Which forecasting time frame best identifies seasonal effects?
Answers
Answered by
0
You can generate both detail (single item) forecasts and summary (product line) forecasts that reflect product demand patterns. The system analyzes past sales to calculate forecasts by using 12 forecasting methods. The forecasts include detail information at the item level and higher level information about a branch or the company as a whole.
3.1 Forecast Performance Evaluation Criteria
Depending on the selection of processing options and on trends and patterns in the sales data, some forecasting methods perform better than others for a given historical data set. A forecasting method that is appropriate for one product might not be appropriate for another product. You might find that a forecasting method that provides good results at one stage of a product life cycle remains appropriate throughout the entire life cycle.
You can select between two methods to evaluate the current performance of the forecasting methods:
Percent of accuracy (POA).
Mean absolute deviation (MAD).
Both of these performance evaluation methods require historical sales data for a period that you specify. This period is called a holdout period or period of best fit. The data in this period is used as the basis for recommending which forecasting method to use in making the next forecast projection. This recommendation is specific to each product and can change from one forecast generation to the next.
3.1.1 Best Fit
The system recommends the best fit forecast by applying the selected forecasting methods to past sales order history and comparing the forecast simulation to the actual history. When you generate a best fit forecast, the system compares actual sales order histories to forecasts for a specific time period and computes how accurately each different forecasting method predicted sales. Then the system recommends the most accurate forecast as the best fit. This graphic illustrates best fit forecasts:
Figure 3-1 Best fit forecast
3.1 Forecast Performance Evaluation Criteria
Depending on the selection of processing options and on trends and patterns in the sales data, some forecasting methods perform better than others for a given historical data set. A forecasting method that is appropriate for one product might not be appropriate for another product. You might find that a forecasting method that provides good results at one stage of a product life cycle remains appropriate throughout the entire life cycle.
You can select between two methods to evaluate the current performance of the forecasting methods:
Percent of accuracy (POA).
Mean absolute deviation (MAD).
Both of these performance evaluation methods require historical sales data for a period that you specify. This period is called a holdout period or period of best fit. The data in this period is used as the basis for recommending which forecasting method to use in making the next forecast projection. This recommendation is specific to each product and can change from one forecast generation to the next.
3.1.1 Best Fit
The system recommends the best fit forecast by applying the selected forecasting methods to past sales order history and comparing the forecast simulation to the actual history. When you generate a best fit forecast, the system compares actual sales order histories to forecasts for a specific time period and computes how accurately each different forecasting method predicted sales. Then the system recommends the most accurate forecast as the best fit. This graphic illustrates best fit forecasts:
Figure 3-1 Best fit forecast
Similar questions