Estimativa do potencial de vendas de peças: estudo de caso em uma empresa de máquinas agrícolas

Abstract

This case study aims to analyze the method used by an agricultural machinery company to estimate the sales potential of replacement parts, identifying its critical points and recommending improvements to enhance sales planning in the after-sales area. The research includes a literature review on demand forecasting and potential estimation models, followed by the analysis and practical application of the method adopted by the company according to the literature, using regressions calculated in Python, whose results were examined within Microsoft’s Business Intelligence environment, Power BI. The findings of this study indicate that, although the method employed by the company is consistent with the theoretical behavior of progressive machinery wear and the consequent increase in replacement part expenses, significant variations were identified that prevent its direct application in the Brazilian agricultural after-sales context. These observations highlight the need for methodological refinements to reflect the real dynamics of customer fleets, especially in high-intensity usage scenarios such as sugarcane cultivation. Based on these results, the study proposes several improvements, including parameter adjustments, data integration through CRM, and the gradual adoption of predictive techniques aligned with Agriculture 4.0. Therefore, it is concluded that the analyzed method serves as an adequate initial reference, but its evolution is essential to strengthen estimation accuracy and support strategic decision-making in after-sales planning for Brazilian companies.