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“智慧兰台”:开启会计档案管理数字化新时代
Xin Hua Ri Bao· 2025-11-13 21:26
Core Viewpoint - The digital transformation of financial management in enterprises is essential for high-quality development, with innovative management of accounting archives being crucial [1] Group 1: Strategic Planning and Implementation - The "Smart Lantai" initiative is guided by strategic leadership and a phased approach, focusing on centralized management of accounting archives and the launch of an electronic accounting archive system [2] - The period from 2023 to 2025 is designated for scaling and innovation, with a focus on centralized management and continuous optimization of business processes [2] - Future plans for 2026 and beyond include the construction of standardized smart archive facilities and the deployment of intelligent devices to enhance quality control and operational standardization [2] Group 2: Intelligent Process Transformation - "Smart Lantai" integrates information technology to transform the entire process of accounting archives, enhancing user experience through smart collection and delivery systems [3] - The intelligent storage and management processes utilize advanced technologies such as AI and robotics to significantly improve efficiency, achieving a threefold increase in operational speed [3] - A digital twin technology-based visualization platform is developed to provide comprehensive management and decision-making support [3] Group 3: Achievements and Industry Benchmarking - Since the implementation of centralized management in 2023, "Smart Lantai" has processed 1.51 million documents, with a peak daily processing capacity of 10,400 documents, and has increased its paperless rate from 60% to 74% [4] - The initiative has led to innovations in management, capability, technology, and user experience, optimizing the operational mechanism of archives [4] - "Smart Lantai" aims to set a benchmark in accounting archive management under the "Digital China" strategy, transitioning from temporary to permanent storage and enhancing the value extraction of data [4]