Enterprise risk profiling using asset transaction history and condition measurements

Abstract

Modern asset information systems collect significant quantities of variable-quality data covering operational performance work and financial transactions, as well as measurement history. This paper presents methods to utilise asset configuration data and transaction data such as work history and to provide insight as to where risk of asset failure is currently peaking across an extensive asset portfolio. By ranking the likely potential for failure based on undesirable condition results which are reasonably current, it is possible to generate detailed watch lists to consider and then escalate decisions on the appropriate timing for cost effective intervention. The methods of reporting not only need to manage multi-variate drivers of risk but also the means to effectively communicate where intervention should be focused across the portfolio.