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The set of all factors that the system takes into account is set by the user and can be individually adapted. The default set of factors is described in the "Default Predictors" section.
Daily forecast adjustment
After completing all forecast calculation stages, the system adjusts it for the upcoming weeks (usually two, which is a configurable parameter) based on the most recent days of sales data. To do this, the system loads sales data at the SKU-store-day level for the last 28 days (configurable parameter) and selects the most relevant 7 (or fewer, also configurable) days of sales:
Separating promotional and non-promotional sales. Past promotional sales adjust only the promotional forecast for the next one to two weeks, while regular sales adjust the regular forecast accordingly.
Filtering out days with insufficient stock. The stock level must be greater than zero and also exceed 30% of the average daily sales (configurable parameter). In some cases, a minimum absolute stock level can be set as a parameter.
Handling cases of insufficient stock. If sales occurred despite low stock, the system may still consider them under certain conditions. For example, if a given day’s sales exceeded 75% of the forecast for that day, the system applies an upward adjustment factor. The assumption is that if stock had been sufficient, sales would have been higher:
By 50%, if the product likely ran out in the second half of the day.
By 25%, if the product likely ran out at the end of the day.
Filtering out days with significant price variations. Days where the price deviation exceeded 15% (configurable parameter) are excluded.
Calculating lost sales
For days when an item was part of the assortment matrix in the last 1–2 weeks (configurable parameter), the system calculates lost sales or missed demand for days when there was no stock or an insufficient stock level.
Similar to the Daily Forecast Adjustment calculation, insufficient stock means that either:
The stock level was zero at the end of the day, or
The stock was less than 30% (configurable parameter) of the forecasted sales for that day.
When distributing the weekly forecast into daily values, the system also applies day-of-week coefficients and considers price elasticity if the price changes during the week.
If the system finds valid sales data from the last 28 days (configurable parameter) that meet the required criteria (i.e., sufficient stock was available, promotional and regular sales are considered separately, and price fluctuations were within acceptable limits), it calculates the average daily sales. This calculation adjusts the identified 7 or fewer (configurable parameter) sales days using the same factors applied in the daily forecast adjustment:
Day-of-week coefficients
Price elasticity and discounts
Seasonality, weather, and other influencing factors
During the adjustment of average daily sales, the system takes into account the seasonality and weather conditions of the week in which the stock shortage occurred, as well as the day-of-week coefficient, price, and discount on the specific date for which the calculation is performed.
As a result, the system generates an adjusted forecast for the specific date.
Lost sales are calculated using the formula:
Lost Sales = max(Forecast} - Actual Sales, 0)
This applies only if stock was insufficient according to the criteria described above.
Lost sales calculated during the forecast process are stored in the FCST_SKU_LOST table, linked to the forecast ID for the last 2 weeks (configurable parameter). Based on this table, the SALES_SKU_LOST table is updated, maintaining a long-term history of lost sales at the SKU-store-day level. Additionally, data may also be aggregated and stored in the SALES_SKU_LOST_WEEK table, which contains weekly-level lost sales information.
Next, the system adjusts daily sales based on influencing factors, aligning past data with future forecast weeks:
Day-of-week coefficients
Price elasticity and discounts
Seasonality, weather, and other factors
Finally, for each future week—separately for promotional and regular sales—the system calculates adjusted average daily sales. The forecast is then fine-tuned within acceptable limits, which depend on the number of selected days and can be defined via parameters.
Parameters
"Parameters" area is designed to choose and configure the basic parameters of the forecast model. It consists of the following categories:
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SKUs chart – visualizes "Components" element in the form of a chart for all SKUs (the user himself selects which component to display on the chart). Details on all components are described in the Forecast analysis components guide;
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SKUs table – visualize "Components' element values as a table for all SKU (the user himself selects which component to display on the chart);
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Stores chart – visualizes "Components" element in the form of a chart for all stores (the user himself selects which component to display on the chart). Details on all components are described in the Forecast analysis components guide;
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Stores table – visualize "Components' element values as a table for all Stores (the user himself selects which component to display on the chart);
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Components – Allows you to add a new component, parameter, result or characteristic of the calculated forecast model to the results display area. Different components display either their influence on the forecast in sales volume or value, and also display historical values of factors that affect the forecast. The use of components allows a more detailed study of the influence of factors on the forecast, as well as understanding why the forecast has one or another value. Anomaly sales and excluded weeks are also added using components (described in detail in the Anomaly & Excluded section). After being added, the component disappears from the list; The component is returned to the list after it is removed from the display area. For more detailed information about the "Components" elements, use the Справочник компонентов анализа Forecast analysis components guide; section;
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Forecast – designed for visual analysis of the forecast results on the chart. The chart allows you to visually estimate built forecast, as well as using components to understand the forecast behavior (for example, using the stock component, you can see changes in the stocks balance and understand that, for example, sales fell precisely because of shortages);
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Predictors – designed for visualization and assessment of the impact of predictors on the predictive model (those predictors that were included in the model are displayed). Allows you to analyze, because of what parameters the forecast has one or another value. For more information on predictors, see Predictors. This element is very important for understanding the forecast itself. It displays the numerical values of the influence of various factors on the forecast and understand on what exactly the sales depend in different periods. User can turn off the display of any element by clicking on its name under the chart.
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order – represents the sales forecast with safety stock (recommended results for ordering);
seasonality – represents the influence of the seasonality on sales volume (taking into account the coefficient of influence on the forecast);
trend – represents the influence of the trend on sales volume (taking into account the coefficient of influence on the forecast);
region – represents the region sales influence on sales volume (taking into account the coefficient of influence on the forecast);
group – represents the group sales influence on sales volume (taking into account the coefficient of influence on the forecast);
auto error – represents the influence of unknown factors on sales volume (taking into account the coefficient of influence on the forecast). It is based on the avg error component, and the duration of the component effect can be 2, 4, or 6 weeks, depending on which duration gives the best result (min error);
min – represents the influence of the sales conversion on sales volume, in the case of a forecast below the historically limiting volume (taking into account the coefficient of influence on the forecast). The limiting sales volume is the lower quartile of sales by default;
price % – represents the price changing influence on sales volume (taking into account the coefficient of influence on the forecast);
discount – represents the discount influence on sales volume (taking into account the coefficient of influence on the forecast);
discount % – represents the discount (in percent) influence on sales volume (taking into account the coefficient of influence on the forecast);
rate – represents the dollar rate influence on sales volume (taking into account the coefficient of influence on the forecast);
a. price – represents the actual price (price with discount) influence on sales volume (taking into account the coefficient of influence on the forecast);
price – represents the price influence on sales volume (taking into account the coefficient of influence on the forecast);
item count. – represents the influence of the width of the available assortment (the width of the assortment on the stock) on the sales volumes (taking into account the coefficient of influence on the forecast);
group disc – represents the influence of the average discount of all SKUs in the product group that the SKU belongs to (taking into account the coefficient of influence on the forecast);
group price – represents the influence of the average price of all SKUs in the product group that the SKU belongs to (taking into account the coefficient of influence on the forecast);
base – represents the base sales level (taking into account the coefficient of influence on the forecast);
temp. – represents the average air temperature during the week influence on sales volume (taking into account the coefficient of influence on the forecast);
rain – represents the proportion of raining days of the week influence on sales volume (taking into account the coefficient of influence on the forecast);
snow – represents the proportion of snowing days of the week influence on sales volume (taking into account the coefficient of influence on the forecast);
anomaly - displays the volume of anomaly sales, and also allows you to enter the value of anomaly sales in the opened field. Anomalies are described in detail in the section 'Anomaly & Excluded';
исключения - displays excluded weeks, and also allows you to exclude weeks in the field that opens. Excludes are described in detail in the section. : 'Anomaly & Excluded'.
If any components from the list are not displayed, this means that they were not included in the predictive model as predictors.
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sales – historical sales value;
forecast – forecast of sales value;
last year saels – historical sales value, shifted 52 weeks ahead in time;
order – represents the sales forecast with safety stock (recommended results for ordering);
seasonality – represents the influence of the seasonality on sales value (taking into account the coefficient of influence on the forecast);
trend – represents the influence of the trend on sales value (taking into account the coefficient of influence on the forecast);
region – represents the region sales influence on sales value (taking into account the coefficient of influence on the forecast);
group – represents the group sales influence on sales value (taking into account the coefficient of influence on the forecast);
auto error – represents the influence of unknown factors on sales value (taking into account the coefficient of influence on the forecast). It is based on the avg error component, and the duration of the component effect can be 2, 4, or 6 weeks, depending on which duration gives the best result (min error);
min – represents the influence of the sales conversion on sales value, in the case of a forecast below the historically limiting volume (taking into account the coefficient of influence on the forecast). The limiting sales volume is the lower quartile of sales by default;
price % – represents the price changing influence on sales value (taking into account the coefficient of influence on the forecast);
discount – represents the discount influence on sales value (taking into account the coefficient of influence on the forecast);
discount % – represents the discount (in percent) influence on sales value (taking into account the coefficient of influence on the forecast);
rate – represents the dollar rate influence on sales value (taking into account the coefficient of influence on the forecast);
a. price – represents the actual price (price with discount) influence on sales value (taking into account the coefficient of influence on the forecast);
price – represents the price influence on sales value (taking into account the coefficient of influence on the forecast);
item count. – represents the influence of the width of the available assortment (the width of the assortment on the stock) on the sales values (taking into account the coefficient of influence on the forecast);
group disc. – represents the influence of the average discount of all SKUs in the product group that the SKU belongs to (taking into account the coefficient of influence on the forecast);
group price – represents the influence of the average price of all SKUs in the product group that the SKU belongs to (taking into account the coefficient of influence on the forecast);
base – represents the base sales level value (taking into account the coefficient of influence on the forecast);
temp. – represents the average air temperature during the week influence on sales value (taking into account the coefficient of influence on the forecast);
rain – represents the proportion of raining days of the week influence on sales value (taking into account the coefficient of influence on the forecast);
snow – represents the proportion of snowing days of the week influence on sales value (taking into account the coefficient of influence on the forecast);
anomaly - displays the value of anomaly sales, and also allows you to enter the value of anomaly sales in the opened field. Anomalies are described in detail in the section 'Anomaly & Excluded'.
If any components from the list are not displayed, this means that they were not included in the predictive model as predictors.
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To upload a forecast from a file, select the file by pressing the button "Select file". After selecting a file, press the button .
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Descriptions of the fields of file with master forecast for a specific week:
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Example of file with master forecast for a specific week File example.
Descriptions of the fields of file with master forecast for the period:
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Example of file with master forecast for the period File example.
To upload master forecast to a file, press the button .
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An example of csv file can be found at link: "New items load file example".
Use cases
Section is under development.
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Sample import file available at link.
Anomaly & Excluded
Overview & Use
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If the user understands the amount of such sales - he should enter a specific (or as close as possible) number of anomaly sales into the system, then the system will see the sales as actual minus the specified anomaly ones;
If the user does not understand the amount of such sales, it makes sense to completely exclude the week from the forecast (how to do this is described in the section Working with excluded tab).
Accounting for anomaly sales is very important for the correct operation of the system. Since unjustified jumps in sales can cause incorrect dependencies in the system and in the future affect the accuracy of the forecast and order.
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