Page 324 - Analysis, Synthesis and Design of Chemical Processes, Third Edition

The situation is further complicated when competing products are considered. For example, if product Y
can be used as a substitute for product X in some applications, then factors that affect Y will also affect
X. It is easy to see that quantifying and predicting changes become very difficult. Torries [3] identifies
important factors that affect both the shape and relative location of the supply and demand curves. These

factors are listed in Table 10.2.

Table 10.2 Factors Affecting the Shape and Relative Location of the Supply and Demand Curves

In order to forecast accurately the prices of a product over a 10- or 15-year project, the factors in Table
10.2 need to be predicted. Clearly, even for the most well-known and stable products, this can be a
daunting task. An alternative method to quantifying the individual supply and demand curves is to look at
historical data for the product of interest.

The examination of historical data is a convenient way to obtain general trends in pricing. Such data
represent the change in equilibrium price for a product with time. Often such data fluctuate widely, and
although long-term trends may be apparent, predictions for the next one or two years will often be wildly

inaccurate. For example, consider the data for average gasoline prices over the period January 1996 to
June 2007, as shown in Figure 10.10. The straight line is a regression through the data and represents the
best linear fit of the data. If this were the forecast for gasoline prices over this period, it would be a
remarkably good prediction. However, even with this predicting line, significant variations in actual