analysis of advanced shipboard technologies and reduced crews has not
received extensive attention, particularly their impact on fleet competitiveness.
One of the questions to the ATOMOS project has been to ascertain to
what extent and under what conditions advanced shipboard technology Systems (such as those
developed in the project) would enhance the competitiveness of the fleet of the EU. In
order to answer this question, a comprehensive cost-benefit analysis is warranted. The
cost-benefit analysis would essentially compare two equivalent ships, one of conventional
technology and crew composition, and one of advanced technology and reduced manning, in
terms of some competitiveness criterion such as Required Freight Rate (RFR). An
appropriately defined sample of ships would be necessary in order to draw some
conclusions.
Notice the use of the RFR as the competitiveness criterion. The RFR is
the break-even freight rate for which the Net Present Value (NPV) of the time stream of
the discounted differences between revenues and expenses over the lifetime of a ship is
zero. This criterion is widely used in evaluating and comparing maritime transportation
investment alternatives. Additional competitiveness criteria were defined in Psaraftis et
al (1992).
In spite of the apparent simplicity of such an approach, the actual
implementation of such a methodology is by no means easy. Several kinds of difficulties
are important. For instance,
・The amount of data necessary for doing a comprehensive analysis
along the above lines is immense.
・Some of the necessary data is difficult to collect, may be
incomplete, or sometimes simply nonexistent.
・Calculating some components of the cost- benefit equation is
extremely difficult or even impossible.
An extensive effort was undertaken to collect the vast amount of data
necessary for the analysis (see Psaraftis et al., 1994a for details). Due to lack of
complete homogeneity in the quality of data collected, it was decided that our
cost-benefit analysis methodology should be structured into three hierarchical levels: I,
II, and III. Due to space limitations this paper cannot present details of the analysis in
all three levels (the reader is referred to Psaraflis et al (1994b) and Psaraftis (1996)
for more details on the methodology, especially on Levels I and II). Here we focus more on
Level III which can support some general conclusions, and present only a summary of
findings of levels I and II.
