Flavor Forecast: Optimizing Potato Chips Production through Demand Forecasting Using Machine Learning Techniques

Vishnu Priya R., Jaianandakrishnaa K., Shigivahan A., Charan Vivek Raj R., Alagu Veera Siranjivee D.

2025

Abstract

In this paper, a comprehensive predictive analytics framework for demand forecasting of fast-moving food products is proposed with emphasis on potato chips across different flavours. The study uses a hybrid approach of SARIMAX (Seasonal Autoregressive Integrated Moving Average model extended to include exogenous factors) and Random Forest algorithms to model historical sales data and exogenous factors such as sports events, festivals, holidays, and promotional activities. The framework's architecture incorporates several components: seasonal pattern analysis for the time series, event study to analyse demand shocks and feature construction for improved model fit. These dynamic factors are incorporated to produce detailed demand forecasts and indicate the percentage change in demand trends. This approach to production planning is ahead of time to avoid overproduction, minimize waste and achieve proper inventory management throughout the supply chain. The implementation is a web application developed using Streamlit, with strong user authentication, data handling, and visualization features. The system architecture consists of data ingestion, preprocessing, feature selection, model training, and real-time prediction generation modules. The preprocessing pipeline contains data cleaning algorithms, temporal aggregation and outlier detection that are applied automatically to the data. The SARIMAX model was found to be more accurate in providing point forecasts of demand in real time with an accuracy of 91.47%. This is because it can incorporate both the seasonal components and other variables easily. The framework’s effectiveness was then established through a rigorous cross-validation procedure and the use of standard performance metrics such as MAE and RMSE.

Paper Citation

in Harvard Style

R. V., K. J., A. S., R. C. and D. A. (2025). Flavor Forecast: Optimizing Potato Chips Production through Demand Forecasting Using Machine Learning Techniques. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 676-683. DOI: 10.5220/0013941800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Vishnu R. and Jaianandakrishnaa K. and Shigivahan A. and Charan R. and Alagu D.},
title={Flavor Forecast: Optimizing Potato Chips Production through Demand Forecasting Using Machine Learning Techniques},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={676-683},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013941800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Flavor Forecast: Optimizing Potato Chips Production through Demand Forecasting Using Machine Learning Techniques
SN - 978-989-758-777-1
AU - R. V.
AU - K. J.
AU - A. S.
AU - R. C.
AU - D. A.
PY - 2025
SP - 676
EP - 683
DO - 10.5220/0013941800004919
PB - SciTePress