Development of a risk assessment selection model for asset maintenance decision making

In today’s industries, asset maintenance plays a strategic role in sustaining the organization’s competitiveness through enhancing equipment availability, reliability and productivity. In recent years, considerable research work on asset management (AM) has been undertaken. This stems from the fact that AM considers the assets life cycle where the operational and maintenance phase is important. Core to asset maintenance is the proposed use of various risk assessment techniques/tools. These tools propose a structured approach through which critical failure modes are identified, analyzed and mitigated. Commonly applied risk assessment tools in AM include the failure mode and effect analysis (FMEA), static/dynamic fault tree analysis (FTA) and static/dynamic Bayesian networks (BN). Despite considerable effort directed towards developing individual risk assessment (RA) tools, few papers propose a structured framework that allows selection of tools best suited for the organization, considering the often varying business or operational context. Thus, this paper proposes a conceptual risk assessment tool selection model. Based on an extensive literature review, the model enumerates generic selection criteria accounting for the well-known ‘factors of production’, i.e. manpower (personnel), machines (assets complexity), methods (procedures), and materials (tools and aids). Next, the selection criteria are assigned priority weights based on the analytic network process (ANP) methodology that accounts for the type of RA tool and business context. Finally, applicability of the conceptual model as an audit tool where the organization assesses its suitability against the varied RA tools is demonstrated.no ISBN or ISSNstatus: publishe