This paper examines how big data is utilized to estimate and predict price fluctuations in major metal markets and how this information can be leveraged to enhance purchasing strategies. The rapid advancement in technology has increased transparency and reduced information asymmetry, giving buyers unprecedented insights into market conditions. The paper distinguishes between cost reduction and cost avoidance, with a focus on the latter. Cost avoidance, although difficult to measure, involves proactive efforts to pay less than the expected price. The paper will define key factors influencing metal prices, explore how these can be tracked through big data, and assess how this data can improve price risk management. Finally, the paper critically reviews current research, identifying limitations and future research directions to provide a comprehensive understanding of the topic.