A Review on Machine Learning Models for Supply Chain Management and Forecasting

Supply chain management is critically important for streamlining and optimizing business logistics. Supply chain forecasting plays a vital role in ensuring that businesses are able to meet customer demand while optimizing their inventory levels and reducing costs. This literature review examines the various forecasting methods used in supply chain management over the past decade, from 2010 to 2020. The review covers time-series forecasting, causal forecasting, judgmental forecasting, and collaborative forecasting. The review also explores the impact of new technologies such as artificial intelligence and machine learning on supply chain forecasting. The findings of this review can help businesses and researchers better understand the current state of supply chain forecasting and identify areas for further research.in the forefront for supply chain forecasting. This paper presents a review of latest evolutionary algorithms in the domain of supply chain forecasting with its salient features.