A Teaching Case Brings Monte Carlo Simulation to Break-Even Analysis

Cost-volume-profit analysis is one of the first tools taught in management accounting. It answers a basic question: how many units must a business sell to cover its costs and earn a target profit? A teaching case published in Issues in Accounting Education updates that tool for a world of data analytics. Students replace single-number estimates with a Monte Carlo simulation written in Python.

The case was written by four authors. Two are McCoy College of Business faculty: Mina Pizzini, Chair and Professor in the Department of Accounting, and Mark E. Vargus, Professor of Instruction in the Department of Accounting. Their co-authors are James W. Hesford of the University of Missouri-St. Louis and Michael J. Turner of the University of Queensland.

This is an instructional resource, not an empirical study. Its purpose is to help instructors teach uncertainty in a hands-on way.

The problem with point estimates

Traditional cost-volume-profit analysis uses one value for each input: price, unit cost, fixed cost, and volume. The result is one break-even figure. Instructors often add scenario analysis, which reruns the model with best-case and worst-case inputs. That gives three answers instead of one. It still says nothing about how likely each outcome is.

Real inputs vary. Prices drift. Volume rises and falls with demand. A manager who plans around a single number can be surprised by results that were always possible but never visible in the model.

How the case works

Students begin with historical sales data. They use it to forecast the average and the spread of both volume and price for the coming year. The spread is measured by the standard deviation, a statistic that captures how far values tend to stray from the average.

The case provides the forecasted cost inputs along with step-by-step instructions for building a Monte Carlo simulation in Python. A Monte Carlo simulation draws random values for uncertain inputs, runs the model, and repeats the process many times. The result is not one profit figure but a distribution of thousands of them.

Students then build visualizations. Time series plots show the sales history. Histograms show how the simulated profit outcomes cluster and how often they fall below zero. Seeing the shape of the distribution makes the idea of risk concrete.

For instructors who prefer not to teach Python, the authors note that the case can be completed in Excel.

Why it matters for educators

Accounting programs face pressure to add data analytics without dropping core content. This case does both in one assignment. Students revisit a foundational tool and learn or reinforce a programming language that employers increasingly expect. The Python steps are spelled out, so prior coding experience is not required.

The case also extends earlier teaching cases on the same topic. Those cases typically supplied the input estimates. Here, students derive the forecasts themselves from data, which adds a layer of judgment.

Why it matters for finance teams

The lesson is not limited to the classroom. Many corporate planning models still rest on point estimates and a handful of scenarios. The same simulation approach can be applied to a budget, a pricing decision, or a new product launch. It answers a better question than “what is the break-even?” It answers “how likely are we to reach it?”

Finance teams that already use Python or Excel can adopt the method with modest effort. The payoff is a clearer picture of downside risk before a decision is made.

This summary is based on the paper’s abstract. The full article reports the data, methods, and detailed results.

What it means for managers

• A single break-even estimate hides risk. Simulating thousands of price and volume combinations shows the range of profit outcomes and how likely a loss is.
• The case pairs a core management accounting tool with Python, a language widely used in data analytics. Students practice both at once.
• Instructors who do not want to teach Python can run the same case in Excel. Finance teams can apply the same logic to their own planning models.

Hesford, J. W., Pizzini, M., Turner, M. J., & Vargus, M. (2025). Using Python in management accounting: Monte Carlo simulation for cost-volume-profit analysis. Issues in Accounting Education, 40(3), 185-201. 10.2308/issues-2023-065