A Systematic Review of Time Series and Machine Learning Techniques for Demand Forecasting and Inventory Management in Retail Supply Chains

Authors

  • Paril Ghori  

Keywords:

Supply Chain Management, Machine Learning, Data Mining, Demand Forecasting, Inventory Management, Stockouts.

Abstract

The integration of machine learning (ML) and advanced data analytics in demand forecasting and inventory management has revolutionized supply chain operations. This review systematically evaluates the application of statistical, time series, and regression methods, along with machine learning algorithms like neural networks and decision trees, in optimizing forecasting accuracy and inventory levels. The analysis highlights significant benefits, including reduced stockouts, enhanced customer satisfaction, and cost optimization. However, it also identifies persistent challenges such as data quality, the need for skilled personnel, and computational demands. This study emphasizes the importance of combining traditional and modern approaches to address these limitations effectively. Key areas for future research include the adoption of hybrid models, real-time data integration, and advanced AI-driven decision-making tools to further improve supply chain performance.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-02-29

Issue

Volume 7, Issue 1, January-February-2020

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Paril Ghori "A Systematic Review of Time Series and Machine Learning Techniques for Demand Forecasting and Inventory Management in Retail Supply Chains" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 1, pp.360-369, January-February-2020.