基于Petri网的供应链时效性和恢复时间操作风险分析方法

IF 1.7 4区 工程技术 Q3 ENGINEERING, INDUSTRIAL
J. Skorupski, A. Tubis, S. Werbińska-Wojciechowska, Adam Wróblewski
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引用次数: 0

摘要

近年来,供应链风险管理越来越受到人们的关注。因此,在这一领域开发了各种方法、工具和实践。然而,他们通常关注的是供应链中断的直接后果。因此,本文的目的是提出一种供应链的操作风险分析方法,其中不良事件发生的后果是基于两个指标来评估的:(1)对客户的供应过程的直接后果(中断),以及(2)供应系统的恢复时间。基于研究结果,我们引入了一个Petri网模型来绘制供应链上的物料流。我们从汽车行业中选择一家公司来实施所提出的分析方法。最后进行的讨论确认了区分风险评估的直接和间接后果的相关性。结果的解释可能是双重的,这可能是选择适当的风险管理工具所必需的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Petri nets-based method for operational risk analysis in supply chains based on timeliness and recovery time
Recently, supply chain risk management has been attracting growing attention. Therefore, various methods, tools, and practices have been developed in this area. However, they usually are focused on the direct consequences of disruptions occurring in supply chains. Thus, the purpose of this article is to present an operational risk analysis method for supply chains, in which consequences of an adverse event occurrence are assessed based on two measures: (1) the direct consequences (disruption) of supply processes to customers, and (2) recovery time for a supply system. Based on research findings, we introduced a Petri nets model for mapping material flows along a supply chain. We implement the proposed analysis method in a selected company from the automotive industry. The performed final discussion confirmed the relevance of distinguishing the direct and indirect consequences of the risk assessment. It was also suggested that the results’ interpretation could be two-fold, which may be necessary for appropriate risk management tool selection.
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来源期刊
CiteScore
4.50
自引率
19.00%
发文量
81
审稿时长
6-12 weeks
期刊介绍: The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome
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