Deterministic Analysis vs Probabilistic Analysis in Projects

Regardless of their size, companies grow through investment projects—whether they are small local businesses or multinational oil giants. Naturally, larger projects are more complex, require greater investments to get off the ground, and demand more critical and strategic decision-making. It's important to highlight that projects involve decision-making at every stage, from planning to execution. In this article, we will focus on decisions made during the planning phase.

Imagine your company is a major producer of processed foods. As part of an expansion plan, you aim to build a new factory in a state where you currently do not operate. The goal is to penetrate a new market, gain market share by offering lower prices than local competitors, and deliver higher-quality products. You have extensive industry experience, a robust business plan, and an exceptional team. Let’s assume the project will be executed flawlessly, and while challenges may arise during execution, your team will address them swiftly and cost-effectively.

Your financial projections have been meticulously analyzed, your financing comes with an appropriate cost of capital, and the project is expected to recoup its investment within five years—more than enough to be considered attractive based on your industry’s benchmarks. So, what could go wrong? It seems obvious that you should invest in this project, right? Well, let’s take a closer look. A simple analysis reveals that your ability to adjust prices in response to increased costs will be limited, as your strategy relies on offering a superior yet more affordable product to capture market share.

However, many of your input costs—primarily commodities—are highly volatile. This volatility introduces significant uncertainty into your cash flow, directly affecting the project's returns. Additionally, depending on your company’s size, the cost of capital might further expose the project to unfavorable scenarios. As a seasoned entrepreneur, you understand that adverse scenarios are part of the game. You likely didn’t base your business plan on a single scenario but instead developed a range of reference scenarios—commonly referred to as deterministic scenarios. For these, you probably assessed financial performance and concluded that even under more adverse conditions, the project would remain sustainable, albeit with narrower margins and potentially lower returns than the base case.

But here’s the key question: based on historical data and the current market context, how likely is it that an adverse scenario—equally bad or worse than the one you modeled—will occur? Is your “pessimistic” scenario truly pessimistic, or could it be underestimating the risks? Are your decisions being influenced by cognitive biases, perhaps shaped by past successes in other projects? This is where Probabilistic Analysis steps in, providing a more objective and less biased framework for decision-making. The goal of Probabilistic Analysis is to generate a wide array of possible scenarios, each with a probability distribution that aligns with historical data and current market conditions. This approach captures both optimistic and pessimistic outcomes, while ensuring the probabilities remain consistent with reality. For example, if a sharp spike in input prices occurred once in the past and lasted only briefly, such a scenario would be included in the analysis—but it would represent only a small fraction of the thousands of scenarios modeled. The same logic applies to highly optimistic scenarios.

The future is inherently unpredictable on a deterministic basis. Thus, Probabilistic Analysis does not aim to forecast exact prices or other variables that influence project performance. Instead, it seeks to provide the best possible estimates of the probability distributions for these variables, offering a clearer picture of the financial risks and opportunities. Because Probabilistic Analysis relies on rigorous quantitative methods and is often validated through backtesting with historical data, it minimizes biases. It provides reliable estimates of project performance. For instance, if your project has a Minimum Acceptable Rate of Return (MARR) of 15%, the analysis might reveal a 40% likelihood that the project will underperform this threshold. This insight could prompt you to reassess the project—perhaps by reducing capital expenditure (CAPEX), restructuring financing terms, or exploring other risk mitigation measures. Can projects succeed without such analysis? Absolutely. Favorable input prices or lower-than-expected debt costs could lead to a successful outcome. However, with robust planning and the application of Probabilistic Analysis, your odds of success increase significantly. By embracing this approach, you can make more informed, data-driven decisions that enhance the resilience and profitability of your investments.