Chairman Huang Youfang delivered a keynote speech on "2025 Development Trends of China's Port and Shipping Supply Chain Services" at the 2024 Annual Meeting of Modern Logistics News and the Forum on Development Trends of Supply Chain Going Global under the theme of "Following the Trend & Strengthening the Chain".

He emphasized that intelligent and transparent supply chains have become an important strategic direction for enterprises, and the application of technologies such as artificial intelligence can improve the speed and quality of decision-making.

He pointed out that port and shipping enterprises have attached great importance to technological development and actively participated in scientific and technological innovation.

The transformation from single transportation to integrated transportation, and then to logistics and supply chains, including the application of technologies such as blockchain, are all important manifestations of technology transforming port and shipping supply chains.

I. Economic Globalization and the Development of Chinese Manufacturing

• Core Assessment: China has emerged as a vital global manufacturing hub, possessing distinct competitive advantages in cost control, production efficiency, and market responsiveness. However, the international landscape is growing increasingly complex, characterized by escalating trade barriers and continuously rising thresholds for going global.

• Core Trend: The globalization of manufacturing is shifting from "Product Export" (selling products overseas) to "Capacity + Brand + Ecosystem" localization. This transition requires manufacturers to adapt to local market regulations, supply chain demands, and consumer habits, ultimately building a truly globalized operational system.

• Critical Reminder: To avoid inefficient expansion, enterprises expanding overseas must actively navigate and mitigate four major pain points: information asymmetry, compliance risks, cultural differences, and supply chain fragmentation.

II. Current Status and Future Directions of Chinese Manufacturing Layout

1. Characteristics of the Current Layout

• Geographical Distribution: Domestic operations remain concentrated in high-density manufacturing clusters like the Yangtze River Delta and Pearl River Delta. Overseas expansion is actively branching into Southeast Asia (leveraging labor cost advantages), Mexico (for nearshoring and tariff mitigation within the North American market), and Africa (for rich resources and emerging markets), effectively forging a robust "China + N" global production network.

• Layout Drivers:

  • Cost-Driven: Minimizing expenditure across labor, land, taxes, and manufacturing overheads.

  • Market-Driven: Positioning production closer to end consumers to bypass tariff barriers and mitigate geopolitical trade frictions.

  • Supply Chain-Driven: Optimizing global resource allocation to diversify risks and enhance overall supply chain resilience.

2. Future Directions for Global Layout

• High-End Evolution: Moving up the value chain by transitioning toward smart manufacturing, core technology R&D, and premium brand operations to capture higher added value.

• Diversification: Actively opening up and cultivating emerging markets (such as Southeast Asia, the Middle East, and Latin America) to reduce strategic dependence on traditional European and North American markets.

• Synergistic Integration: Fostering deep, strategic alignment with overseas suppliers, logistics providers, and distribution channels to engineer seamless, end-to-end global supply chains.

III. The Chinese Logistics Industry Supporting Manufacturing Growth

1. Core Capabilities of the Logistics Sector

• Foundational Capabilities: A comprehensive global network spanning maritime, air, rail, and multimodal transport that robustly satisfies the cross-border freight demands of manufacturing enterprises.

• Advanced Capabilities: Value-added services including overseas warehousing, bonded warehousing, supply chain finance, and digital trade solutions tailored to support the global footprint of manufacturers.

2. Pain Points in Logistics-Manufacturing Synergy

• Information Asymmetry: A severe lack of visibility across critical logistics nodes leads to opaque inventory and transit statuses, directly disrupting precise production planning and scheduling.

• Elevated Costs: Cross-border logistics involve too many intermediate handling stages and convoluted tax regulations, causing total logistics expenses to consume an unsustainably high percentage of revenue.

• Deficient Resilience: The capacity to absorb sudden shocks—such as geopolitical disruptions, volatile freight rate fluctuations, and severe port congestion—remains fundamentally weak.

3. Key Strategies for Logistics Upgrading

• Multimodal Transport Optimization: Promoting deep integration between factories and ports, alongside sea-rail and air-land intermodal transport. This enables a seamless "straight from the factory to the ocean" workflow, dramatically shortening overall delivery cycles.

• Global Logistics Network Expansion: Strategically deploying hub-based overseas warehouses and bonded logistics centers in key target markets to drastically boost localized final-mile delivery efficiency and emergency response times.

• Logistics Digitalization: Constructing unified global logistics information platforms to achieve end-to-end visibility across order fulfillment, inventory levels, and real-time transit tracking, driving unparalleled collaborative efficiency.

On October 19, 2025, Gu Jinshan, President of China Ports & Harbors Association, attended the 5th North Bund Forum on International Shipping and delivered a keynote speech entitled “Green Transformation Leads the Future: Exploring Sustainable Development Pathways for China’s Ports.”

Gu noted that in recent years, China’s port industry has actively explored pathways toward green and sustainable development and achieved significant progress.

First, policy planning and standards for green port development have gradually taken shape. In recent years, the Ministry of Transport has issued a series of policy documents, including the 14th Five-Year Plan for Green Transport Development, the Implementation Plan for Synergizing Pollution Reduction and Carbon Reduction, and the Guidelines for Green Port Rating and Evaluation, providing strong institutional support for the green development of ports.

Second, diversified application scenarios for clean energy at ports have steadily emerged. At present, shore power facilities cover more than 95% of container terminals at China’s major hub seaports. The use of clean and low-carbon equipment within ports has continued to increase, significantly reducing fossil fuel consumption. Ports are also promoting zero-carbon terminal development through photovoltaic and wind power facilities, while advancing the innovative use of green marine fuels such as LNG and methanol to meet the diverse needs of ships.

Third, a comprehensive port pollution prevention and control system is being developed at an accelerated pace. Through the establishment of ship-generated waste reception and disposal systems and stronger controls over dust and wastewater from port operations, major ports across China have generally achieved comprehensive collection and proper disposal of domestic waste and oily wastewater from ships. In addition, digital transformation and the application of intelligent technologies are providing more efficient and precise technical support for the green development of ports.

Gu stated that, in the face of the severe challenges posed by global climate change, developing green ports is no longer an “option,” but a “must-answer question” that will shape our future. Achieving this will require not only technological innovation, but also broad and in-depth multilateral cooperation across geographical and institutional boundaries within the global port and shipping community.

Looking ahead, he proposed three pathways for the green transformation of ports:

First, use “global governance” to drive “systemic transformation” and build a new framework of rules for green ports. The industry should work toward an inclusive and interconnected international governance model for green ports and shipping, laying the foundation for deeper integration of global green port industrial and supply chains.

Second, use “technological breakthroughs” to drive “industrial advancement” and secure the commanding heights of green port and shipping development. Stakeholders should jointly focus on forward-looking areas such as zero-carbon fuels, smart grids, and end-to-end carbon management, promote the cross-border flow and open sharing of innovation resources, and jointly incubate disruptive technologies and business models for the next generation of the port and shipping industry.

Third, use an “open ecosystem” to enable “collective action” and build a global green port and shipping community with a shared future. Consensus only demonstrates its value when it is translated into action. Gu called on all parties to jointly explore a multilateral cooperation mechanism for global green ports, turning consensus into tangible action and ensuring that the resulting benefits are shared by all.

Professor Liu Zhixue delivered a speech entitled “Building a Smart Supply Chain Management System” at the “AI + Supply Chain Management Program Development” thematic sub-forum of the 24th China Logistics Academic Annual Conference in 2025.

1. The Concept and Basic Characteristics of Smart Supply Chain Management

Liu began by explaining the concept of smart supply chain management. He noted that a smart supply chain management system is an integrated management model through which enterprises optimize their internal and external supply chain processes by applying a combination of management philosophies, management models, advanced technologies, scientific methods, and equipment and tools, ultimately achieving intelligent, collaborative, networked, resilient, and green operations.

He summarized the fundamental characteristics of smart supply chain management and emphasized that, against the backdrop of the digital economy and intelligent manufacturing, supply chain management can only continue to evolve if these fundamental characteristics are properly understood and incorporated.

Addressing the sub-forum’s theme of “AI+,” Liu pointed out that in today’s academic and industrial environment, without seriously considering advances in artificial intelligence and their implications, it is difficult to truly understand the frontier trends in supply chain management.

2. Five Key Characteristics of AI-Enabled Smart Supply Chain Management

Liu summarized five core characteristics of smart supply chain management.

First, technology empowerment. Without the development of modern information technologies, particularly artificial intelligence, supply chain management could not evolve into smart supply chain management. These technologies are already widely embedded in demand forecasting, inventory control, route optimization, contract review, and other applications.

Second, intelligent decision-making. Digital technologies including the Internet of Things, artificial intelligence, big data, 5G, and blockchain provide scientific decision support for procurement, production, distribution, logistics, and other functions, significantly improving the quality, accuracy, and timeliness of decision-making.

Third, human-machine collaboration. Robots, automated sorting systems, collaborative robots, and other technologies work alongside frontline employees, reshaping production and logistics operations and enabling hyperautomation platforms where “digital employees” collaborate with human workers.

Fourth, data-driven operations. By extracting patterns of supply and demand from massive volumes of data generated through logistics, information, and financial flows, enterprises can build smart supply chains that are visible, perceptible, and dynamically adjustable.

Fifth, platform support. Smart supply chain management platforms built on technologies such as IoT, cloud computing, and large AI models can connect upstream and downstream enterprises while integrating capabilities such as warehouse network planning, inventory optimization, vehicle routing, and channel development, thereby supporting integrated supply chain operations.

3. Applications of AI Technologies and Methods in Smart Supply Chains

At the technical level, Liu systematically reviewed a range of AI technologies and methods that support smart supply chain management. These include machine learning and deep learning, reinforcement learning, natural language processing, knowledge representation and reasoning, computer vision, intelligent planning and decision-making, robotic process automation, as well as integrated technologies such as AI + digital twins, AI + operations research optimization, and AI + IoT.

Drawing on corporate cases, he demonstrated applications in demand forecasting, inventory control, intelligent allocation, smart warehousing, and channel development, showing that AI is moving beyond the role of a standalone tool and becoming deeply embedded in supply chain decision-making and operations.

Liu also noted that China has introduced a series of national strategies and action plans relating to artificial intelligence and the digital economy. These developments present unprecedented opportunities as well as challenges for logistics management, supply chain management, procurement management, and related disciplines. Both universities and enterprises should proactively embrace AI and integrate it into key areas such as operations research optimization, route planning, and resource allocation.

4. The Importance of and Pathways for Building Smart Supply Chain Management Systems

Discussing the importance of smart supply chain management systems, Liu examined the issue at several levels.

At the national level, as external uncertainties increase significantly, strengthening the resilience and security of industrial and supply chains has become a major priority. A smart supply chain management system characterized by data visibility and controllable risks can strengthen China’s competitive advantages in sectors such as new-energy vehicles, home appliances, and communications equipment, while also accelerating the development of emerging industrial chains such as intelligent connected vehicles.

At the enterprise level, smart supply chain systems can strengthen market competitiveness and improve customer service.

Regarding implementation pathways, Liu proposed a systematic framework covering intelligent supply chain decision-making mechanisms, integrated supply chain organizational models, collaborative supply chain operations management, digital supply chain information platforms and control towers, intelligent supply chain logistics systems, proactive risk monitoring, and scientific performance evaluation, providing enterprises with a structured reference for building smart supply chain management systems.

Professor Wang Guowen delivered a speech entitled “Latest Trends in AI Applications in Supply Chains and Knowledge-System Development — A Case Study of Specialized Training for General-Purpose Large Models” at the 24th China Logistics Academic Annual Conference in 2025. The following is based on the speech summary prepared from the conference transcript.

Last year, I discussed how, after training large AI models, we used them to regenerate supply chain process standards. This year, I spent additional time overseas working on the latest stage of large-model training, and I would like to share the results and the process with you.

What we are doing is not merely updating supply chain management process standards. How we use AI to reconstruct logistics and supply chain metrics is highly significant for teaching, research, and business applications. This is an area we will continue to explore in the future.

I will begin with April this year. The speakers earlier today have already provided excellent discussions on the latest policies, trends, China-US relations, as well as corporate case studies. Following the publication last year of my book on end-to-end AI applications in supply chains, I continued researching the latest trends in AI-enabled supply chains and identified five major areas of application.

“As of April 2025” means that what I am sharing today covers developments from April up to the present.

Since April, AI has continued to transform the entire industry, improving efficiency, resilience, and sustainability. This is also closely aligned with the “three new transformations” discussed by President Cai Jin this morning, reflecting a broader common trend.

The first major application is AI-driven freight optimization. Supply chain performance has always required measurement through indicators. In the case of Uber Freight’s network planning, the empty-mile rate on US highways is approximately 35%. Following the application of AI, this rate has been reduced by around 10% to 15%, which represents a very significant improvement.

AI has also dramatically shortened customer service response times. Previously, calling customer service could involve waiting 15 to 20 minutes; now the waiting time can be reduced to around 30 seconds. AI-enhanced dashboards have also improved data accuracy and statistical analysis.

The second area is humanoid robots. Why are humanoid robots developing so rapidly in warehouse logistics systems? It is not simply because robots increasingly resemble humans physically. The key is that these robots now possess artificial intelligence.

Artificial intelligence can be understood as computer programs imitating human mental and intellectual behavior. Only robots equipped with an “AI brain” can truly behave in more human-like ways. In warehouse applications, robots demonstrate significant advantages in autonomous mobility, object manipulation, understanding supply chain operating systems, and completing collaborative tasks.

The third area is AI in military logistics. Logistics as a modern concept originated from military logistics during World War II before being transferred into commercial applications. Many cutting-edge technological innovations have historically been adopted first in military logistics. RFID, for example, was used on the battlefield in Iraq in the early 21st century.

The latest wave of AI applications follows a similar pattern. Among the five innovation cases recognized at the annual conference of the Council of Supply Chain Management Professionals (CSCMP) last year and this year, three were related to military logistics. One case involved an investment of USD 15 million in a platform designed primarily to address battlefield logistics support, particularly ammunition replenishment and emergency medical supplies.

The fourth area is enhanced supply chain visibility. Traditional end-to-end visibility can be achieved through full-process tracking systems. However, once the supply chain crosses borders, this visibility may be interrupted. Achieving visibility beyond the conventional line of sight and across the entire supply chain therefore requires the use of big-data resources. Another key issue is how to reconnect supply chains after disruption.

The fifth area is using AI to respond to supply chain disruptions caused by trade conflicts. Whether we call it industrial relocation or global supply chain restructuring, these developments are reshaping supply chains worldwide. The challenge is to ensure that supply chain resources remain available, controllable, and adjustable. A growing number of AI tools can support predictive analysis, helping companies optimize inventory and logistics allocation globally.

Last year, I shared the first and second stages of testing AI applications in supply chain process standards.

In the first stage, I found that general-purpose large models had a basic understanding of AI-related concepts and could respond relatively accurately to supply chain processes and concepts, although their level of precision was still limited.

In the second stage, after process models were fed into the system, we found that both learning efficiency and output quality improved substantially. This gave us the confidence to publish a book on AI applications.

The third stage began with training conducted from June this year onward. The results of this stage are the main focus of today’s presentation. We found that AI-generated information had become more accurate and much more visual.

Previously, we had to draw process diagrams ourselves using PowerPoint or other software. Now, the generated output has changed completely.

During the first stage, we used my own thinking and the supply chain process framework to conduct page-by-page dialogue with the model, ultimately producing an updated comparison of the entire process system. One important conclusion from this stage was that AI’s understanding of supply chain processes and concepts was sufficiently accurate to be usable.

The second-stage experiment began in June 2023, when ChatGPT opened its platform so that users could build their own applications. This provided a much clearer pathway toward specialization.

I built an application on ChatGPT called Supply Chain Sage. When I open ChatGPT, the first interface provides its general-purpose functions, while the second is my own application. My application retains the entire history of our previous conversations with a very high level of accuracy. This became the basis for the second-stage application and improvement of supply chain processes.

I generated a full table of contents through this process. Every third-level heading resulted from one or several rounds of dialogue with the model, with every three or four rounds generating a document. Through human thinking and dialogue mechanisms, we reconstructed the knowledge system underlying supply chain process standards.

I also added issues including COVID-19, trade conflicts, the Russia-Ukraine conflict, and their impacts on resilient supply chains. These formed part of the results produced during the second stage and were incorporated into the book on AI and big-data applications.

The third stage reflects the evolution from ChatGPT-4o to 5.1. During the GPT-5 stage, two distinct directions emerged. The first made the system more human-like, with smoother dialogue and reasoning. The second strengthened deeper reasoning capabilities.

What did we obtain during the third stage?

First, all processes were redefined and optimized, and every line of process-related text was updated. Second, the processes became smoother. Third, the model became capable of generating highly accurate process diagrams.

Previously, producing illustrations for every chapter was extremely difficult. During preparation of the second edition, considerable effort was required simply to produce an image containing “Chapter 1” and “Chapter 2.” Today, however, it is very easy to generate diagrams that incorporate chapter titles and meanings.

More importantly, when I ask AI what it can do in supply chains, the previous answer was simply a block of text. Now, it provides not only text but also a process architecture diagram. It may even ask whether I would like the answer converted into a chart, a PowerPoint presentation, or a Word document. These activities have increasingly become automated processes.

Across the supply chain processes of planning, sourcing, manufacturing, delivery, and returns, AI and big data can now be mapped clearly to specific applications, supported by increasingly accurate process diagrams and textual descriptions.

Take demand forecasting as an example. If a company can improve demand forecast accuracy by just one percentage point, overall supply chain efficiency may improve by 10%, or even 30%. Forecast accuracy therefore has a major influence on overall supply chain efficiency.

In the past, demand forecasting relied primarily on mathematical tools such as regression analysis and historical enterprise data. Today, AI-based analysis can draw not only on historical data but also on information from social media platforms, economic indicators, and other external sources, while applying a wide range of mathematical methods.

The minimum process standards generated by AI indicate that demand forecasting should be assigned to AI-enabled planning teams. Forecasting should integrate machine-learning capabilities to analyze historical data, social-media trends, economic indicators, and external data.

The major difference between AI-enabled forecasting tools and conventional software is that traditional software mainly relies on structured and internal enterprise data, whereas AI can obtain and incorporate external data.

Real-time information from ERP, IoT, CRM, and POS systems can also be incorporated, together with data on pricing, marketing, seasonal trends, and competitor behavior.

AI can also be combined with Collaborative Planning, Forecasting and Replenishment (CPFR) tools for multi-tier collaboration. Forecast performance can be continuously monitored and recalibrated using indicators such as forecast bias, forecast stability, and service levels. Real-time alerts can be generated when deviations occur, while short-term forecasts can be updated on a daily basis.

If students or researchers are working on forecasting processes, this AI-generated process standard can provide meaningful guidance for practical applications.

The second major process is procurement. AI can help determine what procurement strategy should be adopted, clarify procurement sequences and their logical relationships, and show how the technological ecosystem can support procurement processes. More importantly, every issue identified by the model can be explored further by asking AI to explain how a particular technology or tool should actually be applied.

In manufacturing, AI can be combined with lean manufacturing. When Taiichi Ohno developed the Toyota Production System more than two decades ago, AI and big-data analytics had not yet been applied to manufacturing and supply chain analysis. In the future, if manufacturing processes are combined with blockchain-based value-exchange systems and automated contract payments, it may become possible to achieve end-to-end unmanned manufacturing, delivery, and value settlement.

AI can likewise play specific roles throughout the delivery process, from order processing to final product delivery, as well as in returns and execution processes. I have updated approximately ten process areas.

This time, I also added two new process standards: supply chain resilience and green supply chains. Neither was included in the first or second editions of the supply chain process standards.

The result is a comprehensively updated set of process standards. I regard this third stage of training as the combined result of ChatGPT’s evolution over the past year and our own model-training efforts. Altogether, I generated more than 400 pages of process standards.

These standards were presented at a global supply chain conference in October this year. At that time, I issued an open invitation to professionals and entrepreneurs to review and validate the materials and determine whether they met current needs and contained accurate, usable information.

Why did I change today’s title to focus on AI application updates and knowledge-system development?

New process diagrams no longer need to be designed manually. For areas such as resilience, security, and sustainability, we are already seeing third-, fourth-, and even fifth-level processes. Anyone who understands how to use AI tools can continue exploring these processes to almost unlimited levels of detail, extending them all the way to the operational level.

In the e-commerce era, however, supply chains are increasingly being flattened. Under C2M models, where customers place orders directly with factories, some traditional multi-tier supply chain models may no longer retain the same relevance.

This means that AI is disrupting not only the creation of supply chain knowledge systems, but also the way this knowledge is taught and learned.

If process standards can be used to support learning and guide future practice, then the way we teach students and construct knowledge systems may fundamentally change.

Kate Vetashek, who participated in drafting supply chain process standards twenty years ago, attended one of my presentations twenty years later and reviewed the materials we had generated. She was very surprised by what she saw.

Finally, we should pay close attention to major US industry players, particularly the “Magnificent Seven” technology companies and their massive investments in AI infrastructure.

AI is not without bubbles, but the technology is still only at the beginning—almost at an infant stage. AI has certainly not solved every problem. However, the enormous investment currently being made by leading US technology companies in AI will, in my view, reshape the entire landscape over the next three to five years.

Thank you.

Prepared from the conference transcript. Source: China Society of Logistics.

Professor Chen Jihong delivered a special lecture entitled “Port and Shipping Development under Complex Geopolitical Conditions and Strategic Responses for Shenzhen” at the 18th session of the “Economic Lecture Series” organized by the Yantian District Committee of the Chinese People’s Political Consultative Conference.

In the lecture, Professor Chen provided a systematic and in-depth analysis of the historical evolution of the global maritime landscape and the challenges it currently faces. He reviewed the development of the United States’ international shipping industry, from its former prominence to its gradual decline.

He then focused on the evolution of US strategy toward China since the beginning of the 21st century. Chen explained how the United States has shifted from an “Asia-Pacific rebalancing” strategy toward a broader “global node control” strategy encompassing multiple areas, including technology, maritime transportation, and energy corridors. He also offered a detailed analysis of the challenges China has recently encountered in the international maritime arena.

Against this increasingly complex backdrop, Professor Chen explored both short-term responses and medium- to long-term strategic approaches for China to overcome emerging constraints and challenges.

In this context, he placed particular emphasis on deepening cooperation between Shenzhen and Hong Kong and advancing the development of Yantian as an international shipping hub. He proposed seven specific areas of cooperation, including the joint development of a shipping logistics and services coordination platform, the creation of a digital supply chain and smart port system, and the establishment of a “technology + maritime” integrated platform.

These initiatives are intended to contribute to the development of a maritime community with a shared future that is better able to manage risks while supporting global development.

Professor Lu Bo delivered an academic lecture entitled “Transformation and Upgrading of Smart Ports and the Shipping Industry in the Digital Economy Era” at the 12th session of the 2026 Jinggong Lecture Series of Beijing Institute of Technology.

Professor Lu analyzed emerging trends in the development of the port and shipping industries against the backdrop of the digital economy.

He pointed out that with the rapid development of next-generation information technologies such as big data, artificial intelligence, and the Internet of Things, traditional port and shipping systems are accelerating their transformation toward digitalization, intelligence, and green development.

Smart ports are not only an important component of a modern integrated transportation system, but also critical infrastructure for promoting global supply chain coordination and upgrading industrial chains.

In the digital economy era, Lu emphasized that the port and shipping industry needs to place data-driven operations and intelligent decision-making at its core, continuously improving resource allocation efficiency, supply chain coordination, and systemic resilience.

During the lecture, Professor Lu systematically introduced the research achievements of his team in port operations and shipping supply chain management in recent years.

He provided in-depth explanations of several key issues, including:

• optimization of port operations;
• coordinated scheduling of shipping supply chains;
• digital twins for ports and shipping;
• intelligent decision-making; and
• the development of resilient supply chains.

He noted that intelligent analytical models based on big data and artificial intelligence can effectively improve port operating efficiency and the allocation of logistics resources.

At the same time, digital coordination platforms can facilitate information sharing and dynamic coordination among ports, shipping companies, and upstream and downstream participants in supply chains.

Professor Lu also discussed, in the context of major national strategic priorities and industry development trends, how the transformation and upgrading of smart ports and the shipping industry can help strengthen the international competitiveness of China’s port and shipping system.

At the conclusion of the lecture, Professor Lu drew on his own research and practical experience to share insights gained from major projects funded by the National Social Science Fund of China and key projects supported by the National Natural Science Foundation of China.

He also exchanged views with faculty members and students on frontier research topics in transportation and supply chain management in the digital economy era.

The lecture focused on the frontiers of the digital economy and smart port and shipping development, combining theoretical depth with practical relevance. It systematically presented recent research achievements in smart ports and shipping supply chains while also addressing the practical demands of digital transformation and green, low-carbon development.

Participants noted that the lecture broadened their understanding of the digital development of the port and shipping industries and provided valuable insights for research on industrial transformation, modern supply chain governance, and intelligent transportation systems.

Professor He Mingke delivered a speech entitled “Logistics and Supply Chain Management in the Age of Artificial Intelligence” at the thematic sub-forum “Industry-University-Research Collaborative Innovation: Practices and Prospects of AI + Logistics” during the 24th China Logistics Academic Annual Conference in 2025.

1. Evolution of Logistics and Supply Chain Management

Professor He first reviewed the evolution of logistics and supply chain management.

He explained that the industry developed from a fragmented stage before the 1960s, to functional consolidation in the 1960s, integration in the 1980s, and finally value capture from the 2000s onward.

Throughout this process, functions were continuously combined and organizational boundaries steadily expanded. Relevant industry associations were established and transformed accordingly, while the scope of activities expanded from individual functions such as demand forecasting, warehousing, and transportation to broader supply chain management covering areas including marketing, logistics, information technology, and finance.

The core logic behind this evolution, according to He, has been to develop increasingly sophisticated supply chain networks through coordination, optimization, and integration.

2. The Underlying Logic of Logistics and Supply Chain Transformation

Professor He then explained the fundamental logic behind the evolution of logistics and supply chain management, as well as the core meaning of logistics coordination.

He argued that the underlying drivers of industry transformation are competition for survival and innovation-led evolution. The development path follows the logic of:

coordination + optimization + integration

and ultimately leads toward platform-based and ecosystem-based development.

Logistics coordination, meanwhile, refers to the efficient interaction among different participants based on shared objectives.

Through cooperation, adjustment, and synchronization at the strategic, tactical, and operational levels, participants can achieve efficient coordination across different organizations, regions, and operational stages.

This process involves nine major dimensions, including establishing common objectives, building coordination mechanisms, and jointly developing strategies. Its fundamental objectives are to:

reduce costs, improve efficiency, and enhance quality.

Professor He then compared AI-based data processing with physical logistics operations and examined the implications for intelligent logistics.

He noted that the two share similarities in several respects, including the nature of end-to-end processes, the need for resource coordination, and the management of flows across multiple stages.

The logistics industry can therefore draw lessons from AI logic by:

• establishing digital profiles for goods;
• integrating data across the entire logistics chain; and
• building intelligent dispatching and decision-making centers.

However, there are also fundamental differences.

Goods have physical attributes such as weight, volume, and location; logistics operates in open and complex environments; and logistics activities involve many different companies, vehicles, employees, infrastructure systems, and other participants.

These factors create major barriers to logistics intelligence, including:

high costs, low tolerance for errors, and difficulties in standardization.

Such challenges therefore need to be addressed in a targeted manner.

3. Transformation of Logistics and Supply Chain Management in the AI Era

Professor He concluded by discussing how logistics and supply chain management may evolve in the age of artificial intelligence.

He argued that the industry should build on its existing evolutionary logic while taking into account the characteristics of AI technologies.

To overcome the barriers associated with intelligent transformation, improvements are required across multiple dimensions, including:

• facilities and equipment;
• business processes;
• technical standards; and
• data infrastructure.

AI algorithms should be used to support dynamic decision-making and iterative optimization.

At the same time, the industry should work toward unified data formats and standardized equipment interfaces in order to create a more coordinated and efficient operating ecosystem.

Security and regulatory compliance must also be taken into account throughout this transformation.

Ultimately, Professor He argued that logistics and supply chain management should evolve toward systems that are more intelligent, more resilient, and more efficient.