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 ...

Core Viewpoint - The article highlights the transformation of the pharmaceutical supply chain through AI integration, emphasizing the shift from traditional experience-based methods to data-driven, intelligent decision-making systems [2][11][36]. Group 1: Company Overview - Liuyao Group, established in the 1950s, has evolved from a traditional pharmaceutical distributor to a comprehensive health service group, covering hospitals, retail pharmacies, and B2B clients [2]. - The complexity of Liuyao's supply chain is amplified by the need to manage over ten thousand SKUs, multiple warehouses, and stringent compliance and time constraints [2][4]. Group 2: Supply Chain Challenges - Liuyao faces a "triple constraint" in its supply chain, balancing time, compliance, and cost, where improving one aspect can exacerbate the others [4][5]. - The pharmaceutical industry is under pressure to enhance efficiency and reduce costs due to increasing regulatory demands and market competition [7][10]. Group 3: AI Integration and Transformation - Liuyao has partnered with Huawei Cloud to leverage AI for restructuring its supply chain decision-making processes, focusing on data governance, demand forecasting, and intelligent scheduling [2][11]. - The implementation of a data lake has unified fragmented data, enabling real-time visibility and optimization of supply chain operations [15][21]. Group 4: AI Tools and Their Impact - The Pangu predictive model has improved demand forecasting accuracy to over 89%, directly impacting inventory management and reducing stockout risks [16][21]. - The Tianchou AI solver optimizes complex decision-making scenarios, significantly reducing decision-making time and lowering costs by approximately 20% [21][20]. Group 5: Industry Trends and Future Directions - The article notes a global trend where over 50% of large multinational companies are expected to adopt AI and advanced analytics for supply chain management by 2027 [8]. - In China, over 60% of large enterprises are projected to implement AI and intelligent scheduling systems in their supply chains within the next three years, driven by national policies promoting digital transformation [10][11]. Group 6: Conclusion on Supply Chain Evolution - The shift from experience-based systems to computational systems in supply chains is seen as a critical evolution, enabling companies to predict demand, optimize resources, and enhance operational efficiency [26][36]. - Liuyao's experience serves as a model for the industry, demonstrating that intelligent supply chains can become a new growth engine rather than merely a cost center [36].

Core Viewpoint - The article discusses the advancements of Huawei Cloud's Pangu multimodal large model, highlighting its capabilities in generating 4D space images and videos from Mars images, and its unique ability to support both point cloud and video modalities simultaneously [1][7]. Group 1: Model Upgrades - Huawei Cloud has upgraded five foundational models, including Pangu NLP, multimodal, prediction, scientific computing, and CV models [8]. - The Pangu NLP model features two significant technologies: Pangu DeepDiver and a low hallucination new scheme, which enhance its capabilities [12][18]. Group 2: Pangu DeepDiver Technology - Pangu DeepDiver utilizes Search Intensity Scaling (SIS) to improve interaction between large language models (LLMs) and search engines, allowing dynamic adjustment of search frequency and depth based on problem complexity [13][14]. - The model has demonstrated performance comparable to a 671 billion parameter model in various benchmarks, indicating a qualitative leap in open-domain information retrieval capabilities [16][17]. Group 3: Low Hallucination New Scheme - The low hallucination scheme includes a multi-layered hallucination defense system and a closed-loop quality assurance system, focusing on data quality and diversity to reduce hallucination triggers [18][21]. - The model employs reinforcement learning to suppress hallucinations and enhance factual accuracy, logical consistency, and reliability [22][23]. Group 4: Industry Applications - The Pangu models have been applied in various industries, such as agriculture, where a model developed with the Chinese Academy of Agricultural Sciences can recommend gene editing targets, significantly reducing design time [28][34]. - The Pangu prediction model has been implemented in industries like cement and steel, providing process optimization solutions that enhance production efficiency [35][36]. Group 5: Model Development and Training - Huawei Cloud offers a comprehensive AI toolchain through its ModelArts Studio, facilitating the development of industry-specific models without the need for companies to start from scratch [42]. - The industry model training workflow reduces training time and costs by 60%, enabling clients to build high-quality proprietary models efficiently [45][46]. Group 6: Evaluation and Standards - Huawei Cloud has established an industry model evaluation center that provides a three-tier evaluation system across various sectors, helping clients optimize their models based on clear standards [47][48].