Risk assessment in software supply chains using the Bayesian method

Librantz, André Felipe Henriques; Costa, Ivanir; Spínola, Mauro de Mesquita; Neto, Geraldo Cardoso de Oliveira; Zerbinatti, Leandro

doi:10.1080/00207543.2020.1825860

Cited by 3 publications

(5 citation statements)

References 58 publications

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“…In the context of fault diagnosis, Bayesian Networks have been employed to model the causal relationships between symptoms and potential causes, enabling efficient and accurate diagnosis (Jones et al, 2010;Kinjo et al, 2022). Moreover, Bayesian Networks have been utilized in the evaluation of service quality, where they can capture the interdependencies between various quality factors and predict future risks (Badurdeen et al, 2014;Ding et al, 2018;Librantz et al, 2020;Lockamy, 2011).…”

Section: Bayesian Networkmentioning

confidence: 99%

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Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Teles¹,

Librantz

Neto³

et al. 2023

Preprint

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In recent years, applying quantitative risk analysis in Road cargo transport (RCT) has yielded successful results in assessing risks associated with industrial industries. Recent studies have highlighted the significance of different modes of transportation (such as roads, rails, pipelines, and inland waterways)for hazardous materials, emphasizing their role in determining risk levels. Among these modes, road transport via trucks stands out as a crucial component for economic development and is commonly employed for transporting various types of cargo. To effectively evaluate the risk level of a given activity within RCT, it is essential to determine the severity index for each potential situation that may arise during the transportation process. In this context, this research aims to investigate and calculate the risks associated with RCT by employing Bayesian Networks. Computational models were implemented using Bayesian Network software, and data input was carried out using Microsoft Excel®spreadsheets. The methodology employed for this study entailed field research with the involvement of experts and academic sources, along with the utilization of a systematic literature review. Additionally, the Delphi technique was applied, followed by a survey. The results pointed that the proposed model could effectively aid in identifying the level of risk involved in RCT operations across different scenarios. Furthermore, this model enables managers to evaluate the probability of one or more risk factors occurring during operations, facilitating the implementation of more efficient mitigation measures.

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Section: Bayesian Networkmentioning

confidence: 99%

“…Furthermore, Bayesian Networks have been applied to analyze the risks in supply chains, considering the interdependence of various risk factors. This allows for quantitative analysis and modeling of risks in supply chain operations (Badurdeen et al, 2014;Ding et al, 2018;Librantz et al, 2020;Lockamy, 2011).…”

Section: Bayesian Networkmentioning

confidence: 99%

Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Teles¹,

Librantz

Neto³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A parte qualitativa é um DAG e a parte quantitativa são as probabilidades atribuídas aos nós que representam as variáveis. O fornecimento da relação de causa e efeito provém resultados animadores relacionados a diagnóstico, previsão e classificação de falhas (Librantz et al, 2020).…”

Section: Rede Bayesianaunclassified

“…Esta abordagem permite que o conhecimento de especialistas sejam incluídos na modelagem (Librantz et al, 2020). Esta técnica representa uma boa estratégia para lidar com problemas que tratam incertezas.…”

Section: Rede Bayesianaunclassified

Modelagem Bayesiana aplicada para cálculo da probabilidade de falha em Sistemas de Saúde IoT

Kinjo¹,

Librantz²,

Souza³

et al. 2022

risti

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A implantação da tecnologia da Internet das Coisas (IoT) traz benefícios à vida, como controle remoto de pragas na agricultura, monitoramento da cadeia de suprimentos, melhoria na educação e monitoramento de pacientes. No entanto, apesar dos benefícios, existem desafios embutidos na implementação desta tecnologia. Um dos maiores desafios da área é a violação de privacidade e segurança de dados. Portanto, é necessário avaliar a probabilidade de falha dos elementos e, consequentemente, a causa desse problema. Assim, é neste contexto que este trabalho se propõe a identificar, modelar e calcular a probabilidade de falha através de uma análise sistemática, utilizando Redes Bayesianas. Os resultados mostraram que através do uso do modelo proposto foi possível avaliar diferentes cenários para o uso de redes de Internet das Coisas, bem como simular o efeito da probabilidade de falha nos elementos críticos do sistema.

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“…Esta técnica permite descrever a relação de causa e efeito e, por isso, apresenta resultados animadores no que se refere diagnóstico, previsão e classificação de falhas e análise de riscos (Librantz et al, 2020). Entre as aplicações desta técnica encontram-se desenvolvimento de novos produtos (Chin et al, 2009), diagnóstico de infecções (Sareen, Sood, & Gupta, 2017), identificação e controle de riscos no transporte aeronáutico (Martins & Maturana, 2013).…”

Section: Aplicação Do Modelo Bayesianounclassified

Risk Evaluation in Software Project Using Bayesian Network Modeling

Cassia¹,

Librantz²,

Costa³

et al. 2020

RSD

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Project risk events are often influenced by each other and rarely act independently. In this context, effective methods to identify, model and analyze these risks are necessary. The objective of this article is to apply the risk analysis in a software development project, based on the model of the Software Engineering Institute (SEI), using the Bayes model to calculate the event probabilities and also the Noisy-OR calculation structure to assign the initial weights of the network of factors that influence the project. In this way, it is expected to increase the chances of success of the risk analysis process. The results obtained by the techniques adopted prove to be promising in assisting the process of decision making by the managers of software development projects.

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Risk assessment in software supply chains using the Bayesian method

Cited by 3 publications

References 58 publications

Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Modelagem Bayesiana aplicada para cálculo da probabilidade de falha em Sistemas de Saúde IoT

Risk Evaluation in Software Project Using Bayesian Network Modeling

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