Risk assessment of supply-chain systems: a probabilistic inference method

Zheng, Xiaojing; Zhang, Lulu

doi:10.1080/17517575.2020.1762004

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…The results show that this method has great advantages, and can be generalized to credit databases of other financial institutions. Zheng and Zhang constructed a dynamic Bayesian network model of supply chain risk to describe the nature of supply chain risk, and the results showed that supply chain risk changes over time and converges within a certain stable interval, occurrence time and holding time satisfy several Poisson processes [17].…”

Section: Value Miningmentioning

confidence: 99%

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

2023

Teh. vjesn.

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The value of power data credit reporting in the social credit system continues to increase, and the government, users and the whole society have deep expectations and support for power data credit reporting. This paper will combine the data labeling theory as the support, define the power data label and explain its labeling implementation. Based on the construction of knowledge graph, the method of labeling power data is introduced in detail: demand analysis method, index selection method, data cleaning method and data desensitization method. Use the sorted data labels to establish a label system for power data, and through its system, visualize the comprehensive situation of enterprise power data credit information to meet the development of power data credit business. This paper takes shell enterprises as the main representatives of credit risk enterprises, analyzes the power data in the three stages before and after loans, and builds a value mining model for power credit data. In the future, the data labeling technology and value mining model of the power data credit business will be comprehensively applied, and the power data label library and credit model library will be established and continuously improved, so as to facilitate the evaluation of the operation of the enterprise at different stages.

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Section: Value Miningmentioning

confidence: 99%

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

2023

Teh. vjesn.

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“…For example, Lawrence et al (2020) developed a Bayesian Network to analyse supplier disruption following extreme weather risks. Similarly, Kumar Sharma and Sharma (2015) proposed a model to predict disruption risks in a supply chain, and Zheng and Zhang (2020) developed a Bayesian Network model to assess supply chain risk parameters. The latter paper concludes that the probability of a risk occurring becomes stable after a certain time.…”

Section: Bayesian Networkmentioning

confidence: 99%

“…However, the ease comes with a costly assumption of statistical independence of underlying events. Bayesian Networks somewhat alleviate this issue (Garvey et al, 2015;Lawrence et al, 2020;Lockamy, 2014;Qazi et al, 2018;Sharma & Routroy, 2016;Zheng & Zhang, 2020). Two advantages in particular need to be noted.…”

Section: Bayesian Belief Network and Fault Tree Analysismentioning

confidence: 99%

Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications

et al. 2022

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The year 2020 can be earmarked as the year of global supply chain disruption owing to the outbreak of the coronavirus (COVID-19). It is however not only because of the pandemic that supply chain risk assessment (SCRA) has become more critical today than it has ever been. With the number of supply chain risks having increased significantly over the last decade, particularly during the last 5 years, there has been a flurry of literature on supply chain risk management (SCRM), illustrating the need for further classification so as to guide researchers to the most promising avenues and opportunities. We therefore conduct a bibliometric and network analysis of SCRA publications to identify research areas and underlying themes, leading to the identification of three major research clusters for which we provide interpretation and guidance for future work. In doing so we focus in particular on the variety of parameters, analytical approaches, and characteristics of multi-criteria decision-making techniques for assessing supply chain risks. This offers an invaluable synthesis of the SCRA literature, providing recommendations for future research opportunities. As such, this paper is a formidable starting point for operations researchers delving into this domain, which is expected to increase significantly also due to the current pandemic.

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“…It is mostly used to visualise and to prioritise risks to identify and define mitigation measures. Some recent contributions have clearly improved that vision, for instance by including Bayesian consideration as presented in (Zheng and Zhang 2020).…”

Section: General Backgroundmentioning

confidence: 99%

Instability is the norm! A physics-based theory to navigate among risks and opportunities

Bénaben

Faugère

Montreuil

et al. 2021

Enterprise Information Systems

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Most decision-supports methods are dedicated to the identification and characterization of risks and opportunities. The concrete exploitation of these risks and opportunities is generally depending on the ability of users to analyze multi-dimensional situations, to mobilize their experience and to foresee consequences. In this article, a new and original data science-based vision of risk and opportunity management for decision-making purpose is introduced. The main expected benefit of this vision is to enable decision makers to manage the performance trajectory of a considered system by visualizing and combining the impact of risks and opportunity.

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Risk assessment of supply-chain systems: a probabilistic inference method

Cited by 13 publications

References 35 publications

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications

Instability is the norm! A physics-based theory to navigate among risks and opportunities

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