Asset Integrity Management: Operationalizing Sustainability Concerns

R. M. Chandima Ratnayake
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R. M. Chandima Ratnayake: Center for Industrial Asset Management (CIAM), Faculty of Science & Technology, University of Stavanger-UiS, N-4036, Stavanger, Norway

Chapter 14 in Handbook on Business Information Systems, 2010, pp 323-357 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractThe complexity of integrating the concept of sustainable development and the reality of asset integrity management (AIM) practices has been argued. It is important for establishing and consummating an AIM system with practical application value as a whole over the integrity management system. Identifying and prioritizing asset performance through identified risk, detecting and assessing data, resulting in saved costs in the areas of design, operation, and technology application are addressed through sustainability lenses. The research study surfaced over a project initiated to develop governing documents for a major operator company for assessing asset integrity (AI), focusing particularly on design, operational, and technical integrity. The introduction of a conceptual framework for AIM knowledge along with coupled tools and methodologies is vital, as it relates to sustainable development regardless of whether the particular industry belongs to the public or private sector. The subsequent conceptual framework for sustainable asset performance reveals how sustainability aspects may be measured effectively as part of AIM practices. Emerging AIM practices that relate to sustainable development do emphasize design, technology, and operational integrity issues for splitting the problem into manageable segments and alternatively, measure organizational alignment for sustainable performance. The model uses the analytic hierarchy process (AHP), a multicriteria analysis technique that provides an appropriate tool to accommodate the conflicting views of various stakeholder groups. The AHP allows the users to assess the relative importance of multiple criteria (or multiple alternatives against a given criterion) in an intuitive manner. This holistic approach to managing AI provides improvement initiatives rather than a seemingly ad hoc decision making. The information in this chapter will benefit plant personnel interested in implementing an integrated AIM program or advancing their current AIM program to the next level.

Keywords: Information Systems; Systemization; Business Process Development; Health Care; Industrial Management; Data Management; Semantic Web Services; Knowledge Management; Risk Management (search for similar items in EconPapers)
Date: 2010
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