Search Results msc_undo_details_v




Overview
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Overview

MSC_UNDO_DETAILS_V is an APPS-owned, VALID database view within the MSC (Advanced Supply Chain Planning) product of Oracle E-Business Suite, applicable to releases 12.1.1 and 12.2.2. The view exposes the individual field-level changes applied to the output of a supply chain plan, presented as a flat, readable set of rows. In Oracle Advanced Supply Chain Planning, planners frequently modify plan results — rescheduling orders, firming planned orders, changing supplier assignments, or adjusting resource and material constraint flags. Each such modification is captured as an undo record so that the change can be reviewed or reversed. MSC_UNDO_DETAILS_V surfaces those captured changes, translating raw stored identifiers into business-meaningful values.

Rather than exposing only internal IDs, the view performs extensive decoding. Date values are converted to displayable character strings via the FND_DATE package, and numerous foreign-key-style columns — organization, supplier, project, task, department, resource — are resolved to descriptive codes or names through the MSC_GET_NAME package. Lookup-driven attributes such as reschedule flag, firm planned type, and constraint indicators are rendered through lookup meanings. As a result, the view is well suited to reporting, diagnostics, and integration scenarios in which the substantive question is not merely whether a plan changed, but what specifically changed and to what value.

Underlying Base Objects

The documented dependencies for MSC_UNDO_DETAILS_V are:

  • MSC_UNDO_DETAILS (SYNONYM) — the primary source of the detail rows, supplying the UNDO_ID, COLUMN_CHANGED, COLUMN_TYPE, OLD_VALUE, and NEW_VALUE columns.
  • MSC_UNDO_SUMMARY (SYNONYM) — referenced to supply summary-level context, including plan and instance identifiers used during decoding.
  • FND_DATE (PACKAGE) — used to convert canonical date values into displayable character format.
  • MSC_GET_NAME (PACKAGE) — used to resolve identifiers (organization, supplier, supplier site, project, task, department, resource) and lookup codes into readable text.

The view therefore sits over the undo tracking tables maintained by Advanced Supply Chain Planning, layering descriptive decoding on top of their raw content. It is a presentation-oriented view rather than a base transaction object.

Key Columns

  • UNDO_ID — the identifier linking each detail row back to its parent undo summary record; the join key that groups changes belonging to a single plan modification.
  • COLUMN_CHANGED — the name of the column whose value was modified in the plan output.
  • COLUMN_CHANGED_TEXT — a descriptive, human-readable label for the changed column.
  • OLD_VALUE / NEW_VALUE — the values before and after the change, decoded according to column type. Dates are formatted; organization, supplier, supplier site, project, task, line, and resource identifiers are resolved to names or codes; and flag columns (RESCHEDULE_FLAG, FIRM_PLANNED_TYPE, OPTIMIZE_FLAG, the daily/weekly/period resource and material constraint flags, CURR_PLAN_CAPACITY_FLAG, FIRM_FLAG) are converted to lookup meanings.

Common Use Cases and Queries

Typical scenarios include auditing who changed a plan and how, explaining differences between consecutive plan runs, and troubleshooting why a planned order was firmed or rescheduled. To list all changes captured, simply query the view directly:

  • SELECT undo_id, column_changed, column_changed_text, old_value, new_value FROM apps.msc_undo_details_v;
  • Filter to a specific change set by joining on undo_id, e.g. WHERE undo_id = :p_undo_id, to review every field touched by one modification.
  • Focus on a particular attribute, e.g. WHERE column_changed = 'RESCHEDULE_FLAG', to isolate rescheduling activity.
  • Compare before-and-after values side by side using OLD_VALUE and NEW_VALUE for reporting on plan evolution.

Because the view returns already-decoded values, it is generally preferred over querying the underlying undo tables when the objective is reporting rather than raw data extraction.