Rate of Sale (ROS) calculations are a key component of demand forecasting in Assortment Planning (AP) as well as in Allocations (AL). In AP, ROS represents the average sales per week per store within a cluster, calculated and maintained at the cluster level. Since AL uses location lists in lieu of clusters, ROS calculations are shown at the total location list level in the allocations module.

Toolio automates ROS calculations via the ROS Model while allowing you to override these values as needed. This article covers:

The Rate of Sale Model calculates the rate of Gross Sales Units per week per store, which is then used in forecasting. You can define the timeframe and filters (choices) that the system uses for the calculation.

The ROS Model lets you define the following parameters.

The reference timeframe for the calculation. For example, for seasonal products (e.g., SS26), you might want to refer to SS25 to get the ROS calculation.

Which products should be included in the ROS calculation.

You can select Self to ensure that each existing choice uses its own historical data for the ROS calculation. If you choose Self, you can simply drag down the ROS model, ensuring that each selection references its own history.

Which metric type will be used in the calculations. Toolio recommends using Gross Sales Units.

Whether markdown sales will be included in the calculation. If you are planning full-price sales, we recommend excluding markdown sales.

Markdown sales are defined as any week where Markdown $ are greater than $0.

Here you can see a model that describes how the ROS is calculated.

You can preview exactly which products are included in a Rate of Sale calculation, right from the ROS model dialog — no guesswork needed.

This helps you understand the underlying data by seeing the actual products driving a Rate of Sale value before committing to plans, catch filter mistakes early through product verification, and move seamlessly from ROS review to detailed reporting without re-entering filters.

There are two distinct Choice attributes that correspond to ROS.

Type: System-generated

This value is calculated at the offering level per cluster in AP, and at the total location level in AL. It represents the average sales per location, per week, based on historical data from one or more choices. While a single average number is displayed in the Calculated ROS attribute, there may be a range of performances across the products and locations used in the ROS Model.

You can click into the Calculated ROS cell to view the calculated ROS distribution and the rankings at the cluster level in AP, and at the total location list level in AL.

Type: User input

You can adjust the Calculated ROS as needed. In AP, when a Calculated ROS exists, the system uses the weighted distribution among the clusters and adjusts the ROS according to your input. If no ROS Model was defined, each offering gets the same value for the defined Adjusted ROS.

You can read more about the full Choice Planning workflow and details around how the ROS values are utilized in conjunction with Sales Curves, Location Curves, and Cluster Groups in the full Choice Planning guide.

In AL, the workflow is very similar. You can select Demand Type = ROS, and manually input a value to the Demand field to set an average productivity for all locations eligible in the Location List definitions.

Toolio automatically filters out "bad weeks" (weeks with insufficient inventory) during ROS calculations. This ensures only "good weeks" contribute to the calculation.

Good Week Definition: Any week where inventory was available to sell (BOP Units + Receipt Units + Transfer Units > 0).

This evaluation takes place at the Location x Week x SKU granularity during ROS calculation.

For example, say your ROS calculation is configured to include 10 choices and utilizes a 52-week historical timeframe. The system evaluates and excludes any bad weeks at the Location x Week x Choice level. If for a given choice and location combination, 5 weeks in the 52-week timeframe were deemed bad weeks, those weeks are excluded entirely — no sales are considered from those weeks in the numerator, and the total good weeks for that period is reduced to 47 in the denominator.

The ROS calculation for that choice x location would be:

When good weeks are not enabled, the calculation would instead be:

This behavior is controlled by the feature flag isGoodWeeksEnabledInRos. If you would like this turned on, please reach out to your Customer Success Manager.

This behavior is universally configured — meaning that good weeks are always considered when enabled, or never considered when disabled.

If a location is a "pass-through" location (i.e., it doesn't hold inventory but has sales), by default every week can appear as a "bad week" since inventory will always be 0. Examples include a wholesale virtual location or ecommerce (where the warehouse is somewhere else).

To handle this case, you can set a Source Location (see Location Management) for these locations and set the Lead Time to 0. This tells the system that these selling locations use inventory from the source location. With this setup, Toolio considers any week where the source location has inventory a good week.

The ROS Configuration section under Settings > Configuration > Intelligence allows you to define the Good Week Size Threshold and Good Weeks Minimum Inventory — two key settings used in Toolio's intelligence module to determine when a product is considered in stock.

Size Threshold defines what percentage of available sizes qualifies a week as "good" for sales performance analysis.

Minimum Inventory defines the minimum amount of inventory (by size & location) that should be in stock for the week to qualify as a good week. The input value must be a positive integer; the default is 0.

To provide insights into the range of ROS calculations, Toolio presents the ROS distribution across six rankings by cluster/location list:

You can view this distribution by double-clicking the Calculated ROS cell. A pop-out displays the rankings and weighted ROS, which aligns with the Calculated ROS shown on the line-building screen. By default, Average is selected to match previous calculation methods.

The pop-out view presents the ROS across the rankings by cluster/location list as well as a weighted ROS which aligns with the Calculated ROS number on the line-building screen. Average is selected by default to align with the previous generation of ROS calculation.

You can leverage the ROS range to rank products or maintain the default average ROS. Ranking allows you to assign a relative performance expectation to any new choice.

To rank products:

When you select a rank, Toolio maps it to a specific statistical value from the pre-calculated ROS distribution:

For example, selecting High tells the system to use the 75th percentile value from the ROS distribution — meaning the choice is expected to perform better than 75% of the historical results.

This means that when you select High for a choice, the system picks the 75th percentile value from the pre-calculated percentile distribution of ROS values across all matching choices and locations for that offering.

For new store openings, you can assign a Like for Like store along with a growth factor. Toolio then uses the performance information from that LFL store to estimate its anticipated rate of sale. This helps provide a realistic forecast for the new location, even before it has its own sales history.

Select multiple choices, right-click, and run Refresh Locations & ROS to bulk refresh the ROS calculations.

After Refresh Locations & ROS finishes running, the new ROS values don't appear automatically in the grid. You need to pull in the updated data.