Supply chain planning is continuously evolving. Demand changes, supply constraints emerge, planners make adjustments, and plans are rerun. But one important question remains:
How do you understand what changed between one planning cycle and the next?
With Archive and Versioning capabilities for Supply Chain Planning data in Oracle Fusion Data Intelligence (FDI), customers can now build a historical view of selected Planning data using a configurable Data Augmentation / low-code application approach.
Instead of analyzing only the latest state of a plan, organizations can retain prior snapshots and use them for historical comparison, trend analysis, auditability, and deeper planning intelligence.
Why is this important?
Planning decisions are rarely made from a single point-in-time view. Planners and business leaders need to understand how their plans evolve.
With historical versions and archives, customers can answer questions such as:
- How did my supply or demand plan change from the previous planning cycle?
- Which supply quantities, dates, or recommendations changed between plan runs?
- How has the forecast evolved over time?
- What did the plan look like before a planner adjustment or business decision was made?
- Are shortages, exceptions, or supply risks improving or getting worse across planning cycles?
- What historical planning data can I use for trend analysis, audit, or AI/ML use cases?
This turns Planning Analytics from a view of “what does my plan look like now?” into a much richer view of “how did my plan get here, and how is it changing?”
Two complementary ways to retain planning history
Versions — understand how the plan evolves
Versioning is designed for frequent plan-to-plan comparison.
A new version can be captured as the Planning Analytics application is refreshed, allowing customers to retain a configurable number of historical versions.
Each version can carry information such as:
Version Name | Version Number | Version Date
This makes it possible to compare measures across versions—for example, current versus prior quantities—and identify how planning results have changed between successive refreshes.
Archives — preserve important points in time
Archiving provides a longer-term snapshot of Planning data at predefined intervals.
Customers can configure an archive pattern such as weekly or monthly, enabling them to retain important planning snapshots rather than every intermediate state.
Each archive can be identified through attributes such as:
Archive Name | Archive Number | Archive Date
This is particularly valuable for period-over-period analysis, governance, historical reporting, and retaining important planning milestones.
Flexible by design
Supply Chain Planning can contain a very large number of tables and significant data volumes. Rather than forcing customers to archive or version everything, the solution uses a configurable DAS/low-code pattern.
A sample implementation demonstrates the pattern for a Planning table, and customers can extend the approach to the specific Planning datasets that matter to their business.
This gives customers the flexibility to:
- Select the Planning data that requires historical retention.
- Configure the number of versions to retain.
- Configure archive frequency and retention.
- Publish the versioned or archived data into the required analytical Subject Areas.
- Reuse existing Planning dimensions while versioning or archiving the required fact data.
- Build analyses comparing metrics across different versions or archives.
The result is a focused approach where historical data is retained where it delivers business value, without requiring every Planning dataset to be duplicated.
From operational planning to historical planning intelligence
The value goes beyond simply storing old data.
Historical Planning data creates the foundation for several strategic use cases:
Plan-to-plan comparison
Quickly identify where quantities, dates, forecasts, supplies, or other planning measures changed.
Planning stability analysis
Understand whether a plan is converging over successive runs or continuing to experience significant change.
Decision traceability
Analyze the planning state at different points in time and provide greater context around planning decisions.
Performance improvement
Compare previous plans with subsequent outcomes to identify patterns and opportunities to improve planning quality.
Advanced analytics and AI
Create historical datasets that can support future predictive analysis, forecast-error analysis, recommendation intelligence, and other AI/ML scenarios.
The business value
Planning history becomes analytical data.
By bringing Archives and Versions into FDI, customers can move beyond static current-state reporting and gain visibility into the evolution of their Supply Chain Plans.
This enables better comparison, greater traceability, stronger planning governance, and a richer historical foundation for advanced analytics.
For organizations making hundreds or thousands of planning decisions across items, organizations, suppliers, customers, and time periods, understanding what changed, when it changed, and how significantly it changed can be just as valuable as understanding the current plan itself.
Getting started
The current approach is provided as a configurable DAS / low-code implementation pattern for Supply Chain Planning. The SCPA Custom Application must first be successfully published, after which the sample pattern can be adapted for the Planning tables and analytical measures required by the customer.
Resources to Get Started
Attached are the detailed document and sample DAS files that demonstrate the Archive and Versioning implementation pattern and can be used as a starting point for your own Planning Analytics use cases.
The sample files can be adapted based on the Planning tables, measures, retention requirements, and Subject Areas that are relevant to your implementation.
If you have questions, need help understanding the approach, or would like to discuss a specific customer use case, please feel free to contact me directly at priti.nagpure@oracle.com
