The Implementation of Resilience Engineering to Deal with Climate Change Impact

Climate change is a very interesting subject, because it has been compelling us to review our thinking, our development concepts, practices, paradigms, models, and so on from the view of the environment. In such introspections, we may identify the mistakes, weaknesses and unintended consequences of our development concepts, practices; and we may try to overcome those mistakes and will be conscious to avoid such shortcomings in the future. Land and water resources degradation are the major problems in the world. Poor land use practices and improper management systems have played a significant role in causing high soil erosion rates, sediment transport, and loss of agricultural nutrients. This causes various effects on resource bases like deforestation, expansion of residential areas, and agricultural land. The watershed is also facing high erosion due to the effects of intense rainfall of the watershed which aggravates the land cover change of the watershed. This continuous change in land cover has influenced the water balance of the watershed by changing the magnitude and pattern of the components of stream flow between surface runoff and groundwater flow in increasing the extent of the water management problem. In this study, the assessments of resilience engineering and its function as a framework for the environmental issues, such as resilience and management of water and public infrastructure has been done. After risk assessment, we obtained five risk levels and their corresponding resilience levels. The two can be regarded as negatively correlated. And at the same time, four corresponding adaptions are also proposed according to the resilience levels.

An Analytic Framework to Assess Organizational Resilience

BACKGROUND: Resilience engineering is a paradigm for safety management that focuses on coping with complexity to achieve success, even considering several conflicting goals. Modern sociotechnical systems have to be resilient to comply with the variability of everyday activities, the tight-coupled and underspecified nature of work, and the nonlinear interactions among agents. At organizational level, resilience can be described as a combination of four cornerstones: monitoring, responding, learning, and anticipating. METHODS: Starting from these four categories, this article aims at defining a semiquantitative analytic framework to measure organizational resilience in complex sociotechnical systems, combining the resilience analysis gridand the analytic hierarchy process. RESULTS: This article presents an approach for defining resilience abilities of an organization, creating a structured domain-dependent framework to define a resilience profile at different levels of abstraction, and identifying weaknesses and strengths of the systemand potential actions to increase system's adaptive capacity. An illustrative example in an anesthesia department clarifies the outcomes of the approach. CONCLUSION: The outcome of the resilience analysis grid, i.e., a weighed set of probing questions, can be used in different domains, as a support tool in a wider Safety-II oriented managerial action to bring safety management into the core business of the organization.

The Impact of Redundancy and Teamwork on Resilience Engineering Factors by Fuzzy Mathematical Programming and Analysis of Variance in a Large Petrochemical Plant

BACKGROUND: Resilience engineering (RE) is a new paradigm that can control incidents and reduce their consequences. Integrated RE includes four new factors—self-organization, teamwork, redundancy, and fault-tolerance—in addition to conventional RE factors. This study aimed to evaluate the impacts of these four factors on RE and determine the most efficient factor in an uncertain environment. METHODS: The required data were collected through a questionnaire in a petrochemical plant in June 2013. The questionnaire was completed by 115 respondents including 37 managers and 78 operators. Fuzzy data envelopment analysis was used in different α-cuts in order to calculate the impact of each factor. Analysis of variance was employed to compare the efficiency score means of the four above-mentioned factors. RESULTS: The results showed that as α approached 0 and the system became fuzzier (α = 0.3 and α = 0.1), teamwork played a significant role and had the highest impact on the resilient system. In contrast, as α approached 1 and the fuzzy system went toward a certain mode (α = 0.9 and α = 1), redundancy had a vital role in the selected resilient system. Therefore, redundancy and teamwork were the most efficient factors. CONCLUSION: The approach developed in this study could be used for identifying the most important factors in such environments. The results of this study may help managers to have better understanding of weak and strong points in such industries.