Monte Carlo Analysis In A Nutshell

The Monte Carlo analysis is a quantitative risk management technique. The Monte Carlo analysis was developed by nuclear scientist Stanislaw Ulam in 1940 as work progressed on the atom bomb. The analysis first considers the impact of certain risks on project management such as time or budgetary constraints. Then, a computerized mathematical output gives businesses a range of possible outcomes and their probability of occurrence.

Understanding the Monte Carlo analysis

The analysis first considers the impact of certain risks on project management such as time or budgetary constraints.

Then, a computerized mathematical output gives businesses a range of possible outcomes and their probability of occurrence.

The output shows the potential consequences for the most and least conservative actions and details the middle-of-the-road actions that fall in between.

Probability distributions allow businesses to quantitatively determine the level of risk associated with decision making.

In turn, the decision with the most optimal balance of benefit and risk can be selected.

The Monte Carlo analysis is used in a broad swathe of industries such as finance, manufacturing, insurance, and transportation. 

Conducting a Monte Carlo analysis

The first requirement of a Monte Carlo analysis is spreadsheet data. Most spreadsheets incorporate:

  • Outputs – such as cash flow, profit, or sales volume.
  • Inputs – or quantitative factors such as market size, material cost, or production capacity.

For example, a company that builds prefabricated homes might have output data on the total cost of building each home.

Input data would quantify the cost of each component, such as the foundation, plastering, windows, and land acquisition.

For each input, the company then determines a minimum, maximum, and best guess value.

This is performed because component costs tend to fluctuate.

By establishing a minimum and maximum value for each input cost, the business has an idea of the uncertainty of the total output value. The best guess value also determines what the project is likely to cost.

However, there is a better way to calculate uncertainty.

The power of computers

The simple spreadsheet analysis that the home construction company uses has several drawbacks.

It does not consider probabilities of a scenario, nor does it consider the number of combinations that could constitute a scenario.

Indeed, if the company uses 11 input variables with each valued three different ways, over 177,000 combinations can influence uncertainty.

The Monte Carlo analysis replaces the simple “three value” model with complex functions that generate random samples.

These random samples are represented by probability distributions that represent uncertainty in a vast number of scenarios.

Benefits of the Monte Carlo analysis

The primary benefit of the Monte Carlo analysis lies in moving uncertainty from a single simulation to a probabilistic simulation.

Returning to the home construction company:

  • A single simulation of an uncertain system is usually a qualified statement. For example, “If the cost of cement reaches a certain price, our business model may become unprofitable.”
  • The result of a probabilistic Monte Carlo analysis is a quantifiable probability. For example, “If the cost of cement reaches a certain price, there is a 35% chance that our business model becomes unprofitable.

As we have seen, there is also an inherent benefit in the computational power of complex data analysis

The Monte Carlo analysis provides many separate and independent results, with each suggesting a possible future scenario. Results are attained quickly and accurately using common probability distributions such as normal, lognormal, uniform, and triangular.

Ultimately, probability distributions are a much more realistic way of describing variable uncertainty in risk analysis. This helps businesses prepare for and manage risk.

Key takeaways

  • The Monte Carlo analysis is a risk management technique that uses probability distributions.
  • The Monte Carlo analysis allows decision-makers to determine the level of risk in making each decision. The analysis uses mathematical functions to generate many thousands of sample scenarios based on the complex interaction of input values and variables.
  • The Monte Carlo analysis helps businesses move away from simplistic risk assessment decisions by using powerful computational methods that yield fast and accurate results.

Connected Analysis Frameworks

Cynefin Framework

The Cynefin Framework gives context to decision making and problem-solving by providing context and guiding an appropriate response. The five domains of the Cynefin Framework comprise obvious, complicated, complex, chaotic domains and disorder if a domain has not been determined at all.

SWOT Analysis

A SWOT Analysis is a framework used for evaluating the business’s Strengths, Weaknesses, Opportunities, and Threats. It can aid in identifying the problematic areas of your business so that you can maximize your opportunities. It will also alert you to the challenges your organization might face in the future.

Personal SWOT Analysis

The SWOT analysis is commonly used as a strategic planning tool in business. However, it is also well suited for personal use in addressing a specific goal or problem. A personal SWOT analysis helps individuals identify their strengths, weaknesses, opportunities, and threats.

Pareto Analysis

The Pareto Analysis is a statistical analysis used in business decision making that identifies a certain number of input factors that have the greatest impact on income. It is based on the similarly named Pareto Principle, which states that 80% of the effect of something can be attributed to just 20% of the drivers.

Failure Mode And Effects Analysis

A failure mode and effects analysis (FMEA) is a structured approach to identifying design failures in a product or process. Developed in the 1950s, the failure mode and effects analysis is one the earliest methodologies of its kind. It enables organizations to anticipate a range of potential failures during the design stage.

Blindspot Analysis

A Blindspot Analysis is a means of unearthing incorrect or outdated assumptions that can harm decision making in an organization. The term “blindspot analysis” was first coined by American economist Michael Porter. Porter argued that in business, outdated ideas or strategies had the potential to stifle modern ideas and prevent them from succeeding. Furthermore, decisions a business thought were made with care caused projects to fail because major factors had not been duly considered.

Comparable Company Analysis

A comparable company analysis is a process that enables the identification of similar organizations to be used as a comparison to understand the business and financial performance of the target company. To find comparables you can look at two key profiles: the business and financial profile. From the comparable company analysis it is possible to understand the competitive landscape of the target organization.

Cost-Benefit Analysis

A cost-benefit analysis is a process a business can use to analyze decisions according to the costs associated with making that decision. For a cost analysis to be effective it’s important to articulate the project in the simplest terms possible, identify the costs, determine the benefits of project implementation, assess the alternatives.

Agile Business Analysis

Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.

SOAR Analysis

A SOAR analysis is a technique that helps businesses at a strategic planning level to: Focus on what they are doing right. Determine which skills could be enhanced. Understand the desires and motivations of their stakeholders.

STEEPLE Analysis

The STEEPLE analysis is a variation of the STEEP analysis. Where the step analysis comprises socio-cultural, technological, economic, environmental/ecological, and political factors as the base of the analysis. The STEEPLE analysis adds other two factors such as Legal and Ethical.

Pestel Analysis

The PESTEL analysis is a framework that can help marketers assess whether macro-economic factors are affecting an organization. This is a critical step that helps organizations identify potential threats and weaknesses that can be used in other frameworks such as SWOT or to gain a broader and better understanding of the overall marketing environment.

DESTEP Analysis

A DESTEP analysis is a framework used by businesses to understand their external environment and the issues which may impact them. The DESTEP analysis is an extension of the popular PEST analysis created by Harvard Business School professor Francis J. Aguilar. The DESTEP analysis groups external factors into six categories: demographic, economic, socio-cultural, technological, ecological, and political.

Paired Comparison Analysis

A paired comparison analysis is used to rate or rank options where evaluation criteria are subjective by nature. The analysis is particularly useful when there is a lack of clear priorities or objective data to base decisions on. A paired comparison analysis evaluates a range of options by comparing them against each other.

Related Strategy Concepts: Go-To-Market StrategyMarketing StrategyBusiness ModelsTech Business ModelsJobs-To-Be DoneDesign ThinkingLean Startup CanvasValue ChainValue Proposition CanvasBalanced ScorecardBusiness Model CanvasSWOT AnalysisGrowth HackingBundlingUnbundlingBootstrappingVenture CapitalPorter’s Five ForcesPorter’s Generic StrategiesPorter’s Five ForcesPESTEL AnalysisSWOTPorter’s Diamond ModelAnsoffTechnology Adoption CurveTOWSSOARBalanced ScorecardOKRAgile MethodologyValue PropositionVTDF FrameworkBCG MatrixGE McKinsey MatrixKotter’s 8-Step Change Model.

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