Abstract
Complex system governance (CSG) is an emerging field encompassing a framework for system performance improvement through the purposeful design, execution, and evolution of essential metasystem functions. The goal of this study was to understand how the domain of asset management (AsM) can leverage the capabilities of CSG. AsM emerged from engineering as a structured approach to organizing complex organizations to realize the value of assets while balancing performance, risks, costs, and other opportunities. However, there remains a scarcity of literature discussing the potential relationship between AsM and CSG. To initiate the closure of this gap, this research reviews the basics of AsM and the methods associated with realizing the value of assets. Then, the basics of CSG are provided along with how CSG might be leveraged to support AsM. We conclude the research with the implications for AsM and suggested future research.
Highlights
Twenty-first-century organizations and complex technological installations operate in conditions and environments characterized by a lack of clarity of situational awareness involving overall opaqueness, deep uncertainties, and complexity with large numbers of richly interdependent and dynamically interacting elements
Asset management (AsM) has emerged from engineering as a structured approach to organizing complex organizations to realize the value of assets while balancing performance, risks, costs, and opportunities [1,2,3]
Complex system governance (CSG) is an emerging field, representing an approach to improving system performance through the purposeful design, execution, and evolution of nine essential metasystem functions that provide for the control, communication, coordination, and integration of a complex system
Summary
Twenty-first-century organizations and complex technological installations operate in conditions and environments characterized by a lack of clarity of situational awareness involving overall opaqueness, deep uncertainties, and complexity with large numbers of richly interdependent and dynamically interacting elements Their behavior and future conditions are difficult or almost impossible to predict accurately. Those uncertainties relate to the degree of belief that an analyst has in the representativeness or validity of a model and its predictions Complex organizations face both types of uncertainties and often address these issues by using various models and tools to help decrease uncertainties and quantify risks in their asset management decision-making process.
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