Machine Learning in Supply Chain Management. A Systematic Literature Review between 2020-2024

Zavala Raul Bernardino P.; Castro Jose Fernando C.; Velasquez Julio Winston T.; Mendoza Milagros Isabel R. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/laccei2025.1.1.891

Resumen

The present systematic research aims to analyze the influence of Machine Learning applications in supply chain management, focusing on studies published between 2020 and 2024. Thirty relevant researches were identified that met the established inclusion criteria. The results show that Machine Learning significantly improves demand forecasting, inventory management and logistics optimization, allowing to reduce operating costs and increase organizational efficiency. In addition, outstanding applications such as Long and Short Term Memory (LSTM) neural networks for accurate predictions and Support Vector Machines (SVM) for complex classifications were identified. However, challenges remain, such as data quality, integration with traditional systems, and the need for professionals trained in this technology. Despite these limitations, the pharmaceutical, manufacturing and food sectors have demonstrated a positive impact by implementing Machine Learning-based solutions, optimizing processes and increasing their competitiveness. In conclusion, Machine Learning is consolidating as a key driver to transform supply chain management, although more investment in training and integration strategies is required to maximize its potential.

Instituciones: Universidad Tecnológica del Perú