Short Communications / Technical Notes

Demand Forecasting in Supply Chain Planning: Methods, Challenges, and Strategic Implications

Maria Lopez

Received 17 Apr 2026, Published online: 31 Dec 2022

Published in Volume 2, Issue 2, 2022, pages 4-6

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Abstract

Demand forecasting plays a pivotal role in supply chain planning, impacting procurement, production, distribution, and customer satisfaction. Accurate forecasting helps companies reduce costs, optimize inventory, and improve responsiveness. This paper reviews major forecasting methods—including qualitative, time-series, causal, and machine learning approaches—evaluating their applications and accuracy across industries. The paper also addresses common challenges such as demand variability, data quality, and forecasting bias. By analyzing real-world applications and emerging technologies, this study provides practical insights and strategic recommendations for enhancing forecasting accuracy in dynamic supply chain environments.

Keywords

  • Demand Forecasting
  • Supply Chain Planning
  • Forecast Accuracy
  • Time-Series Models
  • Machine Learning
  • ERP

References

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Licensing

© 2022 Journal of Magnetism and Its Applications. This article is published under CC BY 4.0.

How to cite

Maria Lopez , M. L. (2022). Demand Forecasting in Supply Chain Planning: Methods, Challenges, and Strategic Implications. Journal of Magnetism and Its Applications, 2(2), 4-6.

How to Cite

Demand Forecasting in Supply Chain Planning: Methods, Challenges, and Strategic Implications. (2022). Journal of Magnetism and Its Applications, 2(2), 4-6. https://jourmag.org/index.php/jma/article/view/114