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A Review on Demand Forecasting and Predictive Analytics in FMCG Sector

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  • Vaibhavi Parmar
  • Sheshang Degadwala

Abstract

Demand forecasting is a fundamental component in the Fast-Moving Consumer Goods (FMCG) sector, as it directly impacts inventory management, supply chain efficiency, and profitability. This paper presents a detailed review of multiple research studies focusing on statistical and machine learning approaches for demand forecasting. Traditional methods such as ARIMA and SARIMA are compared with advanced techniques including Multiple Linear Regression, Random Forest, XGBoost, Neural Networks, and Big Data analytics. The study highlights how predictive analytics improves forecasting accuracy by identifying patterns in historical data and adapting to market dynamics. Furthermore, this paper discusses challenges, limitations, and future research opportunities in developing robust forecasting models.

Suggested Citation

  • Vaibhavi Parmar & Sheshang Degadwala, 2026. "A Review on Demand Forecasting and Predictive Analytics in FMCG Sector," International Journal of Scientific Research in Artificial Intelligence and Machine Learning, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 2(4), pages 08-15, July.
    • Handle: RePEc:jbo:ijsrml:v2:y2026:i4:id:88
    DOI: 10.32628/IJSRAIML262412
    Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML262412
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