AI-Powered Predictive Analytics for Retail Supply Chain Optimization:  Enhancing Forecast Accuracy and Reducing Operational Inefficiencies

Manthan Lad,Lukesh Singla

AI-Powered Predictive Analytics for Retail Supply Chain Optimization: Enhancing Forecast Accuracy and Reducing Operational Inefficiencies

Authors

Manthan Lad,Lukesh Singla

Keywords:

Artificial Intelligence, Predictive Analytics, Supply Chain Optimization, Machine Learning, Demand Forecasting, Gradient Boosting, Retail Operations

Abstract

For AI to be implemented effectively in supply chains, three elements are required:sound data infrastructure, organizational alignment, and ongoing model maintenance. Thefindings clearly illustrate differentiated, quantifiable performance improvements and best practices for embedding AI into retail operations

References

Tian, J., Garcia, R. J., Danford, F., Patrizi, L., Galasso, J., & Loyd, J. (2020). Big data actionable intelligence architecture. Journal Of Big Data, 7(1).

https://doi.org/10.1186/s40537-020-00378-7

Downloads

Published

2023-06-20

How to Cite

Manthan Lad,Lukesh Singla. (2023). AI-Powered Predictive Analytics for Retail Supply Chain Optimization: Enhancing Forecast Accuracy and Reducing Operational Inefficiencies . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2383–2391. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4593