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].

Group 1: Infrastructure Challenges - Traditional infrastructure is facing systemic shortcomings in risk resilience and emergency response efficiency due to outdated design standards and insufficient disaster resistance [1] - Current infrastructure design standards are primarily based on historical disaster data, which are inadequate for the increasing frequency of extreme weather events [1] - Monitoring methods are lagging, with low coverage of intelligent monitoring systems, making it difficult to detect structural damage in aging infrastructure [1] Group 2: Advancements in Urban Infrastructure - The acceleration of new urban infrastructure construction, particularly through the application of artificial intelligence, is reshaping urban disaster prevention systems [2] - The integration of digital construction, smart facilities, and information platforms significantly enhances data collection, analysis, and forecasting capabilities for urban infrastructure [2] - Technologies like digital twins are enabling simulation-based urban governance, allowing managers to optimize emergency response plans in virtual environments [2] Group 3: Disaster Management in Zhejiang - Zhejiang faces significant risks from typhoons and secondary disasters, necessitating advanced disaster management strategies [3] - The province has developed the first national resilience disaster prevention model, integrating comprehensive risk assessment and decision support systems [3] - This model can provide real-time forecasts of various risks and assist in decision-making for population relocation and resource allocation [3] Group 4: Resilience of the Power Grid - The extensive power grid system in Zhejiang is vulnerable to natural disasters, making it crucial to enhance its disaster resilience [4] - The State Grid Zhejiang Electric Power Company has established a comprehensive disaster resilience model that incorporates advanced technologies for risk forecasting and assessment [4] - This model enables dynamic risk predictions for power grid equipment during disasters and supports optimal decision-making across various stages of disaster management [4]