Operational Efficiency through Machine Learning and Optimization in Supply Chain Management

Jianke Zou; Huayu Zhao; Ningxin Li

doi:10.54097/9cb36341

Authors

Jianke Zou
Huayu Zhao
Ningxin Li

DOI:

https://doi.org/10.54097/9cb36341

Keywords:

Machine Learning; Supply Chain Management; Demand Forecasting; Data Optimization

Abstract

Supply chain management faces many challenges and opportunities in increasingly fierce global competition. The surge in data volume and the decline in data quality brought about by the era of big data has prompted enterprises to introduce advanced machine learning techniques to optimise supply chain management. This paper explores the application of machine learning in sensing supply risk, demand forecasting, inventory optimisation, and distribution and transportation planning to improve the ability of enterprises to predict and respond to market changes by automatically identifying and extracting patterns in data, thereby improving operational efficiency and customer satisfaction. While there are challenges such as data quality and availability when implementing machine learning solutions, these issues can be addressed with proper planning and deployment, and enterprises can stay ahead and grow sustainably in a competitive market.

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References

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