Adopting results based management in the non-profit sector: Trócaire’s experience

The increasing emphasis on aid effectiveness, accountability and impact measurement in international development and humanitarian work has generated a requirement for high quality internal systems for the management of programmes. To help to address this requirement, Trócaire adopted Results Based Management in the 20 countries in which it works. This paper provides an overview of Trócaire’s RBM journey, including the process of embedding the new approach in the organisation, lessons learnt from this process, the subsequent benefits that are emerging at field programme level and the challenges going forward.

Guiding stakeholders to participate in Marine Strategy Framework implementation

Large marine areas and regional seas present a challenge in terms of management. They are often bordered by numerous maritime jurisdictions; with multi-use and multi-sector environments; involving varying governance arrangements; and generation of sufficient levels of data to best inform decision-makers. Marine management at the regional scale involves a range of mechanisms and approaches to ensure all relevant stakeholders have an opportunity to engage in the process; and these approaches can differ in their legal and regulatory conditions. At present, no such comparable structures exist at the transnational level for the ecosystem-based management of the Celtic Sea. Against this backdrop, a participative process, involving representatives from differing sectors of activity in the Celtic Sea spanning four Member States, was established for the purpose of identifying realistic and meaningful management principles in line with the goals of the Marine Strategy Framework Directive.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.