Page 324 - Practical Design Ships and Floating Structures
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         80
                                Freight Rate (Gaussian Distribution)
         70
           I
           i   Framework6                          rl
         60               4                                        Framework4
                          ‘\         Framework1  /  \  d
         50
       h
       C
       2 40
       0-                8
       E
       LL              41
         30
                      I
           I         8
         20  I
                     8
         10
         0
           -21  -18  -15  -13  -11  -9   -6   -4   -1   1   3   5   8   10  13  15  18  20  23  25  28
                                          NPV
              Fig 4. Distribution of NPVs for three Frameworks and variation in Freight Rate

      While  this shows  a  difference  in mean NPV  of  $6.3M, this  is only  50% higher  than  one standard
      deviation. The overlap in Fig 4 indicates that there is a probability that Framework 1  might actually be
      better than  Framework  4, calculated  by the program  as  13.3%. There  is a  zero probability  that  6  is
      better than 4, and less than 1% that 6 is better than 1, so the over-designed ship is clearly not an option.

      So although the owner is paying a ‘premium’ of $10M by designing for jumboisation, it is more than
      repaid by  its greater  earning ability.  Other  variants  which  might  be  explored  would  be  changes  in
      growth rate trends, different extents of jumboising, and applying alternative statistical distributions for
      freight rate such as triangular or beta.


      6  CONCLUSIONS
      Simulation  methods  and  DCF  techniques  are  not  new,  but  bringing  them  together  into  a  formal
      procedure  for  evaluating  alternative  upgrading  scenarios  is  new.  With  commercial  pressures  to
      minimise capital expenditure, even at the expense of operational difficulties later in a project’s life, it is
      important  to  have  agreed  methods  of  assessing  under  what  circumstances  a  degree  of  additional
      expenditure to facilitate later upgrading is justified. A simulation approach provides greater insight into
      the  possible  influence  of  changes  in  market  trends  or  prices,  as  well  as possible  impact  of  new
      regulatory requirements on a project’s  life cycle cost. With the results presented as probabilities,  the
      extent of risk can be gauged, so that the most cost-effective solution can be assessed. The methodology
      has been successful applied to a wide range of MTO products including offshore production platforms,
      process plant, power stations and steel mills. The familiarity of the spreadsheet and the availability of
      linked  probabilistic  software  makes  the  introduction  of  such  a  method  into  an  organisation
      straightforward.
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