Risk assessment of wind turbines: Transition from pure mechanistic paradigm to modern complexity paradigm

Ashrafi, Maryam; Davoudpour, Hamid; Khodakarami, Vahid

doi:10.1016/j.rser.2015.06.011

Cited by 24 publications

(16 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such a modelling technique can help managers visualise supply chain risks and take effective mitigation strategies. BBNs have already been explored in the literature on risk management (Ashrafi et al, 2015;Wu et al, 2015) and SCRM (Garvey et al, 2015;Nepal & Yadav, 2015;Qazi et al, 2017) for modelling and assessing risks. However, to the best of the authors' knowledge, BBNs have not been explored in the literature on SCRM to establish the impact of supply chain risks on multiple (potentially conflicting) objectives and to prioritise supply chain risk mitigation strategies considering the risk appetite of a decision maker.…”

Section: Proposed Modelling Approachmentioning

confidence: 99%

“…With respect to interdependency modelling, there are a number of approaches (similar to the ones discussed in Section 2.2) applied to other application areas including but not limited to project risk management (Qazi et al, 2016;Zhang, 2016), enterprise resource planning (Aloini et al, 2012a,b) and reliability of engineering systems (Ashrafi et al, 2015). Using the unique features of BBNs and EUT, the proposed approach not only captures probabilistic interdependency between risks but also integrates the risk appetite of a decision maker within the risk management process.…”

Section: A Scrm Process Integrating Interdependent Factors and The Rimentioning

confidence: 99%

“…A number of established techniques have been utilised to identify, assess and treat supply chain risks (Colicchia & Strozzi, 2012;Ho et al, 2015). Fault Tree Analysis (FTA) being widely used in the literature on system safety and reliability engineering (Ashrafi et al, 2015) is considered an effective technique for managing supply chain risks (Oehmen et al, 2009;Sherwin et al, 2016). The technique is used to identify a top event (occurrence of a risk) and develop a network of causal factors leading to the top event.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks

Qazi

Dickson

Quigley

et al. 2018

International Journal of Production Economics

170

View full text Add to dashboard Cite

Preprint submitted to International Journal of Production Economics Highlights • A SCRM process integrating all stages of the risk management process is proposed and operationalised. • The operationalisation scheme adapts and integrates various techniques from diversified research areas. • 'Probability-conditional expected utility' matrix is introduced to assess interdependent risks. • 'Weighted net evaluation of risk mitigation' is proposed to capture the decision maker's risk appetite. • Propositions are introduced to elucidate the significance of modelling interdependent risks. Highlights (for review)

show abstract

Section: Proposed Modelling Approachmentioning

confidence: 99%

Section: A Scrm Process Integrating Interdependent Factors and The Rimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks

Qazi

Dickson

Quigley

et al. 2018

International Journal of Production Economics

170

View full text Add to dashboard Cite

show abstract

“…Wind turbine accidents and wind energy risks have drawn certain attention of the academic community [10]- [20]. However, our review of the literature showed that none of existing studies have done a comprehensive analysis of the associations between factors (predictive attributes), and Death and Injury [14]- [16]. Furthermore, none of the papers we found in the literature combine formal statistical methods with data mining approach to analyze a dataset that contains multiple accidents.…”

Section: Literaturementioning

confidence: 98%

Wind Turbine Accidents: A Data Mining Study

Ertek

Haksöz

Pakter

et al. 2017

IEEE Systems Journal

View full text Add to dashboard Cite

While the global production of wind energy is increasing, there exists a significant gap in the academic and practice literature regarding the analysis of wind turbine accidents. Our paper presents the results obtained from the analysis of 240 wind turbine accidents from around the world. The main focus of our paper is revealing the associations between several factors and deaths and injuries in wind turbine accidents. Specifically, the associations of death and injuries with the stage of the wind turbine's life cycle (transportation, construction, operation, and maintenance) and the main cause factor categories (human, system/equipment, and nature) were studied. To this end, we conducted a detailed investigation that integrates exploratory and statistical data analysis and data mining methods. The paper presents a multitude of insights regarding the accidents and discusses implications for wind turbine manufacturers, engineering and insurance companies, and government organizations.

show abstract

“…BBNs have been extensively applied to the field of risk management (Ashrafi, Davoudpour, & Khodakarami, 2015;Norrington, Quigley, Russell, & Van der Meer, 2008;Wu, Yang, Chang, Château, & Chang, 2015) mainly because BBNs offer a unique feature of modelling risks combining both the statistical data and subjective judgment in case of non-availability of data (Dogan & Aydin, 2011).…”

Section: Literature Review: Models For Managing Interdependent Supplymentioning

confidence: 99%

Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies

Qazi

Quigley

Dickson

et al. 2017

European Journal of Operational Research

View full text Add to dashboard Cite

In this paper, we introduce an integrated supply chain risk management process that is grounded in the theoretical framework of Bayesian Belief Networks capturing interdependency between risks and risk mitigation strategies, and integrating all stages of the risk management process. The proposed process is unique in four different ways: instead of mapping the supply network, it makes use of Failure Modes and Effects Analysis to model the risk network which is feasible for modelling global supply chains; it is driven by new dependency based risk measures that can effectively capture the network wide impact of risks for prioritisation; it utilises the concept of Shapley value from the field of cooperative game theory to determine a fair allocation of resources to the critical risks identified; and the process helps in prioritising potential risk mitigation strategies (both preventive and reactive) subject to budget and resource constraints. We demonstrate its application through a simulation study

show abstract

Risk assessment of wind turbines: Transition from pure mechanistic paradigm to modern complexity paradigm

Cited by 24 publications

References 54 publications

Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks

Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks

Wind Turbine Accidents: A Data Mining Study

Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies

Contact Info

Product

Resources

About