Predictive Modelling of Food Demand: Harnessing Machine Learning for Analysis and Insights
Author: T. S Ravi Kiran, A. Sri Nagesh, G. Samrat Krishna
Pages: 58-66
DOI:: https://doi.org/10.55306/CJDTES.2024.1201
Abstract:

Accurate demand forecasting in the food industry is essential for optimizing supply chain efficiency, reducing waste, and ensuring reliable product availability. Traditional forecasting methods often fail to capture the intricate relationships between consumer behaviour and external factors that influence demand fluctuations. This study explores the use of advanced machine learning techniques to improve the accuracy of food demand forecasting. We conduct a comparative analysis of several machine learning algorithms, including time series models and regression-based approaches, applied to historical sales data enhanced with contextual variables. Our results demonstrate the superior forecasting performance of machine learning models. In particular, Long Short-Term Memory (LSTM) networks effectively capture long-term temporal dependencies, while Gradient Boosting Regressors excel in modelling complex, nonlinear relationships within the data. Both models are evaluated using key performance metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings underscore the potential of machine learning to generate more accurate demand forecasts, providing valuable insights into demand patterns and key factors driving variability.
Key Words: Supply chain optimization, Demand forecasting, Food industry, Machine learning, Time series models, Regression-based approaches, Long Short-Term Memory (LSTM), Gradient Boosting Regressors, Performance metrics, Accuracy, Consumer behavior, Temporal dependencies, Nonlinear relationships, Contextual variables, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
Citation: T. S Ravi Kiran, et.al., “Predictive Modelling of Food Demand: Harnessing Machine Learning for Analysis and Insights", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 58-66, 2024.