General Approaches to Forecasting
All firms forecast demand, but it would be difficult to find any two firms that forecast demand in exactly the same way. Over the last few decades, many different forecasting techniques have been developed. Many such procedures have been applied to the practical problem of forecasting demand in a logistics system, with varying degrees of success. Almost any forecasting procedure can be broadly classified into one of the following four basic categories based on the fundamental approach towards the forecasting problem that is employed by the technique.
1.     Judgmental Approaches. The essence of the judgmental approaches to address the forecasting issue by assuming that someone else knows and can tell you the right answer. That is, in a judgment-based technique we gather the knowledge and opinions people who are in a position to know what demand will be. For example, we might conduct a survey of the customer base to estimate what our sales will be next month.
2.     Experimental Approaches. Another approach to demand forecasting, which is appealing when an item is “new” and when there is no other information upon which to base a forecast, is to, conduct a demand experiment on a small group of customers and to extrapolate the results to a larger population. For example, firms will often test a new consumer product in a geographically isolated “test market” to establish its probable market share. This experience is then extrapolated to the-national market to plan product launch. Experimental approaches are very useful and necessary for new products but for existing products that have an accumulated historical demand record it seems intuitive that demand forecasts should somehow be based on this demand experience.
3.     Relational/Causal Approaches. The assumption behind a causal or relational forecast is that, simply put, there is a reason why people buy our product. If we can understand what that reason (or set of reasons) is, we can use that understanding to develop a demand forecast. For example, if we sell umbrellas at a sidewalk stand, we would probably notice that daily demand is strongly correlated to the weather – we sell more umbrellas when it rains. Once we have established this relationship, a good weather forecast will help us order enough umbrellas to meet the expected demand.
4.     “Time Series” Approaches. A time series procedure is fundamentally different than the first three approaches we have discussed. In a pure time series technique, no judgment or expertise or opinion is sought. We do not look for “causes” or relationships or factors which somehow “drive” demand. We do not test items or experiment with customers. By their nature, time series procedures are applied to demand data that are longitudinal rather than cross-sectional. That is, the demand data represent experience that is repeated over time rather than across items or locations. The essence of the approach is to recognize (or assume) that demand occurs over time in patterns that repeat themselves, at least approximately. If we can describe these general patterns or tendencies, without regard to their “causes”, we can use this description to form the basis of a forecast.
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