Maritime transport covers about 90%of world trade and is seen as one of the most cost-effective way of transporting goods. In order to safely accommodate vessels calling into ports, tugs are operated to assist various sized vessels with navigating, manoeuvring and berthing. Competition within the ship handling market has always been fierce. Boskalis Towage, through a network of joint ventures, offers harbour- and terminal towage services around the world, comprised of about 450 vessels. Operating a large fleet comes large Operating Expenses (OPEX). In order to deliver a more cost-effective service, Boskalis turns to business intelligence (BI) to help with the decision-making process. Maintenance is crucial for the operation of the vessels, but also a large part of the expenses. Large deviations in maintenance costs and operational performance exist between the joint ventures. However, the reasons behind these deviations are yet unclear due to the absence of continuous monitoring capabilities. The objective is therefore to increase the management capabilities of detailed performance monitoring of M&R expenses and performance of tugs, through which possible improvements can be determined. The M&R of tugs is influenced by various factors, such as utilisation, operational location and installed equipment. This research has evaluated the current Enterprise Performance Management (EPM) of Boskalis Towage and used literature on Maintenance Performance Management (MPM) in order to erect a multi-criteria hierarchical function-specific framework which is a framework for maintenance performance monitoring. The presented framework covers aspects of alignment with business objectives and plan, strategy and implementation. Moreover, the framework presents the maintenance function and its areas of performance measuring of equipment-, cost- and process performance across the organisational hierarchy. By performing a regression analysis of the maintenance data, this has also resulted in an increased understanding of the relationship between maintenance costs, operational data and vessel characteristics. As a result, an equation with acceptable statistical accuracy for running repairs has been found. The implementing of the MPM framework on the maintenance of tugs has resulted in the MPM model. This model focuses on the running repairs and dry docking direct costs and activities, incorporating basic maintenance types, i.e. Preventive Maintenance (PM) and Corrective Maintenance (CM). Running repairs have been evaluated in the time domain while the evaluation of dry dockings have been evaluated as events due to their relationship with regulatory surveys. The model incorporates operational-, maintenance- and financial data, tug specifications and operational conditions in order to evaluate maintenance performance. Maintenance Performance Indicators (MPIs) have been established to determine deviations and deficiencies among the different aspects of M&R. Challenges were faced on the aspects of data quality and data management, especially in the areas of maintenance process. The model has been validated through a detailed evaluation of five selected tugs. The underlying reasons for the underperformance of these tugs have been found and verified with knowledgable bodies and thus validating the model and its ability to determine underperformance and deviations. An alternative performance analysis method, Data Envelopment Analysis (DEA), has been researched and shows promise. The Slack-Based Measure (SBM) DEA model is a non-parametric frontier approach, based on Linear Programming (LP), to evaluate performance based on slacks (room for improvement) of Decision Making Units (DMUs). The method is capable of determining similarities in inefficiency in maintenance performance, comparing it to results from the MPM model. It, therefore, proves the method is capable of quick evaluation of maintenance performance and of determining new maintenance targets. However, the model is simplistic and requires further detailing and is unable to determine underlying reasons for underperformance. The regression equation and DEA have been used to formulate a so-called performance region which describes the lower- and upper bounds of the running repair costs for individual tugs. Unfortunately, data quality doesn’t allow for the determining of these bounds for all vessels nor for dry docking. New key benchmarking targets have been established through quartile values for respective Maintenance Performance Indicators (MPIs). With this newly obtained knowledge, future maintenance performance evaluation of tugs can now be performed with a higher detail through analysis conducted with the developed MPM model.