Making Decisions Based on Demand and Forecasting
When looking to expand any franchise, it is imperative to first to the math and make sure that it is a financially sound decision. An organization must not only look at the demand in the area, but also the cost at which they can supply the goods to the consumer while still producing a profit. The price can’t be too high or the consumer will be turned away, but if it is too low, then the company will be at a loss financially. The Pizza Company is considering entering the marketplace in the local community but it is important for them to use demand analysis and the forecast for pizza in order to make a decision of whether the area has enough demand and potential revenue at the price with which they are looking to sell their pizzas.
There are a lot of factors that could possibly play a role in this decision. Obviously, supply and demand are the major players. The Pizza Company has to evaluate the demand in the area, but also the minimal cost that they could sell the pizzas but still make a profit. A regression line can be used to “model the relationship between the two variables, price and demand, by fitting a linear equation to the observed data” (http://www.stat.yale.edu/). The price is considered to be an explanatory variable, and the demand is considered to be a dependent variable in the first situation. Other variables that also play a role are the price that competitors are selling their pizzas for, the average household income in the area, as well as the cost of advertising.
Based on a regression line created from the data given in this situation, there is a negative relationship between price and demand, which is to be expected. The more a consumer has to pay, the less likely they are to purchase that brand. This is not always the case, especially when analyzing the demand for luxury goods, but in this instance it is relevant. The regression equation comes out to y = -20195x + 738345 with a coefficient of determination of R² = 0.17292. Based on the coefficient of determination, only 17.3 percent of the time can the relation between the two variables be explained. The chart below shows visually.
But, a key aspect of this comparison is a look when the household income is compared to the demand instead of price. In that instance, the regression line is y = 7.7483x + 202910 with a coefficient of correlation of R² = 0.42616. Although in this second comparison, there is only an explanation of 42.6