Forecasting Food Demand in Supply Chains: a Comprehensive Comparison of Regression Models and Deep Learning Approaches

EasyChair Preprint 15558

Abstract

Effective forecasting and modeling in food demand supply chains are critical to minimizing waste, reducing costs, and ensuring product availability. This paper explores a comprehensive approach to forecasting food demand by leveraging regression-based models for analysis. We investigate how various machine learning regressors can predict food demand more accurately by examining key supply chain factors such as seasonal trends, price fluctuations, and consumer behavior. The study implements and compares multiple regressors to assess their performance in predicting demand. Metrics Evaluation is done by predicting various models which are Ensemble Learning Models and Neural Network Models to calculate the model’s accuracy. By doing prediction, we identified that Gradient Boosting and XGBoost have overall good accuracy in forecasting and it has provided optimized solutions in the supply food chain. This research mainly focuses on using the best modeling techniques which will help the end users to make proper decisions and bring efficiency in food demand management.

Keyphrases: Consumer Behavior Analysis, data-driven decision making, deep learning, predictive modeling

@booklet{EasyChair:15558,
  author    = {Shilpa Katikar and Vikas Maral and Nagaraju Bogiri and Vilas Ghonge and Pawan Malik and Suyash Karkhele},
  title     = {Forecasting Food Demand in Supply Chains: a Comprehensive Comparison of Regression Models and Deep Learning Approaches},
  howpublished = {EasyChair Preprint 15558},
  year      = {EasyChair, 2024}}