Optimizing Supply Chain Efficiency Using AI-Driven Predictive Analytics in Logistics
DOI:
https://doi.org/10.32628/CSEIT25112475Keywords:
Automated ETL, Real-Time Analytics, Supply Chain Optimization, Shipping Delays, Data Integration, AI- Driven Insights, Logistics AutomationAbstract
In modern supply chain management, shipping de- lays remain a significant issue, impacting customer satisfaction, operational effectiveness, and overall profitability. Traditional data processing methods don’t provide real-time information due to the latency in extracting, transforming, and loading (ETL) data from disparate sources. To alleviate this challenge, automated ETL processing combined with real-time data analytics offers an effective and scalable approach to minimizing shipping delays. This research explores the ways in which automated ETL workflows streamline shipping operations through the integration of real-time information from various sources, such as order management systems, GPS tracking, warehouse databases, and customer feedback platforms. The study recognizes the benefits of cloud-based ETL tools like Apache NiFi, Talend, and AWS Glue in automating data pipelines, reducing manual intervention, and improving data accuracy. Through real-time analytics with the help of tools such as Power BI, Apache Kafka, and Snowflake, businesses can monitor KPIs such as transit time, warehouse process efficiency, and last-mile delayed deliveries. The findings demonstrate that automated ETL processing reduces data la- tency, enhances supply chain visibility, and enables proactive decision-making. Real-time alerts generated through AI-based anomaly detection models also help logistics teams reduce poten- tial delays proactively before they become critical. Case studies conducted across e-commerce and third-party logistics providers (3PLs) demonstrate a 30Despite its advantages, challenges such as data integration complexity, security, and infrastructure costs must be addressed for seamless deployment. Hybrid ETL archi- tectures, including edge computing and blockchain, must be the subject of future research to further enhance real-time supply chain visibility. By embracing automated ETL and real-time analytics, businesses can significantly reduce shipping delays, improve logistics performance, and improve overall supply chain resilience in a world dominated by data.
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