Risk-Informed Decision-Making in Sustainable Management of Industrial Assets
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: closed (22 June 2021) | Viewed by 24984
Interests: asset management; reliability; risk analysis, maintenance optimization; operational research
Interests: asset management; intelligent manufacturing (I4.0); lean manufacturing; six sigma; optimization; simulation modeling; supply chain management; operations management; multiple-criteria decision analysis; sustainability
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Modern organizations operate in a complex environment composed of closely interdependent systems and sub-systems. Moreover, the business environment of the modern enterprise is increasingly exposed to deep uncertainties due to the evolution of the market and of customers, budget restrictions, changing regulatory framework and/or deregulation, advent of new technologies, climate change, pandemics, etc. So-called high-impact, low-frequency (HILF) events instigated by the above-mentioned causes have become important influence factors.
Furthermore, organizations should manage the replacement of their assets as they reach the end of their lifecycle or become obsolete due to technological changes and environmental issues. Numerous organizations are now facing the need for growth and increasing efficiency of clean enegy that reduces pollution.
Complexity and interdependencies increase the uncertainty that make systems unstable, creating conditions for cascading failures and causing serious threats to themselves and to society in general. Such breakdowns may consist of a) serious physical damage to assets and/or b) large functional disruptions caused by internal disturbances within a major organization or external factors such as market crashes, pandemics, disruptions of supply chains, etc. These external risks are impossible to control. The COVID-19 pandemic is an example. Thus, modern organizations must increase their resilience and develop the means to cope with this reality in order to remain economically viable in a sustainable way.
Recent trends in research and practice indicate that combining the concepts of structured asset management (AM) and resilience may provide an efficient framework in this regard.
In such a context, decision-making as an essential AM activity is quite challenging. A comprehensive AM approach is vital for modern organizations aiming to maximize the value of their assets throughout their lifecycle. It is essential to strike the right balance between numerous competing factors, such as performance, risks, benefits, costs, opportunities, short-term vs. long-term goals and sustainability.
Existing models show limits in effectively treating complexities and deep uncertainties. Decision-makers often place overwhelming importance on them while ignoring their limitations. Furthermore, the impact of other barely quantifiable or intangible factors (e.g., public perception, political influence, reputation of an enterprise, etc.) could occasionally become dominant in the final decision-making, but it is quite difficult to adequately account for them. It should be recognized that decision-making is not just a technical process or a modeling and data issue. The human/organizational factors and cognitive and motivational biases must be considered in order to ensure that AM decisions are credible and effective.
Thus, new concepts and approaches in AM modeling and the related decision-making process are needed. A holistic, risk-informed decision-making (RIDM) approach has appeared as a promising path forward, capable of capturing insights from various concepts, models, and their limits, the complexity and uncertainties, as well as the strength of knowledge of analysts, experts, and decision-makers.
In this Special Issue of Sustainability, we invite submissions focused on “Risk-Informed Decision-Making in Sustainable Management of Industrial Assets” in the context of the overall operating and business complexity. Thus, of interest are papers that contribute to the body of knowledge by examining links between AM, resilience, and complexity as well as the impact of various influencing factors in AM decision-making in both quantitative and qualitative maners.
Dr. Dragan Komljenovic
Prof. Dr. Georges Abdul-Nour
Prof. Dr. François Gauthier
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- asset management
- risk-informed decision-making