Authors: Roman Stepanov, Mariia Kozlova, and Julian Scott Yeomans
Source: Kozlova, M., & Yeomans, J. S. (Eds.). (2024). Sensitivity Analysis for Business, Technology, and Policymaking: Made Easy with Simulation Decomposition (SimDec). Taylor & Francis. https://doi.org/10.4324/9781003453789
License: CC BY-NC-ND 4.0
📖 Read full Chapter 4: Ch4.pdf
Investment decisions often rely on static financial models and single-number indicators like Net Present Value (NPV). But in real life, projects are shaped by multiple uncertainties and stakeholder perspectives. This chapter shows how Simulation Decomposition (SimDec) brings clarity, flexibility, and actionability to financial modelling — allowing businesses to move beyond the binary “accept/reject” logic of traditional NPV rules.
The chapter starts by reviewing standard financial evaluation methods:
- Static NPV calculation
- Scenario analysis
- One-at-a-time sensitivity (OAT)
- Monte Carlo simulation
Each step adds sophistication, but even Monte Carlo simulations struggle to answer:
“What exactly should we change to make this project profitable?”
That’s where SimDec comes in — it identifies which input combinations lead to success or failure and provides clear direction for decision-makers.
Using the metaphor of a layered cake (page 4), the authors explain that every project involves blending ingredients: revenues, costs, taxes, CAPEX, etc. Different stakeholders (e.g., CFOs, accountants, engineers, bank managers) see the “cake” from different angles.
SimDec helps reconcile these views into a multi-perspective financial model that is both technically sound and action-oriented.
The model tested different tax conditions:
- Immediate vs. delayed payment
- Lower tax rate (10% vs. 25%)
- Five-year tax holiday
Result: None of these schemes significantly improved profitability. Price remained the dominant factor across all cases. SimDec helped redirect attention to what matters most — pricing assumptions — avoiding wasted energy on ineffective tax tweaks.
With fluctuating demand, the model explored whether renting out unused production space could improve outcomes.
- Without renting: Demand drove most of the risk, and NPV often turned negative
- With renting enabled: NPV improved dramatically, and Price became the top sensitivity factor
Result: SimDec showed that a simple operational lever (renting) could meaningfully reduce risk exposure and improve upside.
This case included multiple uncertainties: input costs, inflation, sales volume, and price.
- Without hedging: Wide NPV range; Price was the #1 driver (83% sensitivity index)
- With hedging: Downside reduced, but upside also capped; Materials cost overtook Price as the dominant factor
Result: SimDec clarified the trade-off between risk mitigation and potential profit, helping decide whether hedging is worthwhile.
- SimDec transforms classic financial models into decision-support tools.
- It shows how and when managerial actions (like renting or hedging) can influence outcomes.
- The method surfaces hidden interactions between variables that traditional methods miss.
- Even complex stakeholder inputs (from finance, engineering, risk, and operations) can be unified in one model.
SimDec makes financial modelling actionable — not just descriptive.
It helps shift organizational culture from passive appraisal to proactive investment design.
Instead of asking: "Is this project profitable?"
SimDec helps you ask: "How do we make it profitable — and what levers can we pull?"
Based on Chapter 4 of Sensitivity Analysis for Business, Technology, and Policymaking
© Roman Stepanov, Mariia Kozlova, and Julian Scott Yeomans, 2024 — CC BY-NC-ND 4.0.
This summary is an independent derivative work created for educational and indexing purposes, not affiliated with the original publisher.