Catastrophe-related disruptions’ preparedness and emergency management in Morocco: a proactive risks and resilience digital twin-based analysis

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-09-17 DOI:10.1108/jm2-02-2024-0050
Rachid Mharzi, Abderrahmane Ben Kacem, Abdelmajid Elouadi
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引用次数: 0

Abstract

Purpose

The purpose of this study is to analyze the operations and performance dynamics of a supply chain (SC) subject to disruptions. The preparedness of Moroccan responders in handling emergencies could be enhanced significantly, by devising digital twin-based decision support systems (DSSs).

Design/methodology/approach

The authors create a discrete-event simulation model to investigate proactively risks and resilience of a Moroccan basic-items SC (BISC). In this study, the authors analyze the effects of catastrophe-related disruptions (CRDs) on the Moroccan BISC, by the use of a simulation-based decision-supporting quantitative method.

Findings

In the disruption-free simulation experiment, the outcome was a satisfactory 100% coverage. By implementing CRDs, inventory levels have dropped, service levels decreased, lead time raised and there was an increase in backlogged products and late orders numbers. The highest impact was observed for the shutdown of paths linking suppliers to warehouses, whereas the increase in demand had a comparatively minor effect. The risk analysis approach helps to identify critical products for which the time-to-recover is longer and requires more commitment to enhance their resilience.

Practical implications

The model serves to deduce quantitative resilience assessment from simulation, streamline the selection of recovery strategies and enable the best-informed reactive decision-making to minimize the impact.

Originality/value

The research brings organizing solutions to catastrophe-related emergencies in Morocco. It would contribute significantly by visualizing, examining and unveiling the effects of disruptions on a BISC and offering actionable recommendations for remedial measures.

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摩洛哥与灾害有关的干扰准备和应急管理:基于主动风险和复原力数字孪生分析
目的本研究旨在分析供应链(SC)在受到干扰时的运营和绩效动态。通过设计基于数字孪生的决策支持系统(DSS),可以大大增强摩洛哥应急人员处理紧急情况的准备能力。作者创建了一个离散事件模拟模型,以主动调查摩洛哥基本物品供应链(BISC)的风险和恢复能力。在这项研究中,作者利用基于仿真的决策支持定量方法,分析了与灾难相关的中断(CRDs)对摩洛哥 BISC 的影响。研究结果在无中断仿真实验中,结果是令人满意的 100% 覆盖率。通过实施 CRD,库存水平下降了,服务水平降低了,交付周期延长了,积压产品和逾期订单数量增加了。据观察,连接供应商和仓库的路径关闭影响最大,而需求增加的影响相对较小。风险分析方法有助于确定哪些关键产品的恢复时间较长,需要做出更多承诺以提高其恢复能力。该模型有助于从模拟中推导出定量的恢复能力评估,简化恢复策略的选择,并做出最明智的反应决策,以最大限度地减少影响。它将通过可视化、检查和揭示中断对 BISC 的影响,并为补救措施提供可行的建议,从而做出重大贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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