- ABC analysis
- Backorders
- Container shipments
- Economic order quantity
- Fill Rate
- Financial impact of accuracy
- Inventory accuracy
- Inventory control
- Inventory costs (carrying costs)
- Inventory turnover
- Lead time
- Lead demand
- Min/Max Planning
- Minimal Order Quantities (MOQ)
- Multichannel Order Management
- Optimal service level formula
- Perpetual Inventory
- Phantom Inventory
- Prioritized Ordering
- Product life-cycle
- Reorder point
- Replenishment
- Safety stock
- Service level
- Stock-keeping unit (SKU)

In statistics, the accuracy of forecast is the degree of closeness of the

In this article, we adopt a statistical viewpoint primarily relevant to commerce and manufacturing, especially for inventory optimization and demand planning areas.

- to choose among several forecasting models that serve to estimate the lead demand which model should be favored.
- to compute the safety stock typically assuming that the forecast errors follow a normal distribution.
- to prioritize the items that need the most dedicated attention because raw statistical forecasts are not reliable enough.

In other contexts, such as strategic planning, the accuracy estimates are used to support the what-if analysis, considering distinct scenarios and their respective likelihood.

The most important factor driving the value of the accuracy is the

- larger areas, such as national forecasts vs local forecasts, yield more accuracy.
- idem for longer periods, such as monthly forecasts vs daily forecasts.

Then, once a level of aggregation is given, the quality of the forecasting model plays indeed to primary role in the accuracy that can be achieved. Finally, the accuracy decreases when looking further ahead in the future.

Indeed, once the data is available, it is always possible to produce perfectly accurate forecasts, as it only requires mimicking the data. This single question has kept

The accuracy of the forecasts can only be practically measured against available data; however, when the data is available, those

Overfitting problems can lead to large discrepancies between the empirical accuracy and the real accuracy. In practice, a careful use of backtesting can mitigate most overfitting problems when forecasting time-series.

- MAE (mean absolute error)
- MAPE (mean absolute percentage error)
- MSE (mean square error)
- sMAPE (symmetric mean absolute percentage error)
- Pinball loss (a generalization of the MAE for quantile forecasts)
- CRPS (a generalization of the MAE for probabilistic forecasts)

In practice, a metric should be favored over another based on its capacity to reflect the costs incurred by the company because of the inaccuracies of the forecasts.

Indeed, the

Also, it's quite important not to perform any planning implicitly assuming that the forecasts are

- Video. Accuracy in sales forecasting, Matthias Steinberg, September 2011
- The best forecast error metric, Joannes Vermorel November 2012
- Accuracy financial impact on inventory, Joannes Vermorel, February 2012
- (1) Wikipedia. Vapnik–Chervonenkis theory