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Forecasting in Merchandise Plan
Forecasting in Merchandise Plan

How to use the forecast function to generate gross sales plans.

Updated this week


The Forecasting function in Toolio's Merchandise Plan module streamlines the sales projection process at your desired topline level. A typical OTB process starts with closing the month actuals and reviewing the variances not only for sales but all metrics from gross margin to receipts. The next step is to project out the future months based on your trends. Then you update your purchase order dates to line up with receipts and now find any gaps with what your trend was showing you and your receipt flow. Usually leading up to a meeting to discuss the changes, chase, product mix and the next buying cycle (I just gave myself a headache). Now the manual work of updating your future months sales trend can be accomplished by using the Forecast function.


Navigate to Forecast Menu

Navigate to your Toolio - In-Season Planning view in the Merchandise Plan module. This view will be preconfigured out of the box in your Item Planning module and is designed to help visualize your forecasted sales and inventory values.

Using the filter, group, and sort functions, you can easily configure the view to display the categories you are interested in forecasting. You will be able to forecast what is in the view.

Right-click on any row you wish to forecast and this will bring up the context menu. Select the Forecast function.

The Forecast Menu

Once the menu is open you will need to fill in some fields.

Choose from 2 different Seasonality Methods.

  • Sales History

  • Merchandise Plan

Seasonality Method - Sales History

At the top of the menu you will see the number of categories selected, the time frame selected to forecast and the scenario to forecast to.

Seasonality - With Sales History you will need to select the Plan Year and Rolling Average to define your seasonality curve.

Seasonality Groups - Choose the attributes you want to use to shape your seasonality. The higher the level and/or the less attributes selected the more general it will be. The deeper the level and/or the more attributes selected the more specific it will be. In the example above I am running the curve based on Division, Department and Class for my clothing department. this will insure that it will build clothing for each division and class that is associated with my clothing department.

Volume - This is the timeframe you select that you want to use as your current trend. In this case I am using my last 4 weeks to generate a forecast.

Advanced - Returns can be forecasted using the Independent method, where it forecasts the returns metrics based on their own seasonality, or the Return Rate method which uses the historic return rates determined by the seasonality Plan Year you selected in the seasonality section.

Forecasting Algorithm - Sales History Method

This approach looks at the penetration of the categories you are forecasting to the Seasonality Groups you selected and how they performed in the historical time frame you selected. This algorithm is applied to each Gross Sales metric and to each Returns metric (when independent method is selected).

For Example:

Forecasting Algorithm - Sales History with Rolling Weeks Method

This approach looks at the penetration of the categories you are forecasting to the Seasonality Groups you selected and how they performed in the historical time frame you selected. Historical performance is assessed by looking at a Rolling Average for the time frame, using the trailing X weeks for each weekly forecast in order to smooth out any spikes or dips that may have occurred.

For Example:

Seasonality Method - Merchandise Plan

Use this method when you want to retrend your current forecasts based on recent history.

Seasonality - With Merchandise Plan selected you will need to choose Plan Year (Same as Target), Scenario Type: Scenario OR History and Scenario Name (a scenario you saved). This allows you use any scenario to model your seasonality, price & cost inputs, discount %, markdown % and return rates. Our inputs are usually knowns but our actuals and penetrations can shift. This method will help us couple the "knowns" with the latest sales performance to give you a new forecast.

Forecasting Algorithm - Merchandise Plan Method


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