As an important technique to hide disk I/O latency, prefetching has been widely studied, and dynamic adaptive prefetching techniques have been deployed in diverse storage environments. However, two issues are not well addressed by previous research: (1) how to handle the prefetching resource allocation between concurrent sequential access streams with different request rates, and (2) how to coordinate prefetching at multiple levels in the data access path. Interestingly, we found that these problems bear a strong resemblance to situations long studied in the field of supply chain management (SCM), used by retailers such as Wal-Mart. In this paper, we demonstrate how to perform the problem mapping and then apply SCM principles in practice, particularly from the branch of inventory theory, to improve data prefetching performance in storage systems. More specifically, we applied (1) two SCM policies to dynamically configure the sequential prefetching parameters, and (2) an SCM solution to correct the access pattern information distortion in multi-level prefetching. We implemented these SCM-based strategies in the Linux kernel prefetching algorithm and a multi-level storage simulator, and evaluated the performance with three types of work- loads. The results indicate that the SCM approaches are able to generate up to a 55.0% of performance improvement for a real-world server workload benchmark, and up to 33.3% for a combination of Linux I/O-intensive applications.