Working with Scenario Analysis in Financial Modeling

Introduction

You're building forecasts and need a repeatable way to turn uncertainty into action, so use scenario analysis - structured sets of forward-looking assumptions that produce alternate financial outcomes - to map how different paths affect the business. The purpose is practical: quantify ranges for revenue, EBITDA, free cash flow, and liquidity so you can compare trade-offs, set thresholds, and make decisions under clear bounds. Scenario analysis converts uncertain inputs into decision-ready outcomes. You'll defintely leave single-point forecasts behind and get a clearer sense of risk and opportunity.

Key Takeaways

• Scenario analysis converts uncertainty into decision-ready ranges for revenue, EBITDA, free cash flow, and liquidity.
• Design scenarios deliberately: pick a time horizon, 3-5 key drivers, define base/upside/downside (and recovery shapes), and record likelihoods and rationale.
• Implement with a single source of truth for assumptions, scenario switches/named ranges, automatic P&L/Cash/Balance Sheet recalculation, and reconciliation checks.
• Use sensitivity, two-way/tornado analysis and Monte Carlo/correlation modelling to quantify probabilities, prioritize drivers, and capture tail/covenant risk.
• Present clear ranges and probability-weighted outcomes, call out triggers/break-evens and required actions, document assumptions, and set ownership and review cadence (quarterly scenarios, monthly cash checks).

Purpose and use cases

Inform capital-allocation and investment decisions

You're deciding where to put scarce capital and need a discipline to compare choices across uncertainty; scenario analysis turns variant revenue and margin paths into ranked investment outcomes you can act on.

Start with these steps:

• Centralize decision metrics: NPV, IRR, payback, and probability-weighted free cash flow.
• Build 3-5 scenarios (base, upside, downside, recovery) and link each to project cash flows.
• Run a probability-weighted NPV (expected value) and a downside-loss table (max loss by scenario).
• Rank projects by expected return and downside severity; set gating rules (e.g., minimum expected IRR and maximum downside loss).

Best practices and checks:

• Use a common discount rate across projects for comparability.
• Stress test inputs that drive long-term value: terminal growth, margin recovery, and capex intensity.
• Keep a simple decision rule: proceed if expected NPV > cost of capital and worst-case payback < threshold.

Illustrative FY2025 example - not a forecast but a template for your model: baseline project cash flows give revenue $280,000,000, EBITDA $42,000,000, free cash flow $25,000,000. One-liner: Scenario analysis converts uncertain project inputs into ranked IRR and downside limits so you can pick the best bets.

Here's the quick math: if upside NPV is $18m (40% chance) and downside NPV is -$6m (20% chance), expected NPV = 0.418 + 0.46 + 0.2(-6) = $9.6m. What this estimate hides: correlation across projects and execution risk.

Test liquidity and covenant risk under stress

You need to know whether the business survives a shock or trips a covenant; scenario analysis quantifies when you run out of cash or breach debt terms so you can plan fixes ahead of time.

Operational steps to build a stress test:

• Create a 13-week cash model and extend to 12-24 months for covenant analysis.
• Link revenue, working capital, capex, and debt amortization to scenario drivers.
• Model covenant ratios monthly (leverage: net debt/EBITDA; interest cover: EBITDA/interest).
• Define breach triggers and pre-approved remedies (RCA: reserve lines, asset sales, CAPEX deferral).

Best practices:

• Test at least three stress paths: mild (-10% revenue), severe (-25%), and tail (-40% plus delayed recovery).
• Include timing - a one-quarter shock is different from a two-year slide; model liquidity rolling forward.
• Assign owners: Treasury/FP&A keeps the weekly cash view; Debt Manager owns covenant remediation options.

Illustrative FY2025 numbers: opening cash $10,000,000, monthly burn in downside $4,000,000 → runway = 2.5 months. Net debt $60,000,000 / EBITDA $42,000,000 = 1.43x leverage today; under a severe 25% EBITDA drop to $31,500,000, leverage moves to 1.90x. One-liner: Scenario tests show how many months of runway you have and when covenants bite so you can pre-position fixes.

Here's the quick math: covenant headroom = covenant limit (e.g., 3.0x) minus current ratio; available shock capacity = EBITDA after shock × covenant limit - net debt. What this hides: off-balance-sheet items and new working-capital draws.

Support valuation ranges for M&A or fundraising and guide operational contingency budgets

If you're selling, buying, or raising capital you need valuation ranges, not one number; scenario outputs feed offer guidance, pricing cushions, and contingency budgets for operations.

Steps to produce usable valuations and contingency plans:

• Produce three valuation points: worst, median, best, plus a probability-weighted enterprise value.
• Translate valuations into funding needs: equity required, debt capacity, and implied dilution.
• Define contingency budgets tied to triggers: if cash falls below 30 days runway, freeze hiring and cut discretionary capex by 50%.
• Document conditional playbook: action, owner, and timeline for each trigger (example: hire freeze → HR; capex delay → Ops; bridge financing → CFO).

Practical checks and negotiation tactics:

• When fundraising, present a banded valuation (range) not a single price; show the probability-weighted raise size needed under downside.
• In M&A, stress buyer synergies across downside scenarios; buyers will price in correlation risk.
• Keep a contingency budget line in the base case (recommend 5-10% of operating expenses) to avoid reactive cuts.

Illustrative FY2025 operational math: SG&A = $36,000,000; a targeted 10% cut saves $3,600,000 annually, extending runway by ~0.9 months in the downside example above. One-liner: Use scenario-driven valuation bands to set offer limits and keep a contingency budget that buys time, not headlines.

Here's the quick math: probability-weighted enterprise value = 0.25best + 0.5median + 0.25worst; if best = $520m, median = $420m, worst = $280m, weighted EV = $410m. What this estimate hides: market multiple compression and deal timing risk - defintely model both.

Designing scenarios and assumptions

You're about to build scenario sets that convert uncertainty into numbers you can act on; the direct takeaway: pick the right horizon, isolate the 3-5 drivers that move cash and EBITDA, and make scenario shapes and probabilities explicit so your model yields decision-ready ranges.

Choose time horizon and reporting cadence

Pick a horizon tied to the decision: use 12 months for liquidity and covenant stress, 24 months for operating plans, and 36 months when capital allocation or M&A are on the table. Monthly cadence for cash, quarterly for P&L and covenant runs is the practical default.

One-liner: shorter horizon for cash, longer for strategy.

Practical steps:

• Set baseline at FY2025 actuals - e.g., revenue $150,000,000, EBITDA $27,000,000, cash $20,000,000, debt $40,000,000.
• Build monthly cash flows for the first 12 months, then quarterly or annual for months 13-36.
• Calculate runway under downside: if downside FCF causes a monthly cash burn of $2,000,000, $20,000,000 cash gives ~10 months runway (here's the quick math: $20,000,000 / $2,000,000 = 10 months).
• Validate timing: capex and receivables hit cash immediately; revenue recognition may lag - align model timing to accounting rules.

What this estimate hides: assumptions about seasonality and one-time receipts can flip runway fast; capture those in a monthly sheet.

Identify 3-5 key drivers

Focus on the handful of inputs that change EBITDA and cash materially: revenue growth, gross margin (cost of goods sold), working capital (days), capex, and pricing. Use FY2025 values as the baseline for sensitivity math.

One-liner: pick few, measure impact per unit.

Concrete guidance and quick math:

• Revenue sensitivity: with baseline $150,000,000, a 1% revenue change = $1,500,000 of top-line movement.
• Margin sensitivity: a 1 percentage-point gross-margin move on $150,000,000 = $1,500,000 gross profit swing (same quick math as revenue %).
• Working capital days: 1 day = $411,000 (calculated $150,000,000/365). Ten days change = ~$4,110,000.
• Capex: treat as lumpy - model base $8,000,000 FY2025, define upside/downside spend timing separately.

Best practices:

• Rank drivers by impact (run a one-way sensitivity to produce a tornado chart).
• Use driver templates: baseline value, distribution/range, source (sales plan, supplier contracts), and confidence level.
• Link all drivers to downstream sheets (P&L, balance sheet, cash) so a single driver change recalculates everything.

Define base, upside, downside and recovery-path shapes; assign probabilities and record rationale

Define clear, numeric scenarios relative to FY2025 baseline. Example definitions off FY2025 $150,000,000 revenue:

• Base: revenue +8% => $162,000,000.
• Upside: revenue +20% => $180,000,000.
• Downside: revenue -15% => $127,500,000.

One-liner: name scenarios, quantify them, then attach a probability and a recovery shape.

Assign probabilities and compute expectation (example): Base 55%, Upside 20%, Downside 25%. Probability-weighted revenue = 0.55×$162,000,000 + 0.20×$180,000,000 + 0.25×$127,500,000 = $156,975,000.

Recovery-path shapes and timing:

• V-shaped: hit bottom in 1-3 months, recover in 6-12 months.
• U-shaped: prolonged weakness, 12-24 months to recover.
• L-shaped: long-term structural decline, no recovery inside the horizon.
• W-shaped: repeated shocks; model as two dips separated by recovery months.

Documentation and rationale (must be explicit):

• Create a scenario table: driver, FY2025 baseline, base/up/down values, probability, qualitative rationale (source: sales forecast, market index, supplier notice), last-updated date, owner.
• Record trigger thresholds: revenue drop > 10% triggers contingency A; cash $10,000,000 triggers covenant talk.
• Where probabilities are judgmental, record the basis (e.g., industry report, management guidance, macro GDP outlook) and use qualitative likelihoods if numeric probability is inappropriate.

What this estimate hides: correlations across drivers - a revenue hit often compresses margin and worsens working capital; capture this by linking assumptions or modelling joint scenarios.

Next step: Finance: create a scenario table and run three-case P&L and monthly cash for the next 12 months by Friday (owner: Finance).

Implementing scenarios in a financial model

Centralize assumptions in a single block

You're about to run scenarios but your growth rates, margins, and working-capital drivers are scattered across tabs - that makes errors and conflicting updates almost guaranteed.

Create an assumptions sheet called Assumptions_Scenario and put every forward-looking driver there in a time-series table (years or months across columns, drivers down rows). Name each cell or range (named ranges) so formulas reference names, not sheet coordinates. One place to change, whole model updates.

Include these mandatory columns per driver: source, last-updated date, rationale (short), IFRS/GAAP treatment, and a notes cell for judgement. Add adjacent columns for Base, Downside, Upside, and Recovery-path shape (fast, staged, slow).

Example FY2025 driver snapshot (use as template):

• Revenue (FY2025 base): $120,000,000
• Revenue downside: $96,000,000 (-20%)
• Gross margin (base): 40%
• EBITDA margin (base): 15% → $18,000,000

Best practices: keep the block top-left in the workbook, protect the sheet, log changes with a simple change table, and defintely keep a rationale column - future reviewers will thank you.

Use scenario switches, Excel data tables, or named ranges to toggle cases

You need to flip between Base, Downside, and Upside quickly when debating decisions or stress-testing lenders - manual edits are too slow and error-prone.

Create a single integer switch cell named ScenarioSwitch and map it to the set of multipliers or inputs. Use INDEX or CHOOSE so all downstream formulas reference the named table rather than duplicated hardcoding. Example mapping:

• ScenarioSwitch = 1 → Base multiplier = 1.00
• ScenarioSwitch = 2 → Downside multiplier = 0.80
• ScenarioSwitch = 3 → Upside multiplier = 1.15

Inline formula pattern (write into revenue driver cell): =BaseRevenueFY2025 INDEX(Multipliers,ScenarioSwitch). Use named ranges: BaseRevenueFY2025, Multipliers.

For sensitivity sweeps, use Excel two-way data tables (for price × volume) and tornado charts to rank drivers. For probabilistic views, keep a parallel set of parameter tables to feed Monte Carlo tools - but keep the switch for executive-ready scenarios.

Recalculate P&L, cash flow, balance sheet automatically and validate with reconciliation checks

You want scenario flips to update financial statements end-to-end and show where covenant or liquidity pain appears in seconds.

Link every P&L line to your assumptions: revenue = price × volume; COGS = revenue × COGS% or detailed SKU layering. Let working-capital drivers (DSO, DPO, Days Inventory) feed the balance sheet. Capex and depreciation follow policy cells in assumptions. Cash flow should be the derived result of P&L adjustments, balance-sheet deltas, debt movements, and capex.

Sample recalculation outputs for FY2025 (base vs downside):

• Revenue (base): $120,000,000 → Gross profit $48,000,000 (40% gross margin)
• EBITDA (base): $18,000,000
• Free cash flow (base): $10,000,000
• Revenue (downside): $96,000,000 → EBITDA $9,600,000
• Opening debt: $50,000,000; scheduled amortization $5,000,000

Validation checklist - add automated checks that return TRUE/FALSE and flag exceptions:

• Cash waterfall: Beginning cash + Cash from ops + Cash from investing + Cash from financing = Ending cash
• Balance sheet balance: Assets = Liabilities + Equity
• Debt schedule reconcile: Closing debt = Opening debt - amortization + new borrowings
• Net debt calculation: Net debt = Debt - Cash (show both)
• Covenant tests: Interest coverage = EBITDA / Interest expense; Net leverage = Net debt / EBITDA (compare to covenant thresholds)

Example covenant trigger: if Net debt / EBITDA exceeds 3.0x, flag breach. With downside EBITDA $9,600,000 and Net debt $45,500,000 (debt $50,000,000 minus cash $4,500,000), Net leverage = 4.74x - automatic breach flag.

Technical tips: avoid hidden circulars; if you must model interest as a function of ending cash, isolate with a reconciled debt schedule or use iterative calc sparingly. Add row-level comments for auditors and a Reconcile tab that documents each check and last validation date.

Next step: Finance to build the Assumptions_Scenario sheet, implement ScenarioSwitch, and deliver three-scenario P&L, cash, and covenant-impact workbook by December 14, 2025.

Advanced techniques: sensitivity, Monte Carlo, and correlations

You're trying to move from guesswork to quantified risk so you can act; the quick takeaway: use sensitivity analysis to rank drivers, two-way/tornado views to show interaction and leverage, and Monte Carlo with correlations to estimate true probability-weighted outcomes and tail risk.

Differentiate sensitivity analysis from multi-driver scenarios

Sensitivity analysis changes one input at a time to measure the impact on a single output (for example, change revenue growth while holding everything else constant). Multi-driver scenarios change multiple inputs together to represent coherent business states (for example, downside: revenue -25%, gross margin -400 bps, working capital outflow +2% of revenue).

Steps to run both:

• Pick the output(s): EBITDA, free cash flow (FCF), covenant metric.
• For sensitivity: vary one driver across a sensible band (eg, -30% to +30% in 5% steps) and record output.
• For scenarios: define base/upside/downside as linked sets of drivers and flag the scenario logic in the model.
• Compare: use sensitivity to prioritize; use scenarios to test realistic, internally-consistent states.

Best practices and considerations:

• Keep assumptions realistic: tie margin moves to cost behavior.
• Limit sensitivities to key drivers: revenue, gross margin, working capital, capex, price.
• Record rationale for each band and date them.
• One clean one-liner: sensitivity tells you what matters, scenarios tell you what happens together.

Build tornado charts and two-way data tables for prioritized drivers

Tornado charts rank drivers by impact on a chosen metric; two-way tables show interaction between two drivers (eg, price change vs. volume). Both make trade-offs visible to non-technical stakeholders.

Concrete steps to build a tornado:

• Base: set model at FY2025 baseline (example: revenue $250,000,000, EBITDA margin 18% → EBITDA $45,000,000).
• For each driver, compute output at low and high bounds (eg, revenue -25% → $187,500,000, revenue +15% → $287,500,000).
• Calculate delta from base; sort drivers by absolute delta; plot bars centered on base to form the tornado.

Concrete steps to build a two-way table:

• Pick two high-priority drivers (eg, price change from -10% to +10%, volume change from -20% to +20%).
• Populate grid cells with the model outputs (EBITDA or FCF) and format conditional colors for quick reading.
• Highlight break-evens (cells where EBITDA < 0 or covenant breaches occur).

Best practices and gotchas:

• Use consistent units and horizons (eg, 12‑month FY2025 view).
• Fix other drivers at base so the tornado and table isolate effects.
• Annotate assumptions so viewers see if a cell is plausible.

One clean one-liner: tornadoes show the biggest levers, two-way tables show where two levers combine to break things.

Run Monte Carlo simulations and model correlations and tail events

Monte Carlo gives a probability distribution for outputs by randomly sampling inputs from defined distributions; correlations and tail modeling capture systemic risk and the chance of covenant breaches.

Steps to run a Monte Carlo simulation:

• Choose outputs to track: FY2025 FCF, covenant headroom, and EBITDA.
• Define distributions for each driver (example choices): revenue growth ~ normal(mean 0%, sd 10%), gross margin ~ triangular(12%, 18%, 22%), working capital change ~ lognormal with mu aligned to historical median).
• Set number of iterations - use at least 10,000 to stabilize tail estimates.
• Run draws, recalculate P&L, balance sheet, and cashflow each iteration, and store outputs.
• Report statistics: median, mean, 10th and 90th percentiles, and probability of covenant breach.

Modeling correlations and tail events:

• Build a correlation matrix between drivers (eg, revenue and margin correlation = +0.6; revenue and working capital = +0.3) using historical quarterly data where possible.
• Use a Gaussian copula or rank correlation (Spearman) to impose correlations when sampling; avoid assuming independence.
• Model fat tails with t-distributions or extreme-value distributions for stress-prone variables (eg, demand shocks).
• Overlay scenario-based tail shocks (eg, a 25% revenue shock with margin compression) to validate Monte Carlo tails.

Quick math example and what it hides:

Here's the quick math: baseline FY2025 revenue $250,000,000. A Monte Carlo with revenue sd 10% and margin sd 4% yields a median FCF of $30,000,000, 10th percentile FCF $4,500,000, and 7% probability of covenant breach (net leverage > covenant). What this estimate hides: assumptions on distribution shape and correlations drive tail probability - small changes can double breach probability.

Validation and governance:

• Backtest by running Monte Carlo on prior-year inputs and comparing predicted percentiles with realized outcomes.
• Document distributions, correlation choices, iteration count, and random seed.
• Save raw iteration outputs for forensic checks when a tail event occurs.

One clean one-liner: Monte Carlo gives you probabilities, correlations make those probabilities meaningful, and fat-tail modeling keeps you honest about extreme risk.

Next step: Finance to run a 10,000-iteration Monte Carlo on FY2025 drivers, deliver percentile bands for FCF and covenant breach probability, and share raw outputs in three business days - owner: Finance Modeling Lead.

Presenting results and driving decisions

Show ranges for key metrics: best, median, worst and probability-weighted outcomes

You need clear outcome buckets so executives and the board can act fast when inputs move. Start by publishing a one-page scoreboard that lists Revenue, EBITDA, Free Cash Flow (FCF), and Liquidity for each scenario: Best, Median (P50), Worst.

Steps:

• Pull scenario outputs into a single table by line item.
• Report percentiles: P10, P50, P90.
• Compute probability-weighted (expected) value: multiply each scenario value by its probability and sum.

Example quick math: Best Revenue $120.0m (20%), Median $100.0m (60%), Worst $70.0m (20%) - probability-weighted Revenue = $98.0m. Here's the quick math: 0.2×120 + 0.6×100 + 0.2×70 = $98.0m. What this estimate hides: distribution tails and correlation effects.

One-liner: Turn scenarios into three clean numbers and one expected figure everyone trusts.

Highlight triggers, break-even points, and required management actions by scenario

You need actionable triggers, not just charts. Map thresholds that force decisions - cash runway, covenant headroom, and operational breakevens.

Steps and best practices:

• Define triggers: e.g., Cash runway 45 days, Net Leverage > 3.5x, Interest Coverage 2.0x.
• Calculate operational breakeven: Fixed costs / Contribution margin. Example: fixed costs $25.0m, contribution margin 40% → breakeven Revenue = $62.5m.
• Tier responses: Alert (info), Mitigate (cost saves / working capital), Escalate (board + lenders).
• Assign time-boxed actions: e.g., if runway < 60 days, implement 30% discretional spend freeze within 48 hours.

Use dashboards that flag triggers in red/yellow/green and link each flag to a one-page play describing owner, timing, and expected cash impact.

One-liner: Every metric on the dashboard needs a clear trigger and a one-step action.

Provide clear recommendation and conditional playbook; document assumptions, version control, and update cadence

You want a playbook that leaders can follow without calling Finance at 2 a.m. Build a conditional decision tree tied to scenario outputs and embed it into the model deliverable.

Practical steps:

• Create a conditional playbook table: Trigger → Primary Action → Secondary Action → Owner → Deadline.
• Set RACI: Finance maintains the model (owner), Scenario Lead updates assumptions, CFO approves escalations.
• Version control: use semantic versions (v1.0, v1.1), date-stamp, and store in a shared repo with change notes.
• Document assumptions inline: source, date, and sensitivity note (how a ±100 bps change affects EBITDA).
• Cadence: full-scenario review quarterly; cash stress check monthly; ad-hoc after material events.

Example conditional playbook row: If Liquidity < $15.0m → Pause hiring (owner: HR+Finance) within 7 days → Contact lenders to discuss covenant waivers (owner: CFO) within 3 business days.

Next step: Finance: run three scenarios and deliver P&L, cash, and covenant impacts in 2 weeks (Scenario Lead to supply assumption pack within 3 business days). Defintely keep version notes concise and auditable.

Working with Scenario Analysis - cadence, ownership, and next steps

You're finalizing scenario outputs for FY2025 and need a clear operating rhythm so numbers stay decision-ready. The short take: run a full scenario refresh every quarter and do a focused cash stress check monthly, with clear owners and a two-week sprint to produce the first set of P&L, cash, and covenant impacts.

Quarterly full-scenario review plus monthly cash stress checks

Direct takeaway: schedule a comprehensive scenario review every quarter and a lean cash stress check each month to catch drift early. One-liner: quarterly deep-dive, monthly pulse-check.

If your fiscal year is FY2025, align quarterly reviews to close calendars - for example, run full reviews within the first 10 business days after each quarter close: Q1 close (Apr 10-20), Q2 close (Jul 10-20), Q3 close (Oct 10-20), FY2025 close (Jan 10-20). Monthly cash stress checks should land on the 5th business day of each month.

Steps and best practices:

• Prepare a rolling 12-36 month scenario set before each quarterly review.
• Run a 13-week cash model monthly and refresh assumptions used in the stress test.
• Document changes: who updated assumptions, why, and source data.
• Flag covenant headroom and liquidity triggers in each report.
• Keep one concise deck (3-5 slides) for execs summarizing ranges and triggers.

What to measure: report best, median, and worst cases for revenue, EBITDA, free cash flow, and liquidity; call out any covenant breaches or less than 30 days runway issues.

Assign owners: Finance to maintain model, Scenario Lead to manage assumptions

Direct takeaway: assign clear, operational owners who meet cadence and control access. One-liner: one model owner, one assumptions owner.

Roles and responsibilities:

• Finance model owner - maintain model integrity, refresh actuals, run reconciliations, automate outputs. Expect weekly upkeep ~6-10 hours.
• Scenario Lead - curate macro and operating assumptions, collect inputs from sales, ops, legal, and treasury. Expect monthly coordination ~8-12 hours.
• Controller - validate accounting entries and balance-sheet reconciliations each close.
• Treasury - validate cash lines, debt amortization, and bank covenant language.

Controls and handoffs:

• Use a single-source-of-truth assumptions tab and version control (git or timestamped workbook).
• Require ex-post sign-off: Finance signs model, Scenario Lead signs assumptions.
• Lock formula cells; track material changes in a changelog.

Governance note: if a scenario causes covenant testing within 90 days, trigger an immediate cross-functional review. This is defintely non-negotiable.

Next step: run three scenarios and deliver P&L, cash, and covenant impact within two weeks

Direct takeaway: start now - produce base, downside, and upside scenarios and deliver P&L, cash flow, and covenant impact to stakeholders in 10 business days (two calendar weeks). One-liner: three scenarios, two weeks, executive-ready outputs.

Immediate checklist for the two-week sprint:

• Day 0: agree scope, time horizon (12/24/36 months), and reporting cadence.
• Day 1-2: Scenario Lead collects updated assumptions from Sales, Ops, Treasury.
• Day 3-6: Finance owner loads actuals to date (FY2025 YTD), runs model, and codifies three cases.
• Day 7-9: Reconcile cash movements, debt schedules, and covenant ratios; perform sanity checks.
• Day 10: Deliver package - P&L bridge, 13-week cash, covenant sensitivity table, and recommended management actions.

Deliverable specifics:

• Include a single-sheet executive summary showing best, median, worst outcomes for revenue, EBITDA, free cash flow, and liquidity.
• Attach a covenant matrix showing trigger points and days-to-breach under each scenario.
• Provide an action playbook: immediate (0-30 days), tactical (30-90 days), strategic (>90 days).

Owner and timing assignment: Finance - build and validate model by Day 9; Scenario Lead - confirm assumptions by Day 6; Exec sponsor - review and sign off on Day 10. Finance: draft the deliverables and circulate to stakeholders within 10 business days.