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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.
Inventory optimization refers to the techniques used by businesses to improve their oversight, control and management of inventory size and location across their extended supply network. [1] It has been observed within operations research that "every company has the challenge of matching its supply volume to customer demand.
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.
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]
Accurate forecasting will help retailers reduce excess inventory and thus increase profit margin. Accurate forecasting will also help them meet consumer demand. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and a consensus process.
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 ...
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