BIA105 · Scenario Planning and Decision Models

Categories: Data & BI
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About Course

Build driver-based models that test business decisions under uncertainty using scenarios, sensitivity analysis, decision trees and Monte Carlo simulation.

Advance · 8 hours · 4 lessons, a lab, an assessed assignment and a portfolio piece

Who it is for

Analysts, finance-minded managers and owners facing decisions such as opening a location, hiring, pricing changes or large purchases.

Prerequisites

BIA104 or equivalent: comfortable with spreadsheet formulas, KPIs and basic forecasting concepts.

What you will be able to do

  • Build a driver-based financial model that separates assumptions from calculations.
  • Run best, base and worst scenarios and a one-way sensitivity analysis with a tornado chart.
  • Calculate expected values with a decision tree, including the value of waiting for information.
  • Run a Monte Carlo simulation and report the probability of reaching a target.

Lessons

  • Lesson 1 · Driver-based models and scenarios
  • Lesson 2 · Sensitivity analysis and decision trees
  • Lesson 3 · Monte Carlo simulation and decision memos
  • Lesson 4 · Lab and assignment

Lab: Should we open a second location?

You will model the decision of a café owner considering a second location. Starting from drivers such as daily customers, average ticket, margin, rent and staffing, you will build scenarios, a tornado chart, a simple decision tree and a 5,000-run Monte Carlo simulation that estimates the probability of breaking even within 18 months.

Free tools: Google Sheets (free); optional Google Colab with NumPy for the simulation

Assessed assignment

Model a significant decision for a real or realistic small business. Build a driver-based model with a clearly separated assumptions tab, run three scenarios, a one-way sensitivity analysis on at least five drivers and a Monte Carlo simulation of at least 1,000 runs. Write a two-page decision memo that states the recommendation, the probability of reaching the target, the drivers that matter most and the signals that would make you change course.

Criterion Weight What excellent work looks like
Model structure 25% Assumptions, calculations and outputs are separated; formulas are traceable and free of hard-coded numbers.
Scenario and sensitivity work 25% Scenarios are internally consistent, sensitivity ranges are justified and the tornado chart is correct.
Probabilistic analysis 25% Distributions are reasonable and explained, the simulation is reproducible and results are interpreted correctly.
Decision memo 25% The recommendation is clear, uncertainty is stated honestly and early warning signals and a review plan are defined.

Portfolio piece

A driver-based decision model with scenarios, a tornado chart and a Monte Carlo simulation, plus a two-page decision memo.

Self-paced, 12-month access. Questions are answered in writing through the student panel. A completion record is issued when you finish the course.

Next step

Continue with BIA106 · Data Storytelling for Leaders · 8 hours · $56.00

Continue to the next course

Prefer the whole program? Program in Business Intelligence & AI Decision Making · $277.00 (10% less than buying the courses separately)

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What Will You Learn?

  • Build a driver-based financial model that separates assumptions from calculations.
  • Run best, base and worst scenarios and a one-way sensitivity analysis with a tornado chart.
  • Calculate expected values with a decision tree, including the value of waiting for information.
  • Run a Monte Carlo simulation and report the probability of reaching a target.

Course Content

BIA105 · Scenario Planning and Decision Models (8 h)
Build driver-based models that test business decisions under uncertainty using scenarios, sensitivity analysis, decision trees and Monte Carlo simulation.

  • Lesson 1 · Driver-based models and scenarios
  • Lesson 2 · Sensitivity analysis and decision trees
  • Lesson 3 · Monte Carlo simulation and decision memos
  • Lesson 4 · Lab and assignment
  • BIA105 Assessment · Scenario Planning and Decision Models
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