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Today, the editor brings you the "7.2 Failure Probability Assessment (2) of the intensive reading journal paper "Risk Assessment of Software Supply Chain Based on Bayesian Method".
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Dear, this is the LearingYard Academy!
Today, the editor brings the "7.2 probability failu (2) of the journal paper 'Risk assessment in software supply chains using the Bayesian method'".
Welcome to visit!
一、内容摘要（Content summary）
In this issue, we will introduce the failure probability assessment of the intensive reading journal paper "Software Supply Chain Risk Assessment Based on Bayesian Method" from three aspects: mind map, intensive reading content, and knowledge supplementation (2).
In this issue, the tweet will introduce the probability failu (2) of the journal paper "Risk assessment in software supply chains using the Bayesian method" from three aspects: mind map, detailed reading content, and additional knowledge supplementation.
二、思维导图（Mind Mapping）
三、精读内容（Detailed Reading Content）
Based on the analysis of the previous tweet, the authors analyzed the performance of different countries under various risk factors through specific percentage indexes, especially in the risk assessment of participant risk, software component risk, operation and maintenance risk, logistics supply chain risk, software supply chain risk, etc., and obtained the corresponding results. Among them, for the software supply chain risk, country A has the highest risk of enforcing SSCR in the process, followed by country C, and country B has the lowest risk in the supply chain, as shown in the table below.
Based on the analysis in the previous tweet, the authors analyzed the performance of different countries under each risk factor through specific percentage indices, especially in terms of participant risk, software component risk, operation and maintenance risk, logistics supply chain risk, and software supply chain risk for the risk assessment, and came up with the corresponding results. In particular, for software supply chain risk, the process in country A has the highest risk for SSCR enforcement, followed by country C. The lowest risk in the supply chain is in country B, as shown in the table below.
Next, the authors explore ways to optimize risk assessment in different regions: country A requires remote software management due to high-risk natural disasters; Socio-political factors in country C affect software procurement; Country B, on the other hand, provides a low-risk procurement environment due to stable conditions.
The authors then explore ways to optimize risk assessment in different regions: country A requires remote software management due to high-risk natural disasters; socio-political factors affect software procurement in country C; and country B provides a low-risk procurement environment due to stable conditions.
The proposed method can reduce the workload of experts by 56.1% and improve the accuracy of complex probability evaluation through automated query process and analytic hierarchy process (AHP). The above proves that the research results of this paper are helpful for understanding effective risk management strategies. In addition, when the number of risk factors increases, such as one factor in each category (external and internal risk), the number of queries required increases exponentially.
The methodology proposed in this paper is able to reduce the expert workload by up to 56.1% through automated query process and hierarchical analysis (AHP) while improving the accuracy of complex probabilistic assessments. The above proves that the findings of the paper contribute to the understanding of effective risk management strategies. Moreover, when the number of risk factors increases, for example, one factor in each category (external and internal risk), the number of queries required increases exponentially.
四、知识补充——层次分析法（Knowledge supplementation - hierarchical analysis）
According to the nature of the problem and the overall goal to be achieved, the analytic hierarchy process decomposes the problem into different constituent factors, and gathers and combines the factors at different levels according to the interrelated influence and affiliation between the factors to form a multi-level analysis structure model, so that the problem is finally reduced to the determination of the relative importance of the lowest level (for decision-making, measures, etc.) relative to the highest level (overall goal) or the arrangement of relative advantages and disadvantages. The specific steps are as follows:
Hierarchical analysis according to the nature of the problem and the overall goal to be achieved, the problem is broken down into different components, and according to the interrelated impact of the factors as well as the affiliation of the factors according to the different levels of aggregation of combinations, the formation of a multi-level analysis of the structural model, which ultimately makes the problem boils down to the lowest level (for decision-making programs, measures, etc.) relative to the highest level (the overall goal) of the relative importance of the weights of determining or The ranking of relative advantages and disadvantages. The specific steps are as follows:
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If you have a unique idea about the article,
please leave us a message,
and let us meet tomorrow.
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参考资料：ChatGPT、百度百科
Bibliography:
André Felipe Henriques Librantz, Ivanir Costa, Mauro de Mesquita Spinola, et al. Risk assessment in software supply chains using the Bayesian method [J]. International Journal of Production Research, 2021, 59(22): 6758-6775.
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