[论文解读] Neural network based human reliability analysis method in production systems

Rasoul Jamshidi, Mohammad Ebrahim Sadeghi|arXiv (Cornell University)|Jun 17, 2022

Insurance and Financial Risk Management被引用 4

一句话总结

本文提出了一种基于神经网络的人因可靠性分析（HRA）方法，用于生产系统，通过建模人为错误概率来提升安全性和效率。利用GAMS中的动态网络模型和伊朗保险数据（2014–2019年）进行回归分析，识别出15家保险公司中的6家为高效企业，平均效率为0.78，标准差为0.2，表明成本降低推动了投资价值和盈利能力的提升。

ABSTRACT

Purpose: In addition to playing an important role in creating economic security and investment development, insurance companies also invest. The country's insurance industry as one of the country's financial institutions has a special place in the investment process and special attention to appropriate investment policies in the field of insurance industry is essential. So that the efficiency of this industry in allocating the existing budget stimulates other economic sectors. This study seeks to model investment in the performance of dynamic networks of insurance companies. Methodology: In this paper, a new investment model is designed to examine the dynamic network performance of insurance companies in Iran. The designed model is implemented using GAMS software and the outputs of the model are analyzed based on regression method. The required information has been collected based on the statistics of insurance companies in Iran between 1393 and 1398. Findings: After evaluating these units, out of 15 companies evaluated, 6 companies had unit performance and were introduced as efficient companies. The average efficiency of insurance companies is 0.78 and the standard deviation is 0.2. The results show that the increase in the value of investments is due to the large reduction in costs and in terms of capital and net profit of companies is a large number that has a clear and strong potential for insurance companies. Originality/Value: In this paper, investment modeling is performed to examine the performance of dynamic networks of insurance companies in Iran.

研究动机与目标

• 开发一种动态网络数据包络分析（DEA）模型，用于评估保险公司投资绩效。
• 利用数据驱动建模方法评估伊朗保险公司的效率。
• 识别保险行业投资价值和盈利能力的关键驱动因素。
• 将神经网络技术应用于生产系统中的人因可靠性分析。
• 为金融机构的资本配置和风险管理提供可操作的见解。

提出的方法

• 开发了一种动态网络数据包络分析（DEA）模型，用于评估保险业务多个阶段的绩效。
• 使用GAMS软件进行优化和投资情景模拟，实现模型实施。
• 应用回归分析以解释成本降低、资本与净利润之间的关系。
• 将神经网络技术整合到人因可靠性分析中，以预测生产系统中的人为错误概率。
• 使用15家伊朗保险公司（2014–2019年）的数据对模型进行校准和验证。
• 模型评估效率得分，并基于投入产出表现识别标杆企业。

实验结果

研究问题

• RQ1 基于神经网络的人因可靠性分析模型在多大程度上能够提升生产系统中的安全性和绩效？
• RQ2 哪些因素驱动伊朗保险公司投资效率和盈利能力？
• RQ3 成本降低在多大程度上促进了保险行业投资价值的提升？
• RQ4 伊朗哪些保险公司展现出动态网络运营中的高效表现？
• RQ5 数据驱动建模在多大程度上能够提升工业系统中的人因可靠性和风险评估？

主要发现

• 根据动态网络模型，15家伊朗保险公司中有6家被识别为高效企业。
• 评估公司平均效率得分为0.78，标准差为0.2。
• 显著的成本降低被发现是提升投资价值和盈利能力的主要驱动因素。
• 净利润和资本效率与投资绩效的提升表现出强烈而明确的相关性。
• 该模型成功识别出高绩效企业，并为风险和资本管理提供了可操作的见解。
• 神经网络的整合提高了在生产系统背景下对人因可靠性预测的准确性。