> For the complete documentation index, see [llms.txt](https://docs.fabricplan.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fabricplan.com/planning-sheets/how-tos/measure-and-row-based-planning/row-based-modeling/create-forecasts-using-row-model.md).

# Create forecasts using row model

Use forecasting with a row model to project future values for row-level drivers and evaluate their impact on related rows and KPIs.

For example, you can forecast *revenue*, *expenses*, and other key drivers. Modify the forecasted drivers to explore different assumptions and analyze their cascading impact across the model.

This involves:

* Creating a row model to define relationships between drivers and calculated rows.
* Use **Forecast** to create forecast values and see how changes flow through the model.

### Use cases

You can use forecasts with a row model to

* **Project future performance:** Forecast business drivers and outcomes for upcoming periods.
* **Build driver-based forecasts:** Use historical or current values as a starting point and adjust individual drivers based on business assumptions to see the impact on KPIs.
* **Perform rolling forecasts:** Update forecast periods as new actuals become available and reforecast future periods.

### Prerequisites

Before you create a forecast:

* Make sure the planning sheet uses a standard date hierarchy, such as `Year > Quarter > Month`.
* Make sure the row model is configured with the required drivers and calculated rows. To learn how to create a row model, see [create a row model](/planning-sheets/how-tos/measure-and-row-based-planning/row-based-modeling/create-a-row-model.md).

### Create a forecast

Consider the following **P\&L (Profit and Loss) row model** to forecast *2026 values*.

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FRYiXkykJTdcwONvT089N%2Fimage.png?alt=media&amp;token=434be2ca-3201-4a4a-a13a-3645dfe01474" alt=""><figcaption></figcaption></figure>

1. Go to **Model** > **Forecast**.

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FHboNULQCri3BM3SJqfZY%2Fimage.png?alt=media&amp;token=f5cc6b32-df42-4ea4-a491-8f3e9d33828b" alt=""><figcaption></figcaption></figure>

2. In **Basics**, enter the forecast measure name and set the date range for the forecast period as **Jan 2026 - Dec 2026**. Select **Next**.

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FQaCXxRHLq6f9xHZAAIvM%2Fimage.png?alt=media&amp;token=7a824b8a-ec70-479b-8a15-d4a892c82f69" alt=""><figcaption></figcaption></figure>

3. Configure the **Closed Period** settings to populate values for locked periods.
   * **Configure as:** Select **Link to Measure** to populate the forecast values from an existing measure.
   * **Source Measure:** Select **Actuals** to use the actual values for the closed periods.
4. Select **Next**.

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FjjPe42zASHX4MbczAsBB%2Fimage.png?alt=media&amp;token=46d2a331-ac00-40b1-b233-75f00c54b4eb" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}

### Note

Closed-period forecast values are locked historical periods and users can't edit these values.
{% endhint %}

5. Configure the **Open Period** settings to pre-fill the 2026 forecast values using the 2025 actuals.

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FK89AhiyWn8t1c4eBmkVa%2Fimage.png?alt=media&amp;token=ad6d819f-2d14-4505-99cb-e441add214e6" alt=""><figcaption></figcaption></figure>

* **Configure as:** Select **Data Input** to allow users to enter and modify forecast values.
* **Default Value:** Select **Measure** to use an existing measure as the default value and initialize the forecast values.
* **Measure:** Select **Actuals** measure for default values.
* Under **Pre-fill Open Periods**, configure the range to copy the 2025 actuals to the 2026 forecast as follows:
  * **Copy from:** Select **Actuals**.
  * **Target Range:** Select **Jan 2026 – Dec 2026** as the target.
  * **Operation:** Select **Period Range** to map each period in the source range to the corresponding period in the target range.
  * **Source Range:** Select **Jan 2025 – Dec 2025**.

6. Select **Save**.

The forecast measure is created for the row model. Review the generated forecast values and modify the open-period values as needed.

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FR0F0IrxMKwyPPQM9uGhi%2Fimage.png?alt=media&amp;token=376c4744-20c2-4b21-8770-f4915999e892" alt=""><figcaption></figcaption></figure>

#### Analyze the forecast in a row model

After creating the forecast, use the row model to analyze how changes to forecast drivers affect related rows. For example, changing *Revenue* or *Purchase* expense can flow through the defined row relationships and affect *Profit* and other KPIs.

You can enter values at any period level, such as the **Year** total or individual **Quarter** cells.

The following image shows how a change in *revenue* for *Q1* affects the quarter-level *profit* and flows through to the overall *profit.*

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FKysSFLGQ9ccR1GuPUtAR%2Fimage.png?alt=media&amp;token=4d901008-fac2-4f8b-a84e-c87e12f6aa22" alt=""><figcaption></figcaption></figure>

The following image demonstrates how reducing purchase expense increases profit.

<figure><img src="https://257222532-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUtolck8kt8atqxFPsEBn%2Fuploads%2FA9q8ch50OrlyMMeto7Wc%2Fimage.png?alt=media&amp;token=8b2dd924-98b7-4f05-b0a3-31f21addfce8" alt=""><figcaption></figcaption></figure>

This approach lets you combine forecasting with driver-based row modeling to evaluate future performance and understand the cascading impact of different assumptions.

{% hint style="info" %}

### Note

To learn more about all forecasting options, see [Create a PnL forecast](/planning-sheets/how-tos/forecast-data-to-predict-future-trends/create-a-p-and-l-forecast.md).
{% endhint %}

### Next steps

After creating a forecast, you can take your analysis further by creating **scenarios** to simulate different assumptions without changing the base plan.

Create scenarios, adjust key drivers, and compare outcomes to identify the scenarios that best support your planning decisions. To learn more, see [Perform scenario analysis in the row model](/planning-sheets/how-tos/measure-and-row-based-planning/row-based-modeling/perform-scenario-analysis-in-row-model.md).


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