codexkit-fpa-scenario-modeling

name	codexkit-fpa-scenario-modeling
description	Build structured 3-scenario financial models (Base / Bull / Bear) for business planning or investment decisions. Sensitize revenue and cost drivers across scenarios, produce P&L summaries, cash flow bridges, and executive narratives. Aligns with FP&A 2.0 and CIMA Management Accounting standards. Use when leadership needs to understand the financial impact range of a decision. Do not use for statutory financial reporting or audit-ready financials.
version	1.0.0
category	data

FP&A Scenario Modeling

Purpose

Transform business assumptions into a structured 3-scenario financial model that gives leadership a clear view of upside, baseline, and downside outcomes.

When to use

• annual or quarterly business planning cycle
• before a major investment decision (new product, M&A, expansion)
• when the CFO asks "what if revenue drops 20%?"
• presenting financial impact range to the board

When not to use

• statutory financial reporting (IFRS/GAAP compliance)
• audit-ready financial statements
• daily operational cost tracking

Inputs

• business model type (SaaS, retail, manufacturing, services)
• historical financials (last 2–3 years if available)
• key revenue drivers (pricing, volume, churn, expansion)
• key cost drivers (headcount, COGS, marketing spend, capex)
• specific decision context (what are we modeling?)
• time horizon (12 months, 3 years, 5 years)

Procedure

1. Define 3 scenarios:
   • Base Case: most likely outcome based on current trajectory
   • Bull Case: upside scenario (best 30% of outcomes)
   • Bear Case: downside/stress scenario (worst 20% of outcomes)
2. Identify key variables to sensitize:
   • Revenue-side: growth rate, gross margin, CAC, churn rate, ASP
   • Cost-side: fixed cost base, variable cost as % revenue, capex, working capital (DSO, DPO, DIO)
3. Build scenario assumption table — side-by-side comparison of all variables across 3 scenarios.
4. Calculate P&L summary per scenario: Revenue → Gross Profit → EBITDA → Net Profit.
5. Build cash flow bridge per scenario: Operating CF → Investing CF → Financing CF → Free CF.
6. Calculate key metrics per scenario: EBITDA margin, ROI, Payback Period, Break-even point.
7. Create sensitivity table — identify which single variable has the largest impact on outcome.
8. Write executive narrative — "Under the base case, the business generates…" with clear decision implications.

Output

• scenario assumption table (side-by-side, 3 columns)
• P&L summary per scenario (Revenue → EBITDA → Net Profit)
• cash flow bridge per scenario
• key metrics comparison table
• sensitivity analysis (which variable moves the needle most)
• executive narrative (1–2 paragraphs per scenario)

Definition of done

• all 3 scenarios have distinct, justified assumptions
• P&L, cash flow, and key metrics are calculated for each scenario
• sensitivity analysis identifies the top 3 swing variables
• executive narrative is decision-ready (not just numbers)

Examples

• "Model 3 scenarios for our SaaS expansion into Southeast Asia over 3 years."
• "What happens to EBITDA if raw material costs increase 15% (bear case)?"
• "Build a scenario model for the board comparing organic growth vs. acquisition."

Quality Criteria

• Data sources and assumptions are explicitly stated
• Calculations are reproducible from provided inputs
• Visualizations or tables have clear labels, units, and time ranges
• Caveats and confidence levels are documented for estimates

Verification (4C)

Check	Question
Correctness	Are formulas, aggregations, and statistical methods applied correctly?
Completeness	Does the analysis cover all requested metrics and time ranges?
Context-fit	Are the chosen metrics relevant to the business question being answered?
Consequence	If this data were used for a decision today, what blind spots remain?

Edge Cases

• Missing or incomplete data — Document gaps and their potential impact on conclusions. Provide ranges instead of point estimates.
• Outliers skewing results — Report with and without outliers. Document the decision to include or exclude.
• Changing data definitions mid-period — Split analysis at the change boundary and note the schema difference.

Changelog

• v1.0.0 — Initial release