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To make a more accurate forecast, one should look beyond the data that is easy to find and search for the underlying factors that are also relevant to the future outcomes. A team who was studying demand growth for copy paper faced this challenge. When demand for copy paper was growing at a rate faster than the economy, they had to find other factors that could be causing the growth. They tested the relationship between reductions in cost and number of tons produced. The resulting demand curve proved that cost reductions played an important role in past demand growth. The team concluded that because declines in the price of copy paper were not likely, future increases in demand would rely mainly on economic growth.
They used this newfound information in creating their next forecasts. In another example, a team forecasting demand for maritime satellite terminals exceeded their penetration curves for each of the five types of ships. The team recreated forecasts by taking into effect other factors. They added the depressing effect of the growing oil glut and took out the historical trends the unnatural demand growth that had been a result of the Falklands war. Three years later, these new forecasts were within 1 % of the forecast. In the early 1980 s, an electric companys management team tried to study end-user demand in a less traditional way by dividing electricity demand into three categories: residential, commercial, and industrial.
Each category was studied separately by changes within that category (ex. changes in home size of residential users). After accounting for these factors, the companys new forecasts were much smaller than the previous forecasts. The company then cancelled two $ 700 million generating plants in the planning stage. In 1983, a computer industry company did a study to refute beliefs that the computer industry would keep rising rapidly because of the amount of white-collared workers. Its forecasts took for account that more than two-thirds of white-collared workers (such as actors and elevator operators) do not require a PC.
Sure enough, the market began to flatten the following year. In the last example, a company was thinking about buying a maker of video games because the overall market penetration for video games was only 10 % of U. S. households. This meant there was a lot of room for growth in the video game market.
Before the company made forecasts on this available data, they decided to look at other factors in the video game market by categorizing. When categorizing the market by incomes and childrens ages the company found that 75 % of the main target market, families with incomes above 50, 000 and children ages 6 to 15, was already penetrated. This led to the decision to not buy the maker of video games. Shortly after, video game sales declined.
Free research essays on topics related to: forecasts, computer industry, copy, video, video game
Research essay sample on Computer Industry Video Game
