Estimating resilience of manufacturing plants to physical disruptions: Model and application

弹性(材料科学) 过程(计算) 计算机科学 事件(粒子物理) 生产(经济) 风险分析(工程) 持续时间(音乐) 供应链 业务 运营管理 过程管理 工程类 经济 艺术 物理 文学类 宏观经济学 量子力学 营销 热力学 操作系统
作者
Antonio C. Caputo,Lorenzo Donati,P. Salini
出处
期刊:International Journal of Production Economics [Elsevier]
卷期号:266: 109037-109037 被引量:4
标识
DOI:10.1016/j.ijpe.2023.109037
摘要

The concept of Resilience is gaining an increasing importance in industrial systems, service networks and supply chains, as it extends traditional risk assessment methods by capturing both shortterm and long-term effects of accidents and disruptions. Resilience is, in fact, both a qualitative property and a performance indicator that measures the system's ability to survive disruptive events, and rapidly restore its functionality after the disruptive event has occurred. However, while literature about systems resilience is ample, comparatively scarce attention has been devoted to the evaluation of production systems resilience. As a contribution to fill this gap, in this paper a quantitative method for resilience estimation specifically aimed at manufacturing plants is developed. The method focuses on computing resilience of the manufacturing process, according to its structure, when resources are subjected to physical damage resulting from disruptive events such as sabotage, technical accidents or natural disasters. Overall, the paper allows to assess the economic loss and duration of production interruption in any kind of manufacturing plant when subjected to damage produced by the above causes. In particular, the method enables direct assessment of the initial capacity loss following a prescribed disruptive event, and explicit estimation of the time-dependent capacity recovery path based on the actual process structure and sequence of resources restoration tasks, also allowing the quantification of resulting economic losses. In the paper, after model development, an application example is provided to show model capabilities. Facility designers and emergency managers can use this model as a decision support tool for risk assessment, and to plan emergency actions and plant refurbishment aimed at resilience enhancement.
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