Globalizing Food Items Based on Ingredient Consumption

Yukthakiran Matla (), Rohith Rao Yannamaneni and George Pappas ()
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Yukthakiran Matla: Department of Electrical and Computer Engineering, Lawrence Technological University, 21000 W 10 Mile Rd, Southfield, MI 48075, USA
Rohith Rao Yannamaneni: Department of Electrical and Computer Engineering, Lawrence Technological University, 21000 W 10 Mile Rd, Southfield, MI 48075, USA
George Pappas: Department of Electrical and Computer Engineering, Lawrence Technological University, 21000 W 10 Mile Rd, Southfield, MI 48075, USA

Sustainability, 2024, vol. 16, issue 17, 1-22

Abstract: The food and beverage industry significantly impacts the global economy, subject to various influential factors. This study aims to develop an AI-powered model to enhance the understanding of regional food and beverage sales dynamics with a primary goal of globalizing food items based on ingredient consumption metrics. Methodologically, this research employs Long-Short Term Memory (LSTM) architecture RNN to create a framework to predict food item performance using historical time series data. The model’s hyperparameters are optimized using genetic algorithm (GA), resulting in higher accuracy and a more flexible model suitable for growing and real-time data. Data preprocessing involves comprehensive analysis, cleansing, and feature engineering, including the use of gradient boosting models with K-fold cross-validation for revenue prediction. Historical sales data from 1995 to 2014, sourced from Kaggle open-source database, are prepared to capture temporal dependencies using sliding window techniques, making it suitable for LSTM model input. Evaluation metrics reveal the hybrid LSTM-GA model’s efficacy, outperforming baseline LSTM with an MSE reduction from 0.045 to 0.029. Ultimately, this research underscores the development of a model that harnesses historical sales data and sophisticated machine learning techniques to forecast food item sales growth, empowering informed investment decisions and strategic expansions in the global food market.

Keywords: food and beverage industry; ML framework; long-short term memory; genetic algorithm; ingredient consumption pattern; time series analysis; hyperparameter tuning; K-fold cross-validation; gradient boosting; global market (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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