Leveraging Data Science for Demand Forecasting and Inventory Management: A Comprehensive Review

Both demand forecasting and inventory management are essential in supply chain management; however, traditional techniques face challenges in dealing with variability of the contemporary markets. In this comprehensive review article, the author discusses how data science has revolutionized these fields, using techniques such as ARIMA, LSTM networks, hybrid models and machine learning algorithms. The review highlights successful applications across industries, with 126% of businesses using ML-based techniques outperforming their competitors. However, challenges such as high computational demands and data integration complexities remain key areas for future research. Demand planning and management is improved through the use of predictive analytics because it includes historical data, external factors and real time information. Examples from various industries show the implementation of external factors like seasonality, macroeconomic factors, and disruptions into the forecasting process, thus enhancing flexibility and robustness. However, issues such as data quality, scalability, and interpretability still remain, and demand collaboration between fields and the use of novel techniques such as federated learning and quantum computing. In this paper, integrating the existing practices and the future trends, the authors offer a roadmap for researchers and practitioners to fully unlock the power of data science for supply chain transformation.