Discuss the problems of forecasting future trends in transport market (20).

While using imports data there are a lot of assumptions. One of them is black market. Imports data is based on how many products were imported from another country legally. Black market is not included there and the ‘underground economy’ has big figures as well. For example, people import cigarettes, alcohol, clothes, food from China in they ordinary transport suitcases. A lot of figures of black market are excluded from the imports data, therefore it becomes unreliable and inaccurate. And if somebody will base their forecast on imports data there main problem would be that the transport project would be inappropriate in real life even though figures said another thing. Money will be wasted on nothing useful.

Last but not least, we base our forecast only on the past trends which do not include : just changed tastes/ fashion, population growth, technology development and new legal restrictions in current time.  Past trends were based also on current demand during that time which was obviously lower 20 years ago than now because people got higher incomes and the transport got cheaper in comparison to another years. Therefore , while we are making our forecasts on the base of past trends we are increasing our chances to be wrong.  We can not predict what the future trend/fashion will be. We can’t predict what the future technology would be.

Even though those problems seem to be unsolvable, we can minimize them by making forecasts not for 20-100 years but for a shorter period of time. That would minimize our problems because the probability that economists will be wrong is reduced by a big amount. Also, in order to solve the black market problem we can use the Delphi technique of different universities. For example, the University of Chicago sent their student to collect the data of how many drugs, cigarettes are imported from another countries. Of course there is another problem: the forecast might be out of date as every day figures changes. But they will be approximately correct and appropriate. It is better than do not use any alternatives to make the data more reliable. Also, we could use much more data in order to make the forecast even more accurate but we have to be sure that these datas are accurate and not out of date.

Despite the many problems, it would be a bigger problem if we would not use methods mentioned above. By using wider range of data and predicting the future demand for a shorter demand, we rescue ourselves of being completely wrong.