Groovy scripting in Oracle Cloud EPM Planning is not just a scripting tool—it's a superpower. With the ability to manipulate forms, validate data, execute calculations, and orchestrate workflows dynamically, Groovy turns your Planning environment into a living, breathing application.
Whether you’re validating input, enforcing business rules, or orchestrating complex data flows, Groovy scripting provides flexibility and control that traditional Planning business rules can only dream of.
In the world of Cloud EPM Planning, where forms, calculations, and allocations rule the day—and multiple times, you need Groovy scripting to do what regular calc scripts simply can’t. This blog gives you the easy to use functions and codes that you can reuse work across projects.
Sometimes you just need to let someone know what’s going on in your EPM application—maybe a process finished, a check failed, or you just want to send a heads-up. This Groovy business rule makes it easy by sending emails right from EPM, using just a few runtime prompts.
Have
you ever wanted to run a series of Data Management integrations in
Oracle Cloud EPM automatically for specific forecast periods — all while
staying user-friendly with runtime prompts and full-on status
tracking?
Well,
this Groovy business rule does exactly that. Let’s break it down
This
Groovy rule is designed to automate and control the execution of Data
Management (DM) integration jobs for forecast data. It dynamically
interprets user input, validates required conditions, and executes the
appropriate integration job for each forecast period in sequence.
The rule orchestrates the execution of multiple forecast data movement jobs in Oracle Cloud EPM. The jobs transfer forecast data from one scenario/version to another (like from mid-forecast to working version) using a Data Management (DM) integration job. The timing and execution logic is governed by runtime prompt inputs and substitution variables.
In
this blog, we will see how to perform an intercompany data translation with ease,
This rule ensures intercompany data is clean, aligned, and instantly processed when users save a form. It applies standard naming and business logic automatically—no manual cleanup needed. Data flows straight into the target cube or POV, reducing cycle time and reconciliation effort. It improves auditability and consistency across entities, accounts, and products. The result? Faster closes, fewer errors, and more trust in your numbers
For example, consider running a forecast pipeline in Oracle EPM, we often deal with multiple forecast periods that need to be processed sequentially.
However,
ensuring that each period runs correctly, waiting for previous jobs
to complete, and handling errors gracefully can be tricky.
In
this the script ensures that each forecast period is processed properly,
one at a time, and prevents overlapping or conflicting jobs.
If
you’ve been working with Oracle EPM Cloud, particularly Profitability
and Cost Management (PCM), you might need to copy data between different
POVs (Point of View) in your application.
In Oracle's Enterprise Profitability and Cost Management (EPCM), efficiently managing data across different Points of View (POVs) is crucial for accurate financial analysis and reporting. A POV typically represents a specific combination of dimensions such as year, period, scenario, and version. Copying data from one POV to another allows organizations to replicate datasets across different scenarios or time periods, facilitating comparative analysis and forecasting. Oracle provides a REST API endpoint specifically designed for this purpose: the "Copy Data by Point of View" operation.
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In
Oracle EPM, pipelines are essential for automating data workflows, integrating
data sources, and performing transformations. To ensure smooth execution, it's
crucial to monitor pipeline runs and detect job failures in real-time.
When
running automated data processing or calculations in Oracle EPM Cloud,
monitoring the execution status of pipelines is critical. You don’t want
to just trigger a pipeline and hope for the best—you need real-time insights
into whether jobs succeed or fail.
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Groovy
scripting,
which enables automation, dynamic validation, and metadata updates.
In
this blog, we’ll walk through two Groovy business rules that manage version
members in the Version dimension:
We’ll explain the logic behind each rule,
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).
Lets explore on how to extract cell notes and attachments from Oracle Cloud EPM using Groovy scripting.
This blog walks through retrieving Entity and Cost Center descendants, querying financial data, and identifying cells with notes or file attachments.
The script dynamically iterates through each combination, loads a data grid, and logs key findings after finding the cell Notes and Attachment if any.
Suppression rules ensure efficient execution, while error handling prevents failures. Ideal for auditing financial data and ensuring compliance.