A Groovy Script for Dynamic Substitution Variables - Automating Rolling Forecast Periods in Oracle EPM

This blog explores a powerful business rule that dynamically updates forecast substitution variables based on the current fiscal period. Learn how to map months to quarters, process rolling forecasts, and seamlessly update variables for accurate planning. 🏆 Say goodbye to manual adjustments and let Groovy handle the quarterly forecasting logic with ease!

Why Automate Forecast Substitution Variables?

Substitution variables act as dynamic placeholders in Oracle EPM, allowing calculations, rules, and integrations to reference time-sensitive values (e.g., current month, current year, forecast period).

 

Challenges with Manual Updates:

• Time-Consuming – Requires manual intervention each cycle.
• Error-Prone – Risk of setting incorrect period values.
• Not Scalable – Inefficient for organizations with multiple forecast cycles.

 

✅ Benefits of Automation:

✔ Eliminates manual updates for forecast periods.
✔ Ensures accuracy and consistency in planning.
✔ Dynamically adjusts for multiple fiscal years.
✔ Reduces the risk of data misalignment.

How Does Groovy Script automates the Forecast Substitution Variable Assignment? 

1. Fetch the Current Forecast Start Month and Year
• Get substitution variables (Fcst_First_Mth, Fcst_First_FY).
2. Define Quarter Mappings
• Map fiscal months (Sep, Dec, Mar, Jun) to respective quarters (Q1, Q2, Q3, Q4).
3. Define Case Rules for Quarter Processing
• Based on the start month, determine how many quarters to process in the current, next, and following fiscal years.
4. Generate the Forecast Periods List
• Compute the quarter-year combinations dynamically.
5. Update Substitution Variables
• Assign calculated periods to Fcst_Qtr1 through Fcst_Qtr8.
6. Ensure Data Integrity
• Fill any missing values with "-" to maintain structure.

 

Breaking Down the Script Logic

Here’s a step-by-step breakdown of what happens in the script:

 Fetching the Initial Substitution Variables

The script starts by retrieving the first forecast period and year from substitution variables:

 

These values determine which quarters should be processed for forecasting.

Defining Quarter Processing Logic

Different months require different forecast coverage. The caseDetails map determines how many quarters should be forecasted in the current year, next year, and beyond.

 

This ensures that forecasting dynamically adjusts based on the business's quarterly cycle.

Mapping Quarters to Fiscal Periods

The script uses a quarter mapping to correctly associate months with quarter names:

Processing Forecast Quarters

Using looping logic, the script assigns the correct forecast quarters and years based on the business rules:

This ensures that forecast quarters are automatically calculated without manual intervention.

Updating Substitution Variables

Once forecast quarters are calculated, the script updates substitution variables dynamically:

This final step ensures all substitution variables are dynamically updated, keeping forecast periods aligned with the correct fiscal years.

Key Benefits of This Approach

✅ Fully Automated – No manual updates required for forecast periods.
✅ Adaptable to Any Fiscal Year – Works across any fiscal cycle.
✅ Error-Free Updates – Eliminates human errors in forecast period selection.
✅ Time-Saving – Reduces administrative overhead for EPM admins and finance teams.

By leveraging Groovy scripting, this approach automates substitution variable updates, ensuring your forecast cycles remain accurate and efficient.

The Complete script is here 

Hope this was useful!