Maintenance and Repair

Abstract

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.