Although considered a form of financial voodoo in many industries, accurate forecasting is vitally important in any industry that must make business decisions depending on what the future holds. Demand forecasting is important for manufacturers when determining how much of a product to produce, and equally important for industries such as retailers when trying to predict how much of a product demand will support. There are several commonly used forecasting methods, with the choice primarily guided by the demand one is trying to predict. Here are some methods used with examples of how some industries use them as well as how these methods are used in my current industry; home improvement retail. The first method is historical analogy. This is a qualitative forecasting method, that is to say quite subjective and based on estimates and opinions. This method is commonly used when a company is trying to forecast demand for a new product (Chase, 2006). A company could use past demand for a similar product to predict future demand for the new product. Chase et al used the example of a company that produces toasters and wants to carry a coffee maker. They could reasonably use the toaster story as a model for possible growth. Although the two devices serve very different purposes, their similarity in other aspects is enough to make this method viable. For example, they are both small countertop appliances. Their price points are relatively similar. A specific demographic with demand for a toaster may have similar demand for a coffee maker. Seasonal forecasting is simply a time series forecasting method that capitalizes on a seasonal component of demand. The variation of components can be additive or multiplicative. Unlike a historical analogy forecasting method, a time series is used to predict future demand based on past data (Chase, 2006). This presents itself as a better choice for estimating demand for products with a long enough history to be relevant. Noting that a time series can be defined as "chronologically ordered data that may contain one or more components of demand", including trend and seasonal demand, one can base their forecasts on both components simultaneously (Chase, 2006) . The differences lie in how these components relate to each other. Additive variation assumes that the variation is independent of the trend. Imagine a sales plot of a retail store with the dollar amount on the y-axis and time (in months or years) along the x-axis..