R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis

Hamidreza Seiti; Ahmad Makui; Ashkan Hafezalkotob; Mehran Khalaj; Ibrahim A Hameed

doi:10.1016/j.psep.2022.01.010

R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis

Process Saf Environ Prot. 2022 Mar:159:585-604. doi: 10.1016/j.psep.2022.01.010. Epub 2022 Jan 12.

Authors

Hamidreza Seiti¹, Ahmad Makui¹, Ashkan Hafezalkotob², Mehran Khalaj³, Ibrahim A Hameed⁴

Affiliations

¹ Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
² College of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
³ Department of Industrial Engineering, Islamic Azad University, Robat Karim Branch, Tehran, Iran.
⁴ Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 6009 Alesund, Norway.

Abstract

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry.

Keywords: ANP, Analytic network process; AR, Acceptable risk; AXIOM, The advanced cross-impact option method; BASICS, Batelle scenario inputs to corporate strategies; BM, Bayesian model; BN, Bayesian network; BWM, Best-worst method; CAST, Causal analysis based on systems theory; CIAM, Cross impact analysis model; COVID-19; COVID-19, Coronavirus disease of 2019; Causal chain; DBN, Dynamic Bayesian network; DEMATEL, Decision-making trial and evaluation; EXIT, Express cross-impact technique; GDP, Gross domestic product; HAZOP, Hazard and operability study; HWA, Hybrid weighted averaging; INTERAX, The acronym for the futures research process; ISM, Interpretive structural modeling; MCM, Multi-criteria based model; MICMAC, Cross-impact matrix multiplication applied to classification; OECD, The organization for economic co-operation and development; OWA, Ordered weighted averaging; QFD, Quality function deployment; R.Graph; RBA, Risk-based approach; Risk analysis; SARS, Severe acute respiratory syndrome; SCC, Spearman’s correlation coefficient; SMIC, Cross impact systems and matrices; STAMP, Systems-theoretic accident model and processes; WAA, Weighted arithmetical averaging.