HA3 - Defining the Major Uncertainties for Application Our projects consists of a National Cargo Operator for Portugal, a kind of Fed-ex for freight. The idea is to take advantage of inter-modality and connect all major cargo hubs in a single supply chain. Besides operation, this might involve the construction of some infrastructure (like new rail connections between logistic platforms, etc).
Major uncertainties • Total freight movement • Oil Price • Share of the market • Availability of partners • New infrastructure developments (future rail, highways, ports…) • Technical changes in rolling stock, particularly trucks. • Competition • Regulatory framework • Social/Political unrest From this list of possible risks, we consider that the most influential in the success of our project are: • Share of the market – the viability of this project depends on having a minimum demand that justifies the costs of implementing the system. One of the key assumptions is to minimize the cost and aggregated energy consumption of the cargo movement in the country which implies the use of rail as the main mode between the main distribution centers (using trucks for local distribution). • Total freight movement – We consider this risk as very important due to the fact that, even if we have a substantial share of the market, if there is a significant reduction in total freight movement we will not be able to generate enough returns to finance the project. • Oil price – this is significant part of our operational costs but we also consider that high oil prices can be an advantage to us because it will increase much more the price/costs road transportation. Either way, it’s a significant factor to take into account. Major Uncertainties Francisco Furtado – Raul Pires
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Uncertainties characterization We decided to focus our attention on the total freight movement because this is the most determinant over which we have no control (comparing to Share of Market) and also because Oil price uncertainty characterization is a subject of other comprehensive studies. We collected historical data from total national road and rail movements, from 1987 until 20081 for the next 20 years and for this objective we used the Geometric Brownian Motion method. We did a Monte Carlo simulation with 1000 iterations for the years 2010 and 2015 and we obtained the following probability distribution for the freight movement in Portugal for those years. These results are displayed in the following graphs and tables (also in Annex A).
Freight movement (1000 tons) average 283215 max 395903 min 204428 2010 percentiles 10%
chance of having freight below
249131
1000 ton
10%
chance of having freight above
318838
1000 ton
Figure 1 - Freight movement (1000 tons) for 2010
average max min
Freight movement (1000 tons) 307720 494802 154473 2015 percentiles
10%
chance of having freight below
236589
1000 ton
10%
chance of having freight above
385757
1000 ton
Figure 2 - Freight movement (1000 tons) for 2015
In the excel AnnexB we performed the same analysis but in this case we used more pessimistic approach by increasing the probability of having the uncertain factor being negative, reflecting these way what we think is a possible short term trend.
Sources: Eurostat, INE 1
For this year there are only values until September. We used the variation rate to the same period of 2007 to get the values for the rest of 2008.
Major Uncertainties Francisco Furtado – Raul Pires
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