Interpretable Monthly Decision Support for Sugarcane Mill Planning: One-Month-Ahead Cane Tonnage Forecasting and Operational-State Profiling in Coastal Ecuador

Monthly cane supply anticipation is critical for harvest scheduling, mill intake coordination, transport allocation, labor planning, and maintenance organization in vertically integrated sugarcane agroindustrial systems. This study developed an interpretable monthly decision support framework using twelve years of original institutional records from Compañía Azucarera Valdez S.A., Milagro, Guayas, coastal Ecuador, covering January 2007 to December 2018. The primary endpoint was one-month-ahead monthly cane tonnage, predicted from variables available at the current monthly time point to avoid look-ahead bias. Complementary diagnostic layers were used to characterize recurrent operational states and to interpret monthly energy-saving behavior. A chronological validation design was applied, with 2007–2015 used for model training and 2016–2018 used for independent testing. The full linear and LASSO models achieved the strongest test performance, with R2 values of 0.916 and 0.915 and mean absolute percentage errors near 5%, outperforming random forest and ANN/MLP benchmarks. The area-only baseline was weaker, while the no-area model retained predictive capacity, indicating that harvested area was important but insufficient to explain monthly cane tonnage. PCA and k-means clustering identified four recurrent operational states related to production scale, stress, crop quality, and energy performance conditions. Monthly energy saving showed ceiling-constrained behavior near 40% and was therefore interpreted as a diagnostic indicator rather than a robust forecasting target. Overall, the framework supports transparent monthly planning by combining leakage-aware forecasting, operational-state interpretation, and conservative energy performance diagnostics.