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Integrating business impact analysis and risk assessment for physical asset criticality analysis: A framework for sustainable operations in process industries.

Authors :
Aghabegloo, Mohsen
Rezaie, Kamran
Torabi, Seyed Ali
Yazdani, Maziar
Source :
Expert Systems with Applications. May2024, Vol. 241, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• Proposing a physical asset criticality analysis decision model based on BIA. • Risk Appetite and physical asset continuity measures are related. • Defining sub-criteria for impact and likelihood estimation in criticality analysis. The paper tackles the issue of managing physical assets in asset-intensive industries, where continuity plans are crucial for business operations during disruptions. However, resource constraints often hinder companies from implementing these plans for all their assets. Moreover, efforts to maximize resource utilization efficiency and minimize environmental impacts are limited to isolated disciplines, necessitating an integrated approach across natural, social, and technological systems. To address these challenges, this study proposes a framework based on a hybrid fuzzy BWM-TOPSIS methodology for identifying critical physical assets. The framework incorporates Business Impact Analysis (BIA) to determine essential products, a physical asset risk assessment matrix (PARAM) to assess critical assets based on likelihood and impact, and a mathematical model to estimate asset continuity parameters. The framework is applied to a petrochemical company in a case study, offering a guide for estimating the probability and impact of physical asset failure while accounting for inherent uncertainties. By adopting this framework, companies can efficiently manage their physical assets, prioritize continuity plans, and address resource constraints and uncertainties. This research contributes to practical tools and approaches that assist industries in sustaining operations and ensuring business continuity during disruptions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09574174
Volume :
241
Database :
Academic Search Index
Journal :
Expert Systems with Applications
Publication Type :
Academic Journal
Accession number :
175345137
Full Text :
https://doi.org/10.1016/j.eswa.2023.122737