Case Study: Economic Feasibility Analysis for Investment in an Industrial Plant

This project was developed throughout my career. Its objective was to conduct a feasibility analysis for the implementation of an industrial plant for an agricultural pesticide (commodity product).
The client — a supplier of agricultural inputs focused on a single crop — sought to reduce its dependency on a single market by locally producing a widely used product for other crops.

Market Access Challenge

Technically, manufacturing the product was straightforward. The real challenge lay in monetization, within a market characterized by low margins and high competitiveness.

To address this, we developed four market access models, all utilizing the same industrial plant:

• Direct Sales: own team and logistics, but high commercial costs.

• Small Distributors: outsourced capillarity, but lower margins.

• Large Distributors: outsourced capillarity, even lower margins, and the need for large volumes, but requiring a smaller commercial structure.

• Mix of the previous options: greater diversification, but with increased operational complexity.

Market Analysis & Associated Risks

We conducted interviews with experts, potential clients, and distributors, in addition to analyzing secondary data. We mapped critical risks:

• Price pegged to the US dollar: high volatility.

• Intense competition: many players bundling the product with other solutions.

• Challenging logistics: a fragmented market within a geographically large country.

• Substitutable product: well-established alternatives available in the market.

• Low margins: limited ability to absorb commercial and administrative costs.

Two-Layer Financial Methodology

To determine feasibility, it was necessary to project the income statement (P&L) over the next five years for each business model.
The greatest challenge was to understand the company’s ability to capture market share in each scenario. To this end, we mapped the market composition in terms of volume consumed, client access channels, and key players.

1. Discounted Cash Flow (DCF)

Five-year P&L Projection
For each business model, we projected:

• Gross Revenue = Installed Capacity × Utilization Rate × Selling Price (USD)
• Net Revenue = Gross Revenue – Taxes
• Gross Profit = Net Revenue – Direct Costs (raw materials, utilities, maintenance)
• Net Profit = Gross Profit – Administrative and Commercial Expenses
• Initial Investment = Plant CapEx + Commercial CapEx (variable by model)

We applied a Discounted Cash Flow (DCF) analysis to calculate:

• Net Present Value (NPV)
• Internal Rate of Return (IRR)
• Discounted Payback Period
• Operating Margin

The DCF analysis provided a baseline scenario. However, due to the high level of uncertainty, we also employed a probabilistic methodology to better capture risks.

While the DCF offered an initial view of feasibility, it is based on fixed assumptions, which can be limiting in highly volatile markets such as this one.
To properly capture the impact of uncertainties — especially regarding price, exchange rate, costs, and utilization rates — we added a second analytical layer: Monte Carlo Simulation.

2. Monte Carlo Simulation

Monte Carlo is a statistical method that simulates the financial model thousands of times, randomly drawing values for each assumption within predefined ranges, in order to reveal the distribution of possible outcomes.

How it was applied:

• We assigned normal or triangular distributions to price, exchange rate, costs, and utilization rates (±20% around the baseline scenario).
• We ran 1,000 iterations, with each round recalculating the P&L, NPV, IRR, and payback.
• We obtained probability curves indicating the likelihood of models resulting in a negative NPV — that is, situations where the investment would not generate returns.

This technique allowed us to capture uncertainties related to price and exchange rate fluctuations, assess the likelihood of worst-case scenarios, and ensure that decision-making would be based on the highest level of certainty possible — not merely on a single baseline scenario.

Results and Insights

In highly complex projects, making decisions based on solid data is what distinguishes real opportunities from disguised risks.

The integrated analysis — combining financial projections with Monte Carlo simulation — revealed that, in most scenarios, the investment would deliver unsatisfactory returns.
We were able to anticipate significant risks and prevent capital allocation into a market with unsustainable margins.

More than simply presenting figures, we delivered confidence for long-term strategic decision-making — a practical example of how an analytical approach can tangibly impact business strategy.