Search

Featured Application: Monte Carlo Simulations can be used to forecast cost risks in agribusinesses, supporting better decision-making. Such risks should be minimized to optimize production costs, namely, materials, labor, and overhead costs, which can vary significantly and differently due to different cost behaviors. Forecasting costs using Monte Carlo simulations can provide a clear view of the expected cost range for the subsequent periods, enabling farmers to make better strategic planning and resource allocation decisions. Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman's demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops. [ABSTRACT FROM AUTHOR]