AbstractBusiness disturbances in terms of pandemics such as COVID-19 or even the sudden fluctuations in the energy supply and demand due to geopolitical conflicts have revealed the need for more resilient approaches and processes in the maintenance and asset performance management domain. Hence, this thesis aims to develop an agile asset performance management framework that can facilitate strategic and dynamic methodology for the decision-maker to sustain the effectiveness and efficiency of the maintenance and asset management programs in different operating contexts.
The proposed framework consists of eight fundamental processes: select asset, set expectations, specify scenarios, start analysis, shape packages, standardise execution, supervise results and sharpen more; the processes have been validated through the implementation in the energy sector. A set of case studies are presented, each verifying one process or three processes together. Results from the case studies suggest that the proposed framework can lead to identifying an innovative seamless integration between the asset performance management processes when the selected decision-making tools, including the decision-making grid (DMG), the analytical hierarchy process (AHP) and the Knapsack method, are properly incorporated. Hence, the suggested approach implementation is expected to promote the practical use of its decision-making tools and enable asset stakeholders to break silo working for clear communication around asset performance. Furthermore, the suggested approach showed apparent flexibility demonstrating the risk line of sight from the failure mode level to the unit level and enabling the seamless update of the asset's risk assessment in response to the operating context changes.
This thesis contributes to the operations and systems management literature concerning one of its core activities, the maintenance and asset performance management, through an innovative, systematic and practical framework that introduces a new set of tools created during the framework development. The main contribution of this work can be classified into three broad categories of innovations. Firstly, the nested decision-making grid, which differentiates the inherent, achieved and operational equipment criticality via the application of the DMG concepts in the asset criticality assessment process, simplifying the process and reducing subjectivity, leading to more accurate results. Secondly, the risk reduction factor (RRF) and the value-added indicator (VAI), which represent two new parameters to assess the maintenance task effectiveness in reducing non-financial risk and evaluate the task efficiency in reducing financial risk, enabling asset owners to select between different maintenance packages while balancing between cost and risk. Finally, the nested criticality grid (NCG), which demonstrates the assets risk profile from the failure mode level up to the unit level, and enables an informative decision- making process where the asset owner can wisely distribute the maintenance budget or achieve efficient cost savings, using the knapsack method and AHP.
|Date of Award||26 Jun 2023|
|Supervisor||Ashraf Labib (Supervisor) & Dylan Jones (Supervisor)|