Big data-driven smart management: Innovative practices in experimental material management at Soochow University

This study investigates the transformative potential of big data technology in revolutionizing experimental materials management throughout its entire lifecycle in higher education institutions, using Soochow University’s innovative practices as a case study. The research systematically examines the limitations of conventional management approaches, including data fragmentation, lack of procurement transparency, and passive inventory control, and develops a comprehensive data-driven framework to overcome these challenges. By implementing this integrated system, the study demonstrates significant improvements in operational efficiency and cost reduction while establishing a reliable foundation for sustainable scientific research amid growing experimental complexities and increasing funding pressures.

This study employs a mixed-methods approach grounded in Soochow University’s experimental material management practices, combining case analysis, predictive modeling, and iterative process optimization. Leveraging big data analytics, the research integrates multi-source data from procurement records, IoT sensors, and laboratory monitoring systems to establish a unified management platform. Machine learning algorithms were applied to analyze historical procurement patterns and predict demand fluctuations, while API-based interoperability protocols enabled seamless integration with external supplier systems. The PDCA (Plan-Do-Check-Act) cycle was rigorously implemented to ensure data quality and refine operational workflows, with real-time feedback from stakeholders informing system improvements. Cross-disciplinary collaboration between laboratory managers, data scientists, and supply chain experts further enhanced model accuracy and adaptability. Additionally, blockchain frameworks were pilot-tested to validate data immutability and traceability across procurement stages. These methodologies collectively supported the transition from fragmented management to a holistic, data-driven ecosystem.

The implementation of big data-driven strategies at Soochow University has yielded significant improvements. The intelligent procurement platform streamlined workflows, drastically reducing administrative burdens in financial reimbursement while achieving widespread adoption across departments. By integrating IoT sensors, real-time monitoring of material consumption and environmental conditions enabled adaptive inventory adjustments, enhancing procurement efficiency and responsiveness. The adoption of a “zero-inventory” model, supported by predictive analytics and seamless supplier collaboration, eliminated traditional warehousing costs while ensuring uninterrupted material availability. AI-powered anomaly detection modules improved supply chain resilience by proactively identifying demand fluctuations and operational risks, enabling dynamic resource allocation. Blockchain technology further strengthened traceability and compliance across procurement cycles, fostering transparency in auditing processes. Additionally, cross-system interoperability between procurement, safety protocols, and financial platforms minimized operational redundancies and elevated risk management capabilities. Collectively, these advancements established a robust, data-driven framework that optimized resource utilization, reduced operational costs, and strengthened support for high-impact scientific research.

The study concludes that the critical role of big in revolutionizing university experimental material management, transitioning from fragmented practices to an interconnected, proactive ecosystem. Soochow University’s “Internet Plus” model demonstrates how integrating IoT, AI, and blockchain can optimize procurement precision, reduce costs, and align with sustainability goals. Key achievements include the unification of siloed systems into a centralized platform, predictive analytics for demand-driven supply chains, and blockchain-enabled accountability. Future directions should prioritize scaling AI models for real-time adaptive decision-making, expanding blockchain applications to cross-institutional collaborations, and embedding circular economy principles (e.g., material recycling) into procurement frameworks. Additionally, fostering partnerships with tech providers and policymakers will be vital to address challenges like data security and ethical AI use. Ultimately, the research underscores the importance of tailoring big data management systems to the unique characteristics and needs of each university to ensure effective experimental material management and support high-quality development in higher education institutions.