Forecasting Methods In Big-Box Retailing

While considered a form of financial voodoo in many industries, accurate forecasting is vitally important in any industry that needs to make business decisions based on what the future holds. Forecasting demand is important to manufactures when determining how much of a product to produce, and equally as important to industries such as retailers when trying to predict how much product the demand bears. There are several methods of forecasting commonly used, with the choice primarily being guided by the demand one is trying to predict. The following are a few methods used with examples of how certain industries use them as well as how these methods are used in my current industry; home improvement retailing.

The first method is historical analogy. This is a qualitative method of forecasting meaning that it is fairly subjective and based on estimates and opinions. A common use for this method is when a firm is trying to forecast demand for a new product (Chase, 2006). A company could use past demand of a similar product to help predict future demand for the new product. Chase, et al used an example of a firm that produces toasters who wants to carry a coffee maker. They could reasonably use the toaster history as a possible growth model. While the two appliances have very different purposes, their similarity in other aspects is alike enough to make this method viable. For instance, they are both small countertop appliances. Their price points are relatively similar. A specific demographic with a demand for a toaster may have a similar demand for a coffee maker.

Seasonal forecasting is simply a time series method of forecasting that capitalizes on a seasonal component of demand. The component variation can be either additive or multiplicative. Differing from an historical analogy method of forecasting, a time series is used to predict future demand based on past data (Chase, 2006). This lends itself as a better choice for estimating demand of 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 forecast on both components concurrently (Chase, 2006). The differences are in how these components relate to each other. An additive variation assumes that the variation is independent of the trend. Imagine a plot of a retail store's sales with the dollar amount on the y-axis and the time (in months or years) along the x-axis.