告别经验主义： 在人类算力的极限之外，重建供应链秩序。 在某个普通的早上，南宁某仓的调度大屏跳出来自八个区域仓的300多条配送请求—— 有医院的急救类补货、夜间药店的销量回补、B2B渠道的大批量集采订单，以及由季节性疾病高发触发的突发需求，也有必须关乎生命的、要在两小时送 达ICU的药品需求，每一条请求背后都对应着不同的时效要求、药品合规要求和路线约束。 另一边，仓内的拣货员已经开始处理医院端的大单：一个订单可能包含80种以上的品规，既有高价值药，也有一旦超温就要整批报废的冷链品类。这些药 必须从不同区域的货架拣取，经过核对批次、效期、规格，再以合规方式打包。如果流程中断或错拣，订单需要整单复核。 过去，调度员需要在路线、车型、载重和时效之间来回推演。一遇到跨仓调拨或需求波动——比如某区域突然感冒高发，导致当地仓库缺货——所有路线 都必须重新排。因此，一个人要处理上万SKU与各仓之间的动态关系，往往一排车就要一小时以上。 这是广西柳药集团的日常缩影。 这家区域龙头医药流通企业成立于上世纪50年代，是广西最早的国有医药公司之一。经过数十年的区域扩张与品类延展，柳药已从传统医药流通企业，成 长为覆盖医院、连锁药店 ...

您知道吗？企业每天产生的数据量超过2.5 万亿字节。在这个数据量和复杂性呈爆炸式增长的时代，传统数据库已无法满足企业对信息速度、 规模和多样性的需求。而数据湖的实施正是为了解决这个问题——它提供了一个统一且可扩展的基础架构，用于存储结构化、半结构化和非结 构化数据的原始数据。 数据湖是现代分析和人工智 能的基石，能够实现实时洞察、自助式商业智能和预测建模。在本 文 中，我们将探讨数据湖的定义、构建数据湖 对企业成功的重要性，以及如何有效地设计数据湖。您还将了解到最佳实践、需要避免的常见陷阱，以及领先企业如何将数据湖转化为创新和 竞争优势引擎的真实案例。 要点总结 一个完善的数据湖能够加快分析和人工智能工作负载的洞察速度，提高可扩展性和效率。 从一开始就注重治理、元数据管理和架构设计，以确保长期成功。 如今，企业需要管理来自传感器、应用程序、客户互动和第三方系统的海量数据。相应地，传统数据库往往难以扩展或有效处理如此多样化的 数据。部署数据湖则提供了一种灵活、经济高效且面向未来的数据存储和分析解决方案。 保持业务团队和 IT 团队之间的紧密协作，以推动数据采用、建立数据信任并实现持续价值。 将数据湖视为战略资产 ...

Core Insights - The People's Bank of China (PBOC) is actively implementing long-term rectification measures following the third round of inspections by the Communist Party, focusing on addressing deep-rooted and common issues in the financial sector [1][2] Group 1: Financial Infrastructure Development - The PBOC is accelerating the construction of technology-enabled monitoring and regulatory facilities, including the development of cybersecurity management systems and expanding the monitoring scope of the financial cybersecurity situation awareness platform [1] - There is a strong emphasis on enhancing treasury construction and management, ensuring the safe and stable operation of the treasury system, and advancing the national treasury project [1] - The PBOC is also focusing on strengthening the regulation and interconnectivity of financial market infrastructure, promoting the implementation of interbank and exchange market connectivity projects [1] Group 2: Information Technology Reform - The PBOC is advancing information technology reforms by establishing project management guidelines and actively promoting the integration of information systems across the organization [1] - There is a plan to enhance centralized management of data centers and to increase the usage rate of data lakes and central bank cloud services [1] Group 3: Financial Legislation Progress - The Anti-Money Laundering Law has been officially implemented, and significant progress has been made in revising the PBOC Law [2] - The PBOC is collaborating with the National People's Congress to advance the review of the Financial Stability Law draft and has achieved milestones in the revision of the Commercial Bank Law, Bill Law, and Foreign Exchange Management Regulations [2] Group 4: Future Work Plans - The PBOC plans to integrate inspection rectification into the implementation of the Central Committee's decisions, maintaining a stable and progressive work approach to foster a favorable monetary and financial environment for economic recovery [2] - There is a commitment to further solidify responsibilities for mitigating financial risks and to continue building a self-controlled, safe, and efficient financial infrastructure system [2]

Core Insights - 85% of big data projects fail, and despite a 20% growth in the $15.2 billion data lake market in 2023, most companies struggle to extract value from text data [2][25] - The reliance on general-purpose large language models (LLMs) like ChatGPT is costly and ineffective for structured data needs, with operational costs reaching $700,000 daily for ChatGPT [2][25] - Companies are investing heavily in similar LLMs without addressing specific industry needs, leading to inefficiencies and wasted resources [8][10] Data and Cost Analysis - ChatGPT incurs monthly operational costs of $3,000 to $15,000 for medium applications, with API costs for organizations processing over 100,000 queries reaching $3,000 to $7,000 [2][25] - 95% of the knowledge in ChatGPT is irrelevant to specific business contexts, leading to significant waste [4][25] - 87% of data science projects never reach production, highlighting the unreliability of current AI solutions [7][25] Industry-Specific Language Models - Business Language Models (BLMs) focus on industry-specific vocabulary and general business language, providing targeted solutions rather than generic models [12][25] - BLMs can effectively convert unstructured text into structured, queryable data, addressing the challenge of the 3.28 billion TB of data generated daily, of which 80-90% is unstructured [21][25] - Pre-built BLMs cover approximately 90% of business types, requiring minimal customization, often less than 1% of total vocabulary [24][25] Implementation Strategy - Companies should assess their current text analysis methods, as 54% struggle with data migration and 85% of big data projects fail [27][25] - Identifying industry-specific vocabulary needs is crucial, given that only 18% of companies utilize unstructured data effectively [27][25] - Organizations are encouraged to evaluate pre-built BLM options and leverage existing analytical tools to maximize current infrastructure investments [27][28]