Risk management can be defined as coordinated activities to conduct and control an organization with consideration of risk. Recently, risk management strategies have been developed to change the approach to hazards and risks. Resilience as a safety management theory considers the technical and social aspects of systems simultaneously. Resilience in process industries, as a socio-technical system, has four aspects of early detection, error-tolerant design, flexibility, and recoverability. Meanwhile, process industries' resilience has three phases: avoidance, survival, and recovery, determining the transition between normal state, process upset event, and catastrophic event. There may be various technical and social failures such as regulatory and human or organizational items that can lead to upset or catastrophic events. In the avoidance phase, the upset event is predicted, and thus, the system remains in a normal state. For the survival phase, the system state is assumed to be an upset process event, and the system tries to survive through the unhealthy process conditions or remains in the same state, probably with low performance. In the recovery phase, the system is supposed to be catastrophic, and the emergency barriers are prioritized to show the severity of the consequences and response time, leading to a resumption of a normal state. Therefore, a resilience-based network can be designed for process industries to show its inherent dynamic transition in nature. In this study, network data envelopment analysis (DEA), as a mathematical model, is used to evaluate the relative efficiency of the process industries regarding a network transition approach based on the system's internal structure. First, a resilience-based network is designed to consist of three states of normal, upset, and catastrophic events. Then, the efficiency of each industrial department, which is defined as decision-making units (DMUs), is evaluated using network DEA. As a case study, a refinery that is considered a critical process industry is assessed. Using the proposed model shows the efficient and inefficient DMUs in each of three states of normal, upset, and catastrophic events of the process and the projection onto efficient frontiers. Besides calculating the network efficiency, the performance of each state is extracted to precisely differentiate between DMUs. The results of this study, which is one of the fewest cases in the area of performance evaluation of process industries with a network approach, indicated a robust viewpoint for monitoring and assessment of risks.

风险管理可以定义为在考虑风险的情况下开展和控制组织的协调活动。近来，已经开发了风险管理策略来改变危害和风险的方法。复原力作为一种安全管理理论，同时考虑了系统的技术和社会方面。作为一种社会技术系统，过程工业的弹性具有早期检测，容错设计，灵活性和可恢复性四个方面。同时，流程行业的应变能力包括三个阶段：避免，生存和恢复，确定正常状态，流程不正常事件和灾难性事件之间的过渡。可能存在各种技术和社会失灵，例如监管和人员或组织方面的失误，可能导致不安或灾难性事件。在回避阶段，可以预测发生不正常事件，因此系统保持正常状态。对于生存阶段，系统状态被认为是不正常的过程事件，并且系统尝试通过不正常的过程条件生存或保持在相同的状态，可能性能较低。在恢复阶段，该系统被认为是灾难性的，并且优先设置了紧急屏障，以显示后果的严重性和响应时间，从而恢复到正常状态。因此，可以为过程工业设计基于弹性的网络，以显示其本质上固有的动态过渡。在这项研究中，网络数据包络分析（DEA）作为数学模型，用于评估基于系统的网络过渡方法的过程工业的相对效率。的内部结构。首先，基于复原力的网络被设计为包含正常，不正常和灾难性事件的三种状态。然后，使用网络DEA对定义为决策单位（DMU）的每个工业部门的效率进行评估。作为案例研究，对被视为关键过程工业的精炼厂进行了评估。使用所提出的模型显示了过程的正常，不正常和灾难性事件的三个​​状态中的每个状态的有效和无效DMU，以及在有效边界上的投影。除了计算网络效率外，还提取每个状态的性能以精确区分DMU。这项研究的结果是使用网络方法进行过程工业绩效评估领域中最少的案例之一，