A Real-Time Analytics Architecture for Enterprise Order Lifecycle Visibility and Backlog Management
DOI:
https://doi.org/10.15662/IJRAI.2019.0206004Keywords:
Real-time analytics, enterprise order management, streaming data pipelines, change data capture, distributed systems, supply chain analytics, backlog management, ERP integration, NoSQL databases, event-driven architectureAbstract
Enterprise order management systems traditionally rely on batch-oriented data pipelines that introduce significant latency between transactional events and analytical insights. In large-scale global supply chains, delays in reporting order status, fulfillment availability, and backlog trends can hinder operational responsiveness and reduce customer satisfaction. This paper presents the design and implementation of a real-time analytics architecture that enables near-instant visibility into the order lifecycle across distributed enterprise systems. The proposed architecture integrates change data capture (CDC), distributed streaming pipelines, scalable NoSQL storage, and search-based indexing to deliver sub-second analytics for order tracking, backlog management, and fulfillment decision support. The system replaces legacy batch reporting processes and enables continuous synchronization between transactional enterprise resource planning (ERP) systems and analytical dashboards. Experimental results demonstrate a reduction in data latency from approximately 24 hours to sub-second availability, a reduction in report load time from several minutes to seconds, and improved scalability supporting large-scale operational analytics across global business units. The architecture has been deployed across multiple enterprise operational units and serves as a reference implementation for event-driven analytics in supply chain environments.
References
[1] M. Stonebraker et al., “The End of an Architectural Era: It’s Time for a Complete Rewrite,” Proceedings of the VLDB Endowment, vol. 7, no. 13, pp. 1150–1160, 2014.
[2] T. Akidau et al., Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing. Sebastopol, CA: O’Reilly Media, 2018.
[3] J. Kreps, N. Narkhede, and J. Rao, “Kafka: A Distributed Messaging System for Log Processing,” in Proc. 6th International Workshop on Networking Meets Databases (NetDB), Athens, Greece, Jun. 2011.
[4] P. Hunt, M. Konar, F. P. Junqueira, and B. Reed, “ZooKeeper: Wait-free Coordination for Internet-scale Systems,” in Proc. USENIX Annual Technical Conference (ATC), Boston, MA, Jun. 2010.
[5] A. Lamb et al., “The Vertica Analytic Database: C-Store 7 Years Later,” Proc. VLDB Endowment, vol. 5, no. 12, pp. 1790–1801, 2012.
[6] F. Hueske and V. Kalavri, Stream Processing with Apache Flink. Sebastopol, CA: O’Reilly Media, 2019.
[7] P. Carbone et al., “Apache Flink: Stream and Batch Processing in a Single Engine,” IEEE Data Engineering Bulletin, vol. 38, no. 4, pp. 28–38, 2015.
[8] G. DeCandia et al., “Dynamo: Amazon’s Highly Available Key-value Store,” in Proc. 21st ACM Symposium on Operating Systems Principles (SOSP), Stevenson, WA, Oct. 2007, pp. 205–220.
[9] S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google File System,” in Proc. 19th ACM Symposium on Operating Systems Principles (SOSP), Bolton Landing, NY, Oct. 2003, pp. 29–43.
[10] R. Cattell, “Scalable SQL and NoSQL Data Stores,” ACM SIGMOD Record, vol. 39, no. 4, pp. 12–27, 2011.
[11] D. Abadi, “Consistency Tradeoffs in Modern Distributed Database System Design,” IEEE Computer, vol. 45, no. 2, pp. 37–42, 2012.
[12] M. Zaharia et al., “Apache Spark: A Unified Engine for Big Data Processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.
