Core Insights - DXC Technology has partnered with ISA Vías to implement a Digital Twin platform aimed at enhancing road safety on the Ruta del Maipo in Chile, which serves over 7 million vehicles monthly [1][3][4] Group 1: Collaboration and Technology Implementation - The Digital Twin technology creates a real-time virtual model of the roadway, allowing ISA Vías to simulate various emergency scenarios without disrupting traffic [3][4] - This collaboration is seen as a significant advancement in modernizing Chile's infrastructure, improving safety protocols, and optimizing traffic management [4][5] Group 2: Benefits and Future Plans - The Digital Twin platform provides real-time insights for better traffic flow, predictive maintenance, and data-driven decision-making, ultimately enhancing operational efficiency and safety [4][5] - Plans for expansion include additional highways and tunnels, setting a new standard for critical infrastructure protection in Latin America [4][5] Group 3: Company Overview - DXC Technology specializes in helping global companies modernize IT and optimize data architectures while ensuring security and scalability across various cloud environments [6][7] - The company operates with a workforce of over 120,000 professionals across more than 70 countries, delivering innovative solutions that reshape industries [5][6]

Investment Rating - The report assigns an "In-Line" investment rating to the Capital Goods sector in Europe [4]. Core Insights - The report emphasizes that early adopters of AI with pricing power in the Capital Goods sector are likely to capture significant benefits, particularly in margin expansion [3][7]. - Six key use cases for AI adoption are identified, which include enhancing sales processes, utilizing AI chatbots, predictive maintenance, product design and testing, inventory management, and energy management [7][32]. Summary by Sections Investment Rating - The Capital Goods sector is rated "In-Line" [4]. AI Adoption Overview - AI adoption in the Capital Goods sector has seen a notable increase compared to the previous year, with a focus on identifying companies that have made significant strides in AI integration [3][27]. - The report highlights that only 9% of global industrial companies classified as AI adopters possess high pricing power, indicating a substantial opportunity for early adopters in the sector [29]. Key Use Cases - **Sales Processes**: AI is used to automate customer quotes and enhance sales strategies, with Rexel implementing an automated request for quotes system [9][34]. - **AI Chatbots**: Both internal and external chatbots are deployed to improve efficiency in customer service and internal operations, with Schneider Electric utilizing chatbots for customer inquiries [10][41]. - **Predictive Maintenance**: Companies like KONE leverage AI to enhance maintenance processes, significantly reducing unscheduled call-outs and improving fault identification [11][52]. - **Product Design and Testing**: AI tools are integrated into R&D to streamline product development, as seen with KONE's use of generative AI for rapid prototyping [12][47]. - **Inventory Management**: AI is applied to optimize inventory levels and supply chain efficiency, with Rexel reporting increased sales through AI-assisted inventory management [13][53]. - **Energy Management**: AI technologies are utilized to forecast and optimize energy consumption, with Schneider Electric achieving significant energy savings through AI integration [14][55]. Company Highlights - **Rexel**: Recognized for its extensive AI use cases, including customer churn algorithms and automated pricing models, which have led to substantial revenue increases [15][34]. - **KONE**: Noted for its proactive application of AI in maintenance and service offerings, enhancing operational efficiency and customer satisfaction [18][52]. - **Schneider Electric**: Acknowledged for its strong focus on energy management and inventory optimization through AI, contributing to significant productivity savings [19][55].

随着气候变化加剧和城市快速发展，传统基础设施在抗风险能力和应急响应效率方面暴露出系统性短 板。 一是设计标准滞后，抗灾韧性不足。当前基础设施的设计标准主要基于历史灾害数据且标准偏低，难以 适应极端气候事件频发的新形势。例如，城市排水系统普遍只能抵御1至3年一遇的降雨标准，而原 本"百年一遇"的强降雨如今已逐渐常态化，城市内涝问题频发。 二是监测手段较为落后，预警效能不足。以公路桥梁为例，全国现有超百万座桥梁，其中40%服役超过 20年。然而，智能化监测覆盖率低，传统人工巡检难以及时发现结构损伤。 三是应急协同低效，响应机制僵化。目前极端灾害场景下的应急指挥仍主要依赖经验和静态预案，智能 化决策辅助模型和系统应用不足，面对突发灾害响应滞后。四是灾后重建标准偏低，欠缺系统韧性规 划。现有基础设施在大灾后恢复仍以"故障—抢修"为主，缺乏系统性协同恢复机制和"重建得更好"的全 生命周期韧性设计，难以有效提升基础设施长期抗风险能力。 近年来，新型城市基础设施建设加速推进，特别是人工智能技术的加速发展和逐步应用，正在重塑城市 防灾体系。数字化建设、智能设施与信息平台的深度融合，使城市基础设施运行数据的采集、分析与预 测能 ...