Roi Npv Sensitivity Model

ROI / NPV Sensitivity Model

Purpose

Calculates ROI, NPV, and IRR plus a sensitivity analysis across scenarios.

Anchored in research

• w95 business-case-builder
• aj-geddes — business-case-development

Method

1. Establish the cash-flow baseline — incremental costs and benefits by period, compared against a credible do-nothing baseline, not against zero. ROI and NPV overstate the case if they're compared to nothing happening instead of to what would happen anyway.
2. Select and justify a discount rate (e.g. WACC, an internal hurdle rate, or the organization's standard rate) — never invent this figure; ask for it, use a marked placeholder, or use a clearly flagged conservative default.
3. Compute NPV (the sum of discounted net cash flows), IRR (the discount rate at which NPV = 0), payback period, and ROI (net benefit ÷ cost) side by side — a single metric can look attractive while another flags a problem, e.g. a fast payback period paired with a negative NPV at the real discount rate.
4. Run a one-at-a-time sensitivity analysis: vary each key input (adoption rate, unit cost, benefit-realization timing, discount rate) by a defined range (e.g. ±20%) while holding the others constant, and rank the inputs by how much they move NPV. This produces a tornado chart that identifies which two or three assumptions actually drive the result.
5. Build at least a downside (pessimistic) and an upside (optimistic) scenario, not only the base case — a sensitivity analysis whose worst case still looks comfortable isn't a real stress test.
6. Report the breakeven point for the most sensitive variable (e.g. "adoption has to exceed X% for NPV to stay positive") so the decision-maker sees exactly which assumption they're betting on.

Available scripts

• scripts/roi_npv_model.py -- calculates NPV, IRR (bisection search), payback period, and ROI from a list of incremental net cash flows (already vs. the do-nothing baseline, per Method step 1), then runs the one-at-a-time sensitivity analysis from step 4, an upside/downside scenario pair from step 5, and the breakeven estimate from step 6. Run it once cash flows and a discount rate are known, instead of computing NPV/ IRR by hand -- it also catches the sign-flip cases (no IRR in a sane range) that are easy to get wrong manually.

  python3 scripts/roi_npv_model.py --example > input.json   # see the input shape
  python3 scripts/roi_npv_model.py input.json                # or: ... - <<< '{...}'
  

  Stdlib only (json, argparse, math) -- no install needed. Exit code 1 on invalid input (missing discount_rate, fewer than 2 cash-flow periods), with a specific error on stderr.

What this skill does NOT do

• Doesn't make the final decision for you — it produces a structured draft to support a human decision.
• Doesn't confirm figures, market data, or competitor data from memory — it uses the inputs you provide, or marks an assumption clearly ([assumption — verify]).
• Doesn't invent precise currency amounts — it calculates from the baseline values you provide and makes every assumption visible.

Refinement notes

Areas to keep deepening with real practice:

• your own rules of thumb and heuristics for this technique
• concrete templates (into ../../references/)
• reference cases / your own examples
• what this skill deliberately does not do (guardrails, common mistakes) — add to the list above

This is an internal working note, not a claim about the skill's current usability. Track depth privately via the maturity field in skills_index.json (see ../../../meta/maturity_levels.md). Don't add new fields to the frontmatter — name and description are the only ones allowed (see ../../../meta/frontmatter_schema.md).

Continue from here

• Next in this pack: ../risk-matrix-and-mitigation/SKILL.md — Identifies and scores risks (probability × impact) and designs mitigations.
• Before this (if the inputs come from a demo/PoC): ../../../prototyping-and-demonstration/skills/demo-to-business-case-bridge/SKILL.md — run the sensitivity analysis especially on the assumptions that skill flagged as weakest in the assumption chain.
• A ready-made skill chain for this situation: see ../../../playbooks/
• This pack's shared guardrails: ../../CLAUDE.md

References

• ../../references/ — the pack's shared background material
• ../../CLAUDE.md — the pack's shared guardrails