Brief presentation evaluation of the fleet economic performance
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Evaluation of the Fleet Economic
Performance
methodology summary
by Gláucio Bastos, M.B.A., Ch.E.
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abstract
target: vessels fleet productivity definition from relevant
economic data whatever the vessels chacracteristics or
their activity and location
due to the difficulty of obtaining complete data on costs,
revenues, employment and activity of the entire fleet, we
set a representative and otpimized sample from the total
number of vessels for this survey which allows to calculate
the revenues by ship according to its characteristics and its
activity
statistical analysis to define a model for fleet’s gross revenue
allows to asses its activity level
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data collection
annual surveys are conducted to update the collection of
the following data:
activity (objective, areas of operation, duration and
number of trips)
diversification of activities (besides the main one)
ship (physical characteristics, purchase price, insurance
value, as is estimated value)
major equipment (description and cost of acquisition
and maintenance of essential parts for the main activity)
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data collection
annual surveys are conducted to update the collection of
the following data :
other onboard equipment (motor, electronic,
mechanical, for storage)
revenues and costs (transaction value, operating costs -
fuel, oil, food, dockage, insurance premiums, repairs,
maintenance, etc).
crew (quantity, functions, salaries)
vessels are grouped into segments each with common
characteristic bands
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sampling
for obtaining reliable economic indicators covering the
entire fleet, the sampling plan is optimized
fundamental to a minimum accuracy level of economic
indicators
the minimum (best) sample is estimated for each segment
so that in at least 80% of the measurements, the range of
variation (precision) around vessels average revenue is 40%
with 95% certainty (as required by the regulator)
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sampling
vessels are selected for an interview according to a
systematic random sampling procedure
it is applied within each segment to ensure location
and vessel length representativity
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gross revenue model
each segment has its linear model
data parameters with significance are (costs are not
significant for the revenue):
ship length
crew number
annual operation months number (least significant at
25%)
held life cycle
accuracy is good mainly due to there is a specific model for
each fleet segment
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gross revenue model
there is a correlation between the following parameters:
length and crew number
operation months number and crew number
crew number and lifecycle (least significant at 15%)
e.g. in the case of segment “TrawlerNex length<12m.”:
population size: 237
minimum size of the representative sample : 6
Gross_Revenue = -7.03 - 19.1 x Length – 11.7 x Crew –
- 103 x Months – 15.4 x LfCycl + 8.63 x Crew x Length +
+ 48.4 x Crew x Months + 4.34 x Crew x LfCycl
(Revenue in $, Length in meters, LfCycl in years)
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results
* evaluation of the sampling distribution algorithm*
comparison of the distribution between sample and population by region
[SRG,AQ,...,PL,SB] and by length [12 – 23 meters]
e.g. for the fleet segment “DemTrawl 12-24m“:
is good the length representation and excellent the spatial representativeness
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results
model of the overall revenue is used to assess the
total income of the fleet segments
cost data allows assessment of the overall and
segment gross profit and the direct employment
level of the fleet
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results
the model of the gross revenue allows definition of each fleet
segment contribution for total gross income and total gross profit
economic data allow assessment of the intermediate costs for a
significant sample of vessels covering the full range of activities