This study examines forecasts of fed beef production through a by-parts estimation framework to provide possibly more practical alternatives to relatively complex systems of equations methods. Recent shifts in cattle production, including elevated contributions of heifers in the slaughter mix and larger weights of fed cattle, provide an appropriate case study that underscores the need to revisit past events in the development of new forecasting strategies. By analyzing survey history in the data selection process, examining analog time periods, and considering the potential of serial correlation within Deterministic Trend / Deterministic Seasonality models, this study highlights that practical enhancements to forecast accuracy can be sustained by simple procedures. The results of this study demonstrate that (1) historical data selection can significantly impact forecast quality; (2) correcting autocorrelated errors can improve model performance; (3) the effectiveness of different forecast estimation methods varies by the selected horizon; and (4) the use of more simplistic assumptions underlying a forecast model can produce competitive and accurate results.