Results for “eam_forecast_wo”

AI-generated from documented ETRM metadata — verify critical details on the linked pages.

Overview

EAM.EAM_FORECAST_WO is a table in the Oracle E-Business Suite Enterprise Asset Management (eAM) module that stores operation-level information for forecasted work orders used in maintenance budgeting and forecasting. Unlike discrete work orders that drive actual shop-floor execution, records in this table represent projected maintenance labor and resource demand, allowing planners to model future workload, cost, and capacity without committing to live work orders. The table is owned by the EAM schema and is marked VALID in both 12.1.1 and 12.2.2; the documented 12.2.2 physical schema contains 77 columns.

From a Data Vault modeling perspective, the metadata classifies this object heuristically as standalone, which suggests treating it as a satellite-like entity that carries descriptive and derived forecasting attributes. Because it has no documented child tables referencing it, it is best modeled as a satellite anchored to a work-order or forecast hub rather than as a link table. Note that this classification is a modeling suggestion derived from FK structure, not an enforced constraint.

Key Information Stored

The table blends work-in-process execution columns (shared lineage with WIP operation records) with forecast-specific attributes. The most significant columns include:

• WIP_ENTITY_ID and OPERATION_SEQ_NUM — Together these identify the work order and the specific operation sequence being forecast; they are the natural business-key candidates for row uniqueness.
• ORGANIZATION_ID — The inventory/master organization context, essential for multi-org reporting.
• FORECAST_ID — The forecast scenario to which this operation-level row belongs, the primary linkage to eAM forecasting logic.
• STANDARD_OPERATION_ID and DEPARTMENT_ID — The standard operation and department responsible, driving labor and cost rollups.
• REPETITIVE_SCHEDULE_ID — Links to repetitive scheduling for planned maintenance programs.
• EMPLOYEE_ID — The assigned resource for the forecasted operation.
• SCHEDULED_QUANTITY, QUANTITY_IN_QUEUE, QUANTITY_RUNNING, QUANTITY_COMPLETED — Quantities driving throughput and completion projections.
• ACTUAL_START_DATE, ACTUAL_COMPLETION_DATE, PROJECT_COMPLETION_DATE — Timing fields used to compare forecast against execution.
• RECOMMENDED, PROGRESS_PERCENTAGE, OPERATION_YIELD — Derived indicators supporting prioritization and yield analysis.
• Audit columns: CREATION_DATE, CREATED_BY, LAST_UPDATE_DATE, LAST_UPDATED_BY, plus REQUEST_ID/PROGRAM_ID for concurrent program traceability.
• Descriptive/extension columns: DESCRIPTION, LONG_DESCRIPTION, and ATTRIBUTE1 through ATTRIBUTE15.

Common Use Cases and Queries

Typical uses include maintenance budget forecasting, labor capacity planning, and comparison of projected versus actual maintenance execution. A common query aggregates scheduled quantities by department for a forecast scenario:

SELECT department_id, SUM(scheduled_quantity) forecast_qty
FROM   eam.eam_forecast_wo
WHERE  organization_id = :org_id
AND    forecast_id = :forecast_id
GROUP BY department_id;

Planners also extract operation-level detail to feed external planning tools, joining to standard operations to derive expected durations. Reporting use cases include forecast-versus-actual variance reports (comparing scheduled_quantity against quantity_completed) and yield analysis leveraging operation_yield and lowest_acceptable_yield.

Related Objects

The documented foreign keys define these key relationships:

• WIP_REPETITIVE_SCHEDULES — joined via repetitive_schedule_id for repetitive maintenance programs.
• WSM_OPERATION_DETAILS — joined via standard_operation_id for operation definitions.
• BOM_DEPARTMENTS — joined via department_id for departmental rollups.
• PSB_EMPLOYEES — joined via employee_id for resource assignment.

Additionally, the shared WIP_ENTITY_ID/OPERATION_SEQ_NUM lineage implies functional proximity to WIP operation tables used in eAM work order execution, making them useful for comparative forecasting against actuals.