Modelo de previsão de faturamento no setor moveleiro: um estudo de caso

Abstract

This study aimed to apply and evaluate revenue forecasting methods in the furniture sector, using the Exponential Smoothing Method (ESM) and the ARIMA model, based on a time series of historical data from a furniture factory between January 2017 and November 2024. The research included in-sample and out-of-sample analyses to compare the models' performance in terms of fit and predictive ability, considering metrics such as MAPE, MAD, and adjusted R2. The ESM was identified as the most robust model for longer forecasting horizons, standing out for its ability to capture growth trends and seasonal patterns in the historical series. The projections indicated continued revenue growth with marked seasonality in the year's final months, aligned with the sector's characteristics. The analysis reinforces the relevance of forecasting as a strategic tool for financial planning, enabling companies to adjust operations, optimize resources, and make informed decisions in a competitive market. As a contribution, the study provides a practical application of statistical methods in the furniture sector.