Blockchain-Enabled Predictive Analytics for Real-Time Supply Chain Decision Making

Abstract

Modern supply chains are still troubled by several problems, including scattered data, a lack of transparency, and inefficient, slow, and error-prone decision-making processes. These problems often result in inaccurate forecasts, late detection of supply chain interruptions, and low resilience of the entire operation. This paper proposes a Blockchain–Machine Learning Integrated Framework that will create a secure, transparent, and analytical supply chain environment. The blockchain is expected to provide a permissioned distributed ledger that will allow the use of efficient consensus techniques, thus providing prompt event finalization and secure data sharing among the parties involved. Based on reliable data from the blockchain, the machine learning layer combines forecasting models, anomaly-detection systems, and real-time data streams to continuously improve predictive accuracy and enable timely decision-making. Certainly, a medium-sized retail supply chain scenario comprising 350,000 events across various points is used as a testing ground for evaluating the proposed framework. The experimental results indicate that the system performance improvements are quite significant: decision latency is reduced, forecast precision is increased, and anomaly detection is more accurate than the traditional and ML-only baselines. Furthermore, the system maintains high throughput as the number of nodes increases, demonstrating its applicability to real-world scenarios. The initial rollout and trial illustrate significant potential across diverse sectors such as manufacturing, pharmaceuticals, agriculture, and logistics by enabling monitoring, reducing data-processing risks, and supporting automation. In this way, the discoveries point out that the confluence of blockchain and machine learning fortifies the formation of forthcoming supply chain systems that will be robust, data-driven, and able to run autonomously.