occurrence causes an extreme impact. Conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present-day and future climate, as well as the uncertainty estimates around such risk. The model includes predictors, which could represent for instance meteorological processes that provide insight into both the involved physical mechanisms and the temporal variability of compound events. Moreover, this model enables multivariate statistical downscaling of compound events. Downscaling is required to extend the compound events' risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis, observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk; in particular, the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

A robust risk analysis method for water resources allocation under uncertainty

A robust risk analysis method (RRAM) is developed for water resource decision making under uncertainty. This method incorporates interval-parameter programming and robust optimization within a stochastic programming framework. In the RRAM formulation, penalties are exercised with the recourse against any infeasibility, and robustness measures are introduced to examine the variability of the second stage costs which are above the expected levels. In this study, a number of weighting levels are considered which correspond to the robustness levels of risk control. Generally, a plan with a higher robust level would better resist from system risk. Thus, decision with a lower robust level can correspond to a higher risk of system failure. There is a tradeoff between system cost and system reliability. The RRAM is applied to a case of water resource management. The modeling results can help generate desired decision alternatives that will be particularly useful for risk-aversive decision makers in handling high-variability conditions. The results provide opportunities to managers to make decisions based on their own preferences on system stability and economy, and ensure that the management policies and plans be made with reasonable consideration of both system cost and risk.

Optimising import risk mitigation: anticipating the unintended consequences and competing risks of informal trade

The effectiveness of risk mitigation may be compromised by informal trade, including illegal activities, parallel markets and extra-legal activities. While no regulatory system is 100% effective in eliminating the risk of disease transmission through animal and animal product trade, extreme risk aversion in formal import health regulations may increase informal trade, with the unintended consequence of creating additional risks outside regulatory purview. Optimal risk mitigation on a national scale requires scientifically sound yet flexible mitigation strategies that can address the competing risks of formal and informal trade. More robust risk analysis and creative engagement of nontraditional partners provide avenues for addressing informal trade.