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It is a measure used to evaluate the performance of regression or forecasting models. It is a variant of MAPE in which the mean absolute percent errors is treated as a weighted arithmetic mean. Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3]
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.
Inventory planning involves using forecasting techniques to estimate the inventory required to meet consumer demand. [ 1 ] [ 2 ] [ 3 ] The process uses data from customer demand patterns, market trends , supply patterns, and historical sales to generate a demand plan that predicts product needs over a specified period.
The classic supply-chain approach has been to try to forecast future inventory demand as accurately as possible, by applying statistical trending and "best fit" techniques based on historic demand and predicted future events. The advantage of this approach is that it can be applied to data aggregated at a fairly high level (e.g. category of ...
We have available a forecast of product demand d t over a relevant time horizon t=1,2,...,N (for example we might know how many widgets will be needed each week for the next 52 weeks). There is a setup cost s t incurred for each order and there is an inventory holding cost i t per item per period ( s t and i t can also vary with time if desired).
Calculating forecast attainment periodically (monthly for example) provides visibility to the overall achievement of the plan and the total business bias. The time period of shipping activity should be compared against the forecast that was set for the time period a specific number of days/months prior which is call Lag.
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