Forecast Quality Analyses compares Quant's sales forecast with the actual products sold. Based on this comparison, Quant calculates the MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) for each day. The calculated MAE and MAPE can then be used to evaluate historical forecasts.

You can find this analysis in the menu Analyses / Products / Forecast Quality.

In order to see the correct results in the Forecast Quality Analyses, you must have Sales Forecast Snapshots enabled. If you need to save forecast snapshots, please contact Quant Support.

Settings of Criteria

The first thing you need to do is to set the analyses criteria that will be used to display the results.

Products 

In this row, you can select the products that you would like to evaluate.

Date

Click here to select the date the forecast was created.

Stores

In this criterion, select the stores whose forecasts you want to evaluate.

Settings of CriteriaInterpretation of the analyses

The results of the analyses are divided into three tabs:

1. Overview

The first tab gives an overall view of the reporting period. Here you can see an overall comparison of all products for all stores and also a comparison of the sales forecast with the actual units sold by day.

Analyses Overview2. Stores

The second tab shows the quality of the forecasts by store. Here you can see the MAPE and MAE for each store.

Store Overview3. Products

The last tab, Products, gives an overview of the forecast quality by product for all stores.

Products OverviewMAE (Mean Absolute Error) - is the average absolute value of the difference between the forecast and actual products sold. It expresses the size of the estimation error.

MAPE MAPE (Mean Absolute Percentage Error) - is the percentage of the MAE displayed. It shows the percentage difference in absolute value between the forecast and actual sales.