Search Results is_valid_pc_class_code




Overview

APPS.FPA_AW_PC_INV_MATRICES_V is an Oracle E-Business Suite view belonging to the Enterprise Territory Management (ETRM) / Field Planning and Alignment (FPA) product family. It exposes Planning Cycle investment matrix data — the mapping between planning cycles, PC categories, class codes, and investment mix percentages that drive territory alignment and quota planning calculations. Rather than storing its rows in a conventional relational table, the view is a thin wrapper over the Oracle OLAP option: its result set is generated at runtime by a table function call to OLAP_TABLE, which resolves an analytic workspace (AW) query and projects the resulting multidimensional data into a relational rowset.

This design matters for anyone searching on "pc_category". The column PC_CATEGORY is not stored in a base table in the conventional sense — it is a measure sourced from the analytic workspace member PC_CATEGORY_M. Understanding this dependency is essential when troubleshooting missing values, unexpected aggregation, or performance behavior, since the view inherits all the operational characteristics of the OLAP option (workspace attach, analytic workspace availability, and OLAP-specific initialization).

Underlying Base Objects

The ETRM metadata documents two referenced objects:

  • FPA_UTILITIES_PVT (PACKAGE) — supplies the function AW_SPACE_NAME, which returns the name of the analytic workspace that holds the planning-cycle investment data. The view concatenates this returned space name with the literal ' duration query' to form the fully qualified AW query identifier passed to OLAP_TABLE.
  • OLAP_TABLE (SYNONYM) — the standard Oracle OLAP table function that materializes AW data as relational rows. It receives the AW query, the target AW object name (FPA_PC_INV_MATRIX_TBL), an empty limit-map argument, and a dimension/measure specification string.

Within the AW, three measure sources and two dimensions are declared: PLANNING_CYCLE_D, CLASS_CODE_D, PC_CATEGORY_M, PC_CLASS_CODE_TARGET_MIX_M, and PC_CLASS_CODE_M. The dimension and measure aliases correspond one-to-one with the view's output columns.

Key Columns

  • PLANNING_CYCLE — maps to the PLANNING_CYCLE_D dimension; identifies the planning cycle whose investment matrix is being reported.
  • PC_CATEGORY — maps to the PC_CATEGORY_M measure; carries the product category classification used in the planning-cycle investment mix. This is the focus of the "pc_category" search and is the primary categorical attribute exposed by the view.
  • CLASS_CODE — maps to the CLASS_CODE_D dimension; the class code (such as a customer size or segment class) associated with each matrix cell.
  • INVESTMENT_MIX — maps to the PC_CLASS_CODE_TARGET_MIX_M measure; the target investment mix value for the combination of planning cycle, PC category, and class code.
  • IS_VALID_PC_CLASS_CODE — maps to the PC_CLASS_CODE_M measure; a validity indicator for the PC class code combination. The view applies a predicate WHERE IS_VALID_PC_CLASS_CODE = 1, so only valid combinations are returned.

Common Use Cases and Queries

Typical uses include validating that a PC category is correctly represented in a planning cycle's target mix, extracting the investment mix matrix for downstream alignment engines, and diagnosing why a class code has no PC category assigned. A representative query is:

  • SELECT planning_cycle, pc_category, class_code, investment_mix FROM apps.fpa_aw_pc_inv_matrices_v; — returns the full valid matrix.
  • SELECT class_code, investment_mix FROM apps.fpa_aw_pc_inv_matrices_v WHERE pc_category = :p_category AND planning_cycle = :p_cycle; — filters the matrix to a single pc_category within a planning cycle, the common lookup pattern.
  • SELECT DISTINCT pc_category FROM apps.fpa_aw_pc_inv_matrices_v; — enumerates the categories present in the analytic workspace.

Because the view's cost is driven by AW attach and OLAP materialization, queries should filter on PLANNING_CYCLE and PC_CATEGORY where possible rather than scanning the full matrix repeatedly.