Abstract: Labour is a critical factor in the manufacturing sector, particularly in agricultural processing industries, which constitute a major industrial base in Thailand. Accurate forecasting of labour demand at the factory level is therefore essential for effective production planning and operational continuity. This study aims to identify an appropriate predictive approach to estimate the number of workers required in individual factories using fundamental production-related factors as predictor variables. Three machine learning models—Decision Tree Regressor, Random Forest Regressor, and Gradient Boosting Regressor—were developed and compared. The dataset was divided into 70% for model training and 30% for testing on unseen data. Based on the results of the 10-fold cross-validation performed on the training dataset, pairwise comparisons using paired t-tests showed that the Random Forest Regressor (RFR) achieved significantly lower MAE and MAPE values than both the Decision Tree Regressor (DTR) and the Gradient Boosting Regressor (GBR) (p < 0.05). However, for RMSE, although RFR significantly outperformed DTR, the difference between RFR and GBR was not statistically significant (p > 0.05). Evaluation on the independent test dataset further demonstrated that RFR achieved the best predictive performance and strong generalisation ability, producing the lowest RMSE, MAE, and MAPE values of 98.0732, 34.2280, and 1.5493, respectively. Furthermore, pairwise comparisons using the Wilcoxon signed-rank test confirmed that the absolute prediction errors of RFR were significantly lower than those of both DTR and GBR at the 95% confidence level (p < 0.05). Overall, these findings provide statistical evidence that RFR consistently delivered superior predictive performance across both the cross-validation phase and the independent test dataset. The combination of high predictive accuracy, statistical superiority, and robust generalisation capability indicates that RFR is the most appropriate tree-based regression model for labour demand forecasting in agricultural processing factories.

Keywords: Aro-processing sector, Decision tree regressor, Gradient boosting regressor, Labour demand, Random forest regressor, Tree-Based Regression

Pages: 391-404