Demand-Driven Forecasting: A Structured Approach to Forecasting, 2nd Edition

CHAPTER 5

Quantitative Forecasting Methods Using Time Series Data

In Chapter 3, we discussed the two broad classes of quantitative methods, time series methods and causal methods. Time series methods are techniques built on the premise that future demand will mimic the pattern(s) of past demand. Time series methods rely on the identification of patterns (i.e., trend, seasonality, and/or cyclical) within the past demand history of those items being forecasted and assume the patterns will continue into the future. The basic premise of causal methods is that future demand of a particular product is closely associated with (or related to) changes in some other variable(s). For example, changes in demand can be associated with variations in price, advertising, sales promotions, and merchandising as well as economic and other related factors. Therefore, once the nature of that association is quantified, it can be used to forecast demand. Another key attribute of causal modeling is the ability to shape demand using what-if analysis utilizing the parameter estimates or elasticities associated with the causal factors to predict changes in demand as a result of varying the levels of the relationship variables. By changing price, say, from $1.34 to $1.40, you can determine what the impact will be on demand for that particular brand or product. In this chapter we discuss these statistical methods in more detail from a practical application standpoint using the beverage data set.

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