【埃森哲】2024生成式AI时代的供应链转型，化潜能为实效

Industry Investment Rating - The report highlights the transformative potential of generative AI in the supply chain, with 95% of executives believing it will force their companies to upgrade their technology architecture [2] Core Perspectives - Generative AI can optimize 43% of the working time across supply chain functions, with 29% through automation and 14% through enhanced employee capabilities [4] - 97% of executives agree that generative AI large language models (LLMs) will bring transformative changes to their organizations [4] - 58% of 122 supply chain processes can be reshaped by generative AI [15] Generative AI's Impact on Supply Chain Functions Design and Engineering - Generative AI accelerates design processes by leveraging historical data, reducing repetitive tasks, and creating new designs quickly [17] - In packaging design, generative AI can propose multiple design concepts and marketing suggestions, saving time and resources [17] - Terray Therapeutics uses generative AI to innovate small molecule drug discovery, optimizing molecular designs [17] Planning - Generative AI simplifies access to insights, enabling employees to query optimization suggestions in natural language and integrate unstructured data sources like market reports and social media [19] - It supports collaborative and streamlined workflows, such as summarizing meeting action points and generating draft plans [19] Sourcing and Procurement - Generative AI simplifies operations, bridges information gaps, and accelerates insights in sourcing and procurement [21] - Carrefour is developing a generative AI solution to streamline tasks like drafting tender documents and analyzing quotes [22] - Generative AI can automate contract generation, RFP drafting, and market intelligence summarization, enhancing upstream procurement activities [24] Manufacturing - Generative AI improves asset maintenance by interpreting complex documentation and generating logical steps for work orders [25] - It supports predictive maintenance, quality control, and compliance by integrating IT, operational, and engineering data [25] - In regulated industries like pharmaceuticals, generative AI monitors data sources for compliance violations and automates technical documentation [25] Fulfillment - Generative AI enhances customer experience by leveraging omnichannel data and optimizing transportation management [28] - It automates the generation of import/export documents, reducing errors and saving time [28] Service - Generative AI transforms customer support by analyzing vast amounts of unstructured data to provide personalized service experiences [30] - It predicts customer intent, summarizes calls, and generates action plans, allowing employees to focus on creative and empathetic interactions [38] Cross-Functional Value Creation Sustainability - Generative AI helps companies accelerate supply chain decarbonization by analyzing supplier data and improving ESG reporting [32] - It matches expenditure data with emissions factors, reducing manual effort from days to minutes [32] Resilience - Generative AI enhances supply chain resilience by analyzing unstructured data to understand N-tier supplier networks and identify risks [36] - It supports procurement teams in making data-driven decisions to mitigate risks [36] Customer-Centricity - Generative AI enables more customer-centric supply chains by analyzing unstructured customer feedback and integrating it into product design workflows [38] - It improves call center experiences by predicting customer intent and generating tailored responses [38] Workforce Transformation - Generative AI will impact over 50% of working hours in 7 out of 15 supply chain roles, including procurement managers and production planners [47] - It enables employees to focus on higher-value tasks by automating repetitive activities and enhancing decision-making [50] Strategic Recommendations - Companies must prepare their data, workforce, and organizational structures for generative AI adoption [9] - Generative AI should be integrated into a broader automation ecosystem, including traditional process automation and machine learning models [9] - A responsible AI framework is essential to address potential biases, security risks, and ensure trust in AI systems [45] - Collaboration with technology ecosystems and partners is critical to scaling generative AI solutions [54]