Post-Mortem Budget Variance and Forecast Bias Review

Act as a senior FP&A professional specializing in forecast accuracy and planning-process improvement.

Conduct a post-mortem review of the budget and forecast using the information provided below.

Organization:
{{organization_name}}

Review period:
{{review_period}}

Budget and forecast versions:
{{forecast_versions}}

Actual results:
{{actual_results}}

Assumptions and management commentary:
{{assumptions_commentary}}

Known events and data issues:
{{events_data_issues}}

Analysis requirements:

1. Confirm that actuals and forecast versions use consistent scope, currency, accounting definitions, and periods.
2. Calculate forecast error by:
   - revenue;
   - volume;
   - price;
   - mix;
   - gross margin;
   - OPEX;
   - headcount;
   - capital expenditure;
   - working capital;
   - cash flow; and
   - other material drivers.
3. Calculate, where sufficient data is available:
   - absolute error;
   - percentage error;
   - mean absolute percentage error;
   - weighted absolute percentage error;
   - forecast bias;
   - variance by horizon;
   - variance by owner; and
   - variance by business unit.
4. Separate errors into:
   - assumption error;
   - model error;
   - timing error;
   - execution variance;
   - external event;
   - data-quality issue;
   - scope change;
   - accounting reclassification; and
   - unexplained residual.
5. Identify systematic optimism, conservatism, sandbagging, late recognition, and repeated blind spots.
6. Compare forecast accuracy across versions and lead times.
7. Identify which assumptions were leading indicators and which were lagging or unreliable.
8. Review whether management overrides improved or reduced accuracy.
9. Develop a root-cause register showing:
   - variance;
   - owner;
   - cause;
   - evidence;
   - controllability;
   - corrective action;
   - due date; and
   - expected benefit.
10. Recommend improvements to:
    - data;
    - driver selection;
    - modeling;
    - governance;
    - review cadence;
    - challenge sessions;
    - ownership;
    - scenario planning; and
    - forecast communication.
11. Do not infer intentional manipulation without evidence.
12. Do not invent actuals, forecast versions, causes, or owner accountability.
13. Distinguish genuine uncertainty from avoidable process failure.

Present the result as:
{{output_format}}

Include:
- executive accuracy assessment;
- forecast-version comparison;
- error metrics;
- variance bridge;
- bias analysis;
- root-cause classification;
- owner and business-unit comparison;
- management-override assessment;
- recurring blind spots;
- corrective-action register;
- process-improvement recommendations; and
- priorities for the next forecast cycle.