Below, you will find a detailed reference of the cubes and fields that make up theItems Ranking andForecast AI, in order to facilitate users' interpretation and use in the continuous improvement of demand planning processes, in terms of accuracy and automation.
Qrank: Item Sorting Cube
Consolidates the classification of each item according to the ABC, FSN and XYZ rankings. The first refers to the item's profitability or revenue; the second to the level of turnover and the third to the item's stability for a forecasting process.
| Column | Column | Description | Parameters |
|---|---|---|---|
|
Item |
Item |
Unique identification of the items or references making up the product portfolio |
|
|
Revenue |
Income |
Sum of income or profitability related to the item |
|
|
RevenuePercent |
Percentage Revenue |
Percentage of income or profitability related to the item |
Values between 0 and 1 (0-100%). The higher the value, the higher the income and profitability of the item. |
|
ABC |
ABC |
ABC classification of the item |
A items represent up to 80% of revenues or returns. Items B and C represent 15% and 5%, respectively. |
|
Frequency |
Frequency |
Percentage of periods (dates) with sales or demand movements of the item |
Values between 0 and 1 (0-100%). The higher the value, the greater the movement of the item. |
|
FSN |
FSN |
FSN classification of the item |
F items have movements at least 75% of the periods analysed. Items S and N have maximum 50% and 25% of dates with activity, respectively. |
|
Stability |
Variation |
Index of variation of historical sales and demand with the stability of the forecast item |
Values from 0. The higher the value, the more difficult the forecast. |
|
XYZ |
XYZ |
XYZ classification of the item |
Items X are of high stability and therefore favours prediction. Items Y and Z are of medium and low stability. |
|
Ranking |
Ranking |
Union of ABC, FSN and XYZ classifications, according to the criteria of income/profitability, turnover and stability. |
The items of highest value to the business are AFX, AFY and AFZ. The items of least value to the business are CNX, CNY and CNZ. |
|
Other Variables |
Other Variables |
Additional attributes of the item. For example description, category, line, brand, subcategory, etc. |
Allow filtering or aggregation by criteria higher in the hierarchy than the item.
|
Qfcst: Forecast Cube
It brings together forecasts and backtests (evaluations of historical data against model predictions) for each item and dates in the forecast period, suggesting the most likely sales or demand quantities for operational, manufacturing, supply or S&OP processes.
| Column | Column | Description | Interpretation |
|---|---|---|---|
|
Date |
Date |
History date and prediction |
|
|
Week |
Week |
Number of the week of the year |
|
|
Item |
Item |
Unique identification of the items or references making up the product portfolio |
|
|
Target |
Actual Demand |
Actual observation or actual historical behaviour of a particular item for a particular date |
|
|
SuggestedForecast |
Suggested Forecast |
Number of units forecast for a particular item and specific date |
This value should be taken as the most likely amount of sales or demand to be observed in the forecast period or date. |
|
SuggestedInterval |
Suggested Interval |
Forecast interval associated with the number of forecast units |
Allows to identify whether the most likely quantity of sales or demand is closer to an under-forecast (Up60, Up80, Up95) or over-forecast (Lo60, Lo80, Lo95). |
|
NextSuggestedForecast |
Suggested Forecast Sup |
Number of over-predicted units for a particular item and specific date |
In S&OP sessions, it allows proposing a higher most likely forecast value, considering that the SuggestedForecast is conservative. |
|
NextSuggestedInterval |
Suggested Interval Sup |
Forecast interval associated with the number of over-predicted units |
Allows to identify whether the most likely quantity of sales or demand, above, is closer to an under-forecast (Up60, Up80, Up95) or over-forecast (Lo60, Lo80, Lo95). |
|
BackSuggestedForecast |
Suggested Forecast Inf |
Number of units under-predicted for a particular item and specific date |
In S&OP sessions, it allows proposing a lower most likely forecast value, considering that the SuggestedForecast is optimistic. |
|
BackSuggestedInterval |
Suggested Interval Inf |
Forecast interval associated with the number of underpredicted units |
Allows to identify whether the most likely quantity of sales or demand, below, is closer to an under-forecast (Up60, Up80, Up95) or over-forecast (Lo60, Lo80, Lo95). |
|
ForecastNaive |
Naive Forecast |
Number of units predicted, according to the naive forecast. I.e. the forecast is equal to the last actual observation. |
Backup forecast so that those items and dates with errors greater than the control value (e.g. 50%) and the comparative error measure MASE is greater than 1 |
|
ForecastPoint |
Forecast Interval Point |
Number of predicted units estimated by the model for the point interval |
Central forecast value generated by the model for a specific item and date. The forecast has a 50% probability of being located at the Forecast Point.
|
|
ForecastLo95 |
Forecast Inf 95 |
Number of predicted units estimated by the model for the interval Lo95 |
Lowest possible forecast value generated by the model for a specific item or date. The forecast has a 95% probability of lying between the Lo95 and the Forecast Point. |
|
ForecastLo80 |
Forecast Inf 80 |
Number of predicted units estimated by the model for the interval Lo80 |
Second lowest possible forecast value generated by the model for a specific item or date. The forecast has an 80% probability of lying between the Lo80 and the Forecast Point. |
|
ForecastLo60 |
Forecast Inf 60 |
Number of predicted units estimated by the model for the interval Lo60 |
Third lowest possible forecast value generated by the model for a specific item or date. The forecast has a 60% probability of falling between the Lo60 and the Forecast Point. |
|
ForecastUp60 |
Forecast Sup 60 |
Number of predicted units estimated by the model for the interval Up60 |
Third highest possible forecast value generated by the model for a specific item or date. The forecast has a 60% probability of falling between the Forecast Point and Up60. |
|
ForecastUp80 |
Forecast Sup 80 |
Number of predicted units estimated by the model for the interval Up80 |
Second highest possible forecast value generated by the model for a specific item or date. The forecast has an 80% probability of lying between the Forecast Point and Up80. |
|
ForecastUp95 |
Forecast Sup 95 |
Number of predicted units estimated by the model for the interval Up95 |
Highest possible forecast value generated by the model for a specific item or date. The forecast has a 95% probability of lying between the Forecast Point and Up95. |
|
WMAPE |
WMAPE |
Measure of forecast error, Weighted Mean Absolute Percentage Error |
It allows to evaluate the prediction performance of the model for each item. Generally, it is evaluated in control bands: 0-30% (excellent), 30-50% (acceptable), +50% (poor). |
|
MASE |
MASE |
Measure of comparative forecast error, Mean Absolute Scaled Error |
Allow to evaluate the prediction performance of the model for each item by comparing the error of the proposed model against the error of the naive forecast (Forecast Naive). Errors less than 1 show that the proposed model performs better. |
|
sMAPE |
sMAPE |
Measure of forecast error, symmetric Mean Absolute Percentage Error |
It allows to evaluate the prediction performance of the model for each item. It is usually evaluated in control bands: 0-30% (excellent), 30-50% (acceptable), +50% (poor). It is recommended to use weighted error measures (e.g. WMAPE). |
|
Ranking |
Ranking |
Union of ABC, FSN and XYZ classifications, according to the criteria of income/profitability, turnover and stability. |
The items of highest value to the business are AFX, AFY and AFZ. The items of least value to the business are CNX, CNY and CNZ. |
|
Other Variables |
Other Variables |
Additional attributes of the item. For example description, category, line, brand, subcategory, etc. |
Allow filtering or aggregation by criteria higher in the hierarchy than the item. |
The forecast intervals are the forecast impact zones, measured as the deviation of the forecast from the actual observation. These zones are delimited by: Forecast Point, Forecast Lo95, Forecast Lo80, Forecast Lo60, Forecast Up60, Forecast Up80 and Forecast Up95.
Qstrgy: Strategic Forecasting Cube
It summarises the most relevant forecast results for each item to guide the continuous improvement processes of demand planning. Firstly, it allows to identify the highest performing items, the items above a control error value (e.g. 50%), the best performing items by a naïve forecast or proposed model, and the forecast interval that best describes a recurrent under- or over-forecast.
| Column | Description | Interpretation |
|---|---|---|
|
Item |
Unique identification of the items or references making up the product portfolio |
|
|
Ranking |
Union of ABC, FSN and XYZ classifications, according to the criteria of income/profitability, turnover and stability. |
The items of highest value to the business are AFX, AFY and AFZ. The items of least value to the business are CNX, CNY and CNZ. |
|
WAPE |
Measure of forecast error, Weighted Mean Absolute Percentage Error |
It allows to evaluate the prediction performance of the model for each item. Generally, it is evaluated in control bands: 0-30% (excellent), 30-50% (acceptable), +50% (poor). |
|
MASE |
Measure of comparative forecast error, Mean Absolute Scaled Error |
Allow to evaluate the prediction performance of the model for each item by comparing the error of the proposed model against the error of the naive forecast (Forecast Naive). Errors less than 1 show that the proposed model performs better. |
|
SuggestedInterval |
Forecast interval associated with the number of forecast units |
Allows to identify whether the most likely quantity of sales or demand is closer to an under-forecast (Up60, Up80, Up95) or over-forecast (Lo60, Lo80, Lo95). |
|
Other Variables |
Additional attributes of the item. For example description, category, line, brand, subcategory, etc. |
Allow filtering or aggregation by criteria higher in the hierarchy than the item. |