Refining Sugar Production Forecasts: Analyzing Forecasting Techniques for Operational Precision

Abstract

This study compares the efficacy of moving averages and double exponential smoothing (Holt's method) in predicting sugar production, using historical data. Results reveal that double exponential smoothing outperforms moving averages, offering more accurate forecasts, particularly in contexts requiring responsiveness to data trends. These findings hold significant implications for enhancing operational efficiency and readiness in the sugar industry, guiding maintenance scheduling and production target achievement. Highlights: Double exponential smoothing (Holt's method) surpasses moving averages in accuracy. Importance of forecasting in operational efficiency and maintenance scheduling. Applicability of forecasting techniques in the sugar industry context. Keywords: Forecasting, Moving Average, Sugar Production