Advanced
Forecast accuracy retrospective: bias, MAPE, and process fixes
Analyzes forecast accuracy using metrics like MAPE and bias, and recommends process improvements. Useful for FP&A teams improving credibility and planning cadence.
Assess forecast accuracy for {company_name} over {period_range}.
Inputs:
- Forecast snapshots over time: {forecast_snapshots}
- Actuals: {actuals}
- Key drivers tracked: {drivers}
- Process notes (who inputs, cadence): {process_notes}
Output:
1) Accuracy metrics by line item and driver (MAPE, bias, hit rate).
2) Where errors concentrate (timing vs magnitude; systematic bias).
3) Root causes (data latency, assumptions, ownership).
4) Improvement plan (process, tooling, driver tracking, governance).
5) Recommendations for next forecast cycle.
Keep the analysis practical and action-oriented.Related Prompts
Management Accounting & FP&A
IntermediateCost allocation model: shared services and drivers
Designs a cost allocation approach for shared services using clear drivers and documentation. Useful for management reporting and chargeback models.
GPT-5.2 Thinking; GPT-4.1; o3-mini
0
0
132
Management Accounting & FP&A
BeginnerExpense variance triage: controllable vs noncontrollable
Separates expense variances into controllable and noncontrollable buckets and drafts targeted follow-ups. Useful for expense owners and cost governance.
GPT-5.2 Thinking; GPT-4.1; o3-mini
0
0
105
Management Accounting & FP&A
IntermediateDriver-based budget model template (revenue, headcount, opex)
Creates a driver-based budgeting framework and templates that link revenue drivers, headcount, and operating expenses. Useful for FP&A teams building a scalable budget process.
GPT-5.2 Thinking; GPT-4.1; o3-mini
0
0
85