Machine learning techniques in supply chain management
ABSTARCT :
In recent years, supply chain management has witnessed a transformative shift driven by the adoption of advanced technologies, among which machine learning (ML) stands out as a revolutionary tool. Machine learning, a subset of artificial intelligence, enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. The complex and dynamic nature of supply chains, which involve numerous processes such as procurement, production, inventory management, logistics, and demand forecasting, makes them ideal candidates for the application of machine learning techniques.
By leveraging large volumes of data generated at various stages, machine learning models can provide valuable insights that enhance decision-making, improve efficiency, and reduce costs. Traditional supply chain approaches often relied on historical data analysis and human intuition, which are limited in their ability to handle variability and uncertainty. Machine learning addresses these limitations by offering predictive analytics, anomaly detection, and optimization capabilities that enable proactive management of supply chain activities. For instance, ML algorithms can accurately forecast customer demand by analyzing patterns from past sales, market trends, and external factors, thus reducing the risks of stockouts and overstocking.
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