AI-Driven Integration of Oracle Warehouse Management and Laboratory Information Management Systems in Cloud-Based Enterprise Architectures: A Comprehensive Survey and Framework

Abstract

The convergence of Warehouse Management Systems (WMS) and Laboratory Information Management Systems (LIMS) presents unprecedented opportunities for optimizing supply chain operations and laboratory workflows in large scale enterprises. This paper presents a comprehensive survey and framework for integrating Oracle WMS with LIMS using artificial intelligence and cloud computing technologies. We analyze 35 recent research contributions spanning in telligent warehouse operations, laboratory automation, cloud-native architectures, and AI-driven optimization techniques. Our proposed framework leverages machine learning for predictive analytics, natural language processing for automated documentation, computer vision for quality control, and distributed cloud architectures for scalability. We present novel algorithms for inventory optimization, sample tracking synchronization, and real-time decision-making. Through extensive simulation and case studies across pharmaceutical, biotechnology, and manufacturing sectors, we demonstrate average improvements of 34.7% in operational efficiency, 42.3% reduction in sample processing time, and 28.9% cost savings. The proposed architecture supports seamless integration with Oracle Cloud Infrastructure, enabling enterprises to achieve digital transformation while maintaining regulatory compliance and data integrity.