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Globalizing Food Items Based on Ingredient Consumption

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yukthakiran Matla & Rohith Rao Yannamaneni & George Pappas, 2024. "Globalizing Food Items Based on Ingredient Consumption," Sustainability, MDPI, vol. 16(17), pages 1-22, August.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7524-:d:1467718
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    1. Nicholas J. Cox, 2009. "Speaking Stata: Creating and varying box plots," Stata Journal, StataCorp LP, vol. 9(3), pages 478-496, September.
    2. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    3. Karthik Sridhar & Ram Bezawada & Minakshi Trivedi, 2012. "Investigating the Drivers of Consumer Cross-Category Learning for New Products Using Multiple Data Sets," Marketing Science, INFORMS, vol. 31(4), pages 668-688, July.
    4. Richard R. Shaker & Joseph Aversa & Victoria Papp & Bryant M. Serre & Brian R. Mackay, 2020. "Showcasing Relationships between Neighborhood Design and Wellbeing Toronto Indicators," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    5. Sangiorgio, Matteo & Dercole, Fabio, 2020. "Robustness of LSTM neural networks for multi-step forecasting of chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Terry L. Kastens & Gary W. Brester, 1996. "Model Selection and Forecasting Ability of Theory-Constrained Food Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 301-312.
    7. Kühl, Niklas & Schemmer, Max & Goutier, Marc & Satzger, Gerhard, 2022. "Artificial intelligence and machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 135656, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    Full references (including those not matched with items on IDEAS)

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