A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study

Garbolino, Emmanuel; Chéry, Jean-Pierre; Guarnieri, Franck

doi:10.1111/risa.12534

Cited by 33 publications

(17 citation statements)

References 12 publications

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“…In addition, the use of stock and flow models expressed using differential equation notation also takes into account the dynamism of time-dependent risk factors. This strength of system dynamics is well recognized outside of the AUV domain and demonstrated in risk studies across various disciplinary boundaries, from chemical (Garbolino, Chery, & Guarnieri, 2016), mining (Cooke, 2003) to aerospace (Dulac et al, 2005). However, the deterministic nature of system dynamic models does not explicitly account for uncertainties in causal relationships and soft factors.…”

Section: System Dynamicsmentioning

confidence: 99%

Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

et al. 2019

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With the maturing of autonomous technology and better accessibility, there has been a growing interest in the use of autonomous underwater vehicles (AUVs). The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extreme operating environment. To control the risk of loss, existing risk analyses approaches tend to focus more on the AUV's technical aspects and neglect the role of soft factors, such as organizational and human influences. In addition, the dynamic and complex interrelationships of risk variables are also often overlooked due to uncertainties and challenges in quantification. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. In the FuSDRA framework, system dynamics models the interrelationships between risk variables from different dimensions and considers the time-dependent nature of risk while fuzzy logic accounts for uncertainties. To demonstrate its application, an example based on an actual Antarctic AUV program is presented. Focusing on funding and experience of the AUV team, simulation of the FuSDRA risk model shows a declining risk of loss from 0.293 in the early years of the Antarctic AUV program, reaching a minimum of 0.206 before increasing again in later years. Risk control policy recommendations were then derived from the analysis. The example demonstrated how FuSDRA can be applied to inform funding and risk management strategies, or broader application both within the AUV domain and on other complex technological systems.

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Section: System Dynamicsmentioning

confidence: 99%

Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

et al. 2019

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“…System approaches to the analysis of risk are numerous, often advocating a general system approach where interventions and effects are seen to be multiply connected and where, as consequence, mitigating interventions need to be made with the understanding that unwanted and unexpected side-effects may/will result. (32)(33)(34)(35)(36)(37) One or two sources have, indeed, applied this general understanding to environmental (38) and even flood risk analyses, specifically. (39) There is some existing use of system dynamics within risk analysis, both in a general sense, (40,41) for the purposes of validation (42) and as a support for the cognitive process so central to risk analysis.…”

Section: Has and Riskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Agency in Disaster Planning: A Systems Approach

et al. 2018

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Current approaches to risk management place insufficient emphasis on the system knowledge available to the assessor, particularly in respect of the dynamic behavior of the system under threat, the role of human agents (HAs), and the knowledge available to those agents. In this article, we address the second of these issues. We are concerned with a class of systems containing HAs playing a variety of roles as significant system elements-as decisionmakers, cognitive agents, or implementers-that is, human activity systems. Within this family of HAS, we focus on safety and mission-critical systems, referring to this subclass as critical human activity systems (CHASs). Identification of the role and contribution of these human elements to a system is a nontrivial problem whether in an engineering context, or, as is the case here, in a wider social and public context. Frequently, they are treated as standing apart from the system in design or policy terms. Regardless of the process of policy definition followed, analysis of the risk and threats to such a CHAS requires a holistic approach, since the effect of undesirable, uninformed, or erroneous actions on the part of the human elements is both potentially significant to the system output and inextricably bound together with the nonhuman elements of the system. We present a procedure for identifying the potential threats and risks emerging from the roles and activity of those HAs, using the 2014 flooding in southwestern England and the Thames Valley as a contemporary example.

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“…Analysts increasingly integrate system dynamics as they seek to build and characterize models to manage risks embedded in complex health‐care systems (Garbolino, Chery, & Guarnieri, ; Kazemi, Mosleh, & Dierks, ; Pate‐Cornell & Cox, ; Sharit, ; Thompson, Tebbens, Pallansch, Wassilak, & Cochi, ). Prior recent work includes frameworks for modeling dynamic risks in trauma care units (Sharit, ), cell therapy manufacturing laboratories (Lopez et al., ), and acute care adverse events (Kazemi et al., ).…”

Section: Introductionmentioning

confidence: 99%

Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety

Vanlandingham

Hampton

Thompson³

et al. 2019

Risk Analysis

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Anatomic pathology (AP) laboratories provide critical diagnostic information that help determine patient treatments and outcomes, but the risks of AP operations and their impact on patient safety and quality of care remain poorly recognized and undermanaged. Hospital‐based laboratories face an operational and risk management challenge because clinical work of unknown quantity and complexity arrives with little advance notice, which results in fluctuations in workload that can push operations beyond planned capacity, leading to diagnostic delays and potential errors. Modeling the dynamics of workload and complexity in AP offers the opportunity to better use available information to manage risks. We developed a stock‐and‐flow model of a typical AP laboratory operation and identified key exogenous inputs that drive AP work. To test the model, we generated training and validations data sets by combining data from the electronic medical records and laboratory information systems over multiple years. We demonstrate the implementation of 10‐day AP work forecast generated on a daily basis, and show its performance in comparison with actual work. Although the model somewhat underpredicts work as currently implemented, it provides a framework for prospective management of resources to ensure quality during workload surges. Although full implementation requires additional model development, we show that AP workload largely depends on few and accessible clinical inputs. Recognizing that level loading of work in a hospital is not practical, predictive modeling of work can empower laboratories to triage, schedule, or mobilize resources more effectively and better manage risks that reduce the quality or timeliness of diagnostic information.

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A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study

Cited by 33 publications

References 12 publications

Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

Human Agency in Disaster Planning: A Systems Approach

Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety

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