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Demand forecasting plays an important role for businesses in different industries, particularly with regard to mitigating the risks associated with particular business activities. However, demand forecasting is known to be a challenging task for businesses due to the intricacies of analysis, specifically quantitative analysis. [4]
There are two forecasting sub-problems: predicting time-phased demand and predicting demand response to the pricing decisions. In yield management-type applications, predicting time-phased demand, at a very granular level, is central since these applications are characterized by fixed capacity against which demand must be balanced by use of ...
Because forecast errors are given, companies often carry an inventory buffer called "safety stock". Moving up the supply chain from end-consumer to raw materials supplier, each supply chain participant has greater observed variation in demand and thus greater need for safety stock. In periods of rising demand, down-stream participants increase ...
Load forecasting (electric load forecasting, electric demand forecasting). Although "load" is an ambiguous term, in load forecasting the "load" usually means demand (in kW) or energy (in kWh) and since the magnitude of power and energy is the same for hourly data, usually no distinction is made between demand and energy. [16]
Forecasts can relate to sales, inventory, or anything pertaining to an organization's future demand. The tracking signal is a simple indicator that forecast bias is present in the forecast model. It is most often used when the validity of the forecasting model might be in doubt.
The problem above is cast as one of maximizing profit, although it can be cast slightly differently, with the same result. If the demand D exceeds the provided quantity q, then an opportunity cost of () represents lost revenue not realized
For example, a retailer might be interested in predicting store-level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These types of problems can be addressed by predictive analytics using time series techniques (see below).
Economic forecasting is the process of making predictions about the economy. Forecasts can be carried out at a high level of aggregation—for example for GDP, inflation, unemployment or the fiscal deficit—or at a more disaggregated level, for specific sectors of the economy or even specific firms.