Enhancing Demand Forecasting in Food Manufacturing: Hierarchical Analysis of Aggregated and Individual Models

This study focuses on production planning in the food manufacturing sector using hierarchical forecasting. The selected case for the focal study represents food products with a common main ingredient used in manufacturing. We employ two scenarios: 1) forecasting aggregated total sales for all products, and 2) forecasting sales for each product separately to calculate the total requirement. We employed three statistical models: autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and Prophet, and five machine learning models such as linear regression (LR), k-nearest neighbors (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost). The key findings highlight that forecasting aggregated total sales for common ingredients outperformed the forecasting for each product individually and got the sum for the overall requirement. Further, we found that island-level forecasts are more accurate than district- and distribution-center-level forecasts. XGBoost performed as the best forecasting model, and MinT outperformed as the best reconciliation approach. Our study contributes to supply chain strategies when products have common ingredients in the manufacturing industry to optimize their resource allocation and production planning. This novel approach contributes to enhancing operational efficiency in food manufacturing.