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供應鏈管理

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1738586

全球物流和供應鏈人工智慧市場規模（按產品、應用、最終用戶、區域覆蓋範圍和預測）

Global AI In Logistics And Supply Chain Market Size By Offering (Hardware, Software), By Application (Supply Chain Planning, Warehouse Management), By End-User (Automotive, Retail, Food And Beverages), By Geographic Scope And Forecast

出版日期: 2025年05月12日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

物流及供應鏈人工智慧市場規模及預測

2024 年物流和供應鏈人工智慧市場規模價值 44.5064 億美元，預計到 2032 年將達到 650.3934 億美元，在 2026-2032 年預測期內的複合年成長率為 46.50%。

物流和供應鏈中的人工智慧是指將機器學習、預測分析和自動化等人工智慧技術應用於供應鏈各層級的商品、服務和資訊管理。人工智慧能夠評估來自眾多資訊來源的大量數據，並透過最佳化路線、管理庫存和預測需求來改善決策。其應用包括用於運輸的自動駕駛汽車和無人機、用於客戶支援的人工智慧聊天機器人，以及用於提高生產力的自動化倉庫業務。該技術能夠提高準確性、降低成本並減少物流行業的人為錯誤。

隨著電子商務、製造業和零售業等行業對更靈活、更敏捷的供應鏈的需求不斷成長，人工智慧在物流和供應鏈管理中的應用正在迅速擴展。隨著人工智慧的發展，其潛在的應用領域包括提升供應鏈可視性、即時追蹤和預測性資產維護。

人工智慧能夠透過資源最佳化降低風險和延誤，並提高永續性，這將成為全球物流網路轉型的關鍵。隨著物聯網、巨量資料和機器人技術在物流業務中的應用日益廣泛，預計該產業對人工智慧解決方案的市場將迅速擴張。

全球物流與供應鏈人工智慧市場動態

影響全球物流和供應鏈人工智慧市場的關鍵市場動態是：

關鍵市場促進因素

電子商務普及率提升：電子商務的快速成長推動了對更有效率物流和供應鏈管理的需求。預計2021年美國電子商務銷售額將達8,708億美元，較2020年成長14.2%。這種激增帶來了許多複雜問題，例如管理大量訂單、確保按時送達以及處理退貨。人工智慧可以透過路線最佳化、倉庫自動化和需求預測來幫助應對這些挑戰，從而提高營運效率並提升客戶滿意度。

供應鏈可視性和透明度需求日益成長：應對供應鏈中斷的需求推動了對供應鏈可視性和透明度的需求。根據業務連續性研究所 (BCI) 預測，2021 年 69% 的公司將至少經歷一次供應鏈中斷。企業和消費者都希望獲得即時追蹤，以確保更順暢的營運、更快的問題解決和更可靠的交付。人工智慧 (AI) 提供了必要的預測技能和即時數據分析，以提高整個供應鏈的可視性、降低風險並增強韌性。

需要降低成本並提高業務效率：根據美國供應鏈管理協會 (CSCMP) 的數據，美國企業的物流支出預計將在 2020 年達到 1.63 兆美元，佔 GDP 的 7.4%。企業越來越依賴人工智慧 (AI) 來最佳化流程、降低人事費用並簡化業務。人工智慧透過自動化、預測分析和庫存管理來提高效率，使企業能夠在競爭激烈的市場中保持卓越服務水準的同時降低成本。

主要問題

高品質資料存取受限：人工智慧依賴高品質、組織良好的數據來做出準確的預測和決策。許多供應鏈處理的資料片段化或格式混亂，導致人工智慧效能低落。即時、純淨數據的存取受限，使得企業難以充分利用人工智慧的潛力，從而降低其最佳化業務的有效性。

監理與合規挑戰：物流的人工智慧營運面臨複雜的法規環境，且因地區和產業而異。遵守資料隱私、勞動法和環境要求等諸多規則並非易事。企業必須檢驗其人工智慧系統是否符合眾多法規結構，這可能會阻礙其部署並增加營運成本。

資料隱私和安全問題：人工智慧系統依賴大量數據，因此隱私和安全是主要問題。隨著企業在供應鏈中轉移敏感訊息，資料外洩的風險也隨之增加。更嚴格的數據標準和客戶隱私期望要求企業保護其數據，這減緩了人工智慧的採用並增加了合規成本。

主要趨勢

需求預測的預測分析：人工智慧驅動的預測分析正成為預測整個供應鏈需求的重要工具。透過分析歷史數據和外部因素，人工智慧可以幫助企業更好地預測需求波動，從而減少缺貨和庫存過剩。這一趨勢源於對更敏捷的供應鏈的需求，這些供應鏈能夠即時回應變化，從而提高客戶滿意度並減少浪費。

利用人工智慧最佳化最後一公里配送：人工智慧正在透過最佳化路線、降低油耗和縮短配送時間，徹底改變最後一公里配送。電子商務的興起以及消費者對快速、經濟高效配送的期望，促使企業利用人工智慧來提升配送流程最後一環節的效率。這一趨勢源自於企業日益成長的需求，即提高配送速度和準確性，同時降低物流成本。

人工智慧主導的風險管理和中斷緩解：人工智慧正迅速被用於預測和緩解供應鏈中斷、自然災害和地緣政治事件等風險。透過分析多種資料來源，人工智慧可以預測未來的中斷，並為不可預見的事件做好準備。這一趨勢是由供應鏈日益複雜和國際化所驅動的，這需要主動的風險管理技術來確保平穩運作。

人工智慧與物聯網 (IoT) 的融合：人工智慧與物聯網 (IoT) 的融合正在透過打造更智慧、更互聯的物流系統來改善供應鏈自動化。物聯網感測器收集來自卡車、倉庫和產品的即時數據，人工智慧分析這些資訊以最佳化營運。這一趨勢源於人們對更聰明、更有效率的供應網路的渴望，這些網路能夠自我監控並持續改進。

AI in logistics and supply chain is the application of artificial intelligence technologies such as machine learning, predictive analytics, and automation to the management of commodities, services, and information at various levels of the supply chain. AI improves decision-making by evaluating massive amounts of data from many sources, optimizing routes, controlling inventories, and forecasting demand. Applications include self-driving cars and drones for transportation, AI-powered chatbots for customer support, and automated warehousing operations for increased productivity. This technology enhances accuracy, lowers costs, and reduces human error in the logistics industry.

AI in logistics and supply chain management is rapidly expanding, driven by the growing demand for more flexible and responsive supply chains in industries such as e-commerce, manufacturing, and retailing. As AI advances, potential applications include improved supply chain visibility, real-time tracking, and predictive asset maintenance.

AI's ability to decrease risks, delays, and boost sustainability through resource optimization will be important in altering global logistics networks. The market for AI-powered solutions in this industry is predicted to expand rapidly, driven by the growing use of IoT, big data, and robotics in logistics operations.

Global AI In Logistics And Supply Chain Market Dynamics

The key market dynamics that are shaping the global AI In Logistics And Supply Chain Market include:

Key Market Drivers:

Increasing E-Commerce Adoption: The rapid growth in e-commerce, with US e-commerce sales expected to reach USD 870.8 Billion in 2021, up 14.2% from 2020, is pushing the demand for more efficient logistics and supply chain management. This spike presents complicated issues such as managing high-order quantities, ensuring timely deliveries, and handling returns. AI can assist address these difficulties by optimizing routes, automating warehouses, and forecasting demand, resulting in more efficient operations and more customer satisfaction.

Rising Demand for Supply Chain Visibility and Transparency: The rising need for supply chain visibility and transparency is driven by the need to manage disruptions, with the Business Continuity Institute projecting that 69% of firms would experience at least one supply chain disruption in 2021. Both organizations and consumers want real-time tracking to ensure smoother operations, faster problem resolution, and more consistent deliveries. AI provides the predictive skills and real-time data analytics required to improve visibility, decrease risks, and strengthen the overall supply chain resilience.

Need for Cost Reduction and Operational Efficiency: The need for cost reduction and operational efficiency is a fundamental driver in supply chain management, with U.S. company logistics expenditures expected to reach USD 1.63 Trillion in 2020, accounting for 7.4% of GDP, according to the CSCMP. Companies are increasingly depending on artificial intelligence (AI) to optimize processes, cut personnel costs, and streamline operations. AI increases efficiency through automation, predictive analytics, and inventory management, allowing firms to reduce costs while maintaining excellent service levels in a competitive market.

Key Challenges:

Limited Access to Quality Data: AI relies on high-quality, well-organized data to make accurate predictions and decisions. Many supply chains still work with fragmented or poorly formatted data, resulting in inadequate AI performance. Limited access to real-time, clean data makes it difficult for businesses to fully leverage AI's assurance, lowering its efficacy in optimizing operations.

Regulatory and Compliance Challenges: AI in logistics operates in a complicated regulatory environment that varies by region and industry. Adhering to many rules, such as those governing data privacy, labor legislation, and environmental requirements, can be difficult. Companies must verify that their AI systems adhere to numerous regulatory frameworks, which can hinder deployment and increase operational costs.

Data Privacy and Security Concerns: As AI systems rely on massive volumes of data, privacy and security are major concerns. As firms communicate sensitive information throughout the supply chain, the danger of data breaches grows. Stricter data standards and customer privacy expectations require enterprises to secure their data, which slows AI adoption and raises compliance costs.

Key Trends:

Predictive Analytics for Demand Forecasting: AI-powered predictive analytics is becoming an essential tool for anticipating demand throughout supply chains. AI assists businesses in better anticipating demand swings by studying past data and external factors, resulting in fewer stockouts and overstocking. This trend is motivated by the demand for more agile supply chains that can react to market developments in real-time, hence increasing customer satisfaction and lowering waste.

AI-Enhanced Last-Mile Delivery Optimization: AI is transforming last-mile delivery by optimizing routes, lowering fuel usage, and shortening delivery times. With the advent of e-commerce and consumer expectations for speedy, cost-effective shipping, businesses are turning to artificial intelligence to increase efficiency in the final leg of the delivery process. This trend is driven by the growing need to improve delivery speed and accuracy while lowering logistical costs.

AI-Driven Risk Management and Disruption Mitigation: AI is rapidly being utilized to predict and mitigate risks such as supply chain disruptions, natural disasters, and geopolitical incidents. AI may anticipate future interruptions and provide contingency preparations by analyzing multiple data sources. This trend is being driven by the increased complexity and internationalization of supply chains, which requires proactive risk management techniques to ensure smooth operations.

Integration of AI and Internet of Things (IoT): The integration of AI and the Internet of Things (IoT) is improving supply chain automation by enabling smarter and more connected logistics systems. IoT sensors collect real-time data from trucks, warehouses, and products, and AI analyzes this information to optimize operations. This trend is motivated by the desire for smarter, more efficient supply networks that can self-monitor and continuously improve.

Global AI In Logistics And Supply Chain Market Regional Analysis

Here is a more detailed regional analysis of the global AI In Logistics And Supply Chain Market:

North America:

North America is dominant in the AI In Logistics And Supply Chain Market. North America leads in AI adoption in logistics and supply chain management due to its advanced technological infrastructure, strong research and development (R&D) skills, and large number of early adopters. The region's well-established logistics sector, combined with a constant focus on efficiency and innovation, creates ideal conditions for AI solutions to thrive. According to the US Bureau of Labor Statistics, employment in logistics and supply chain management is expected to increase by 30% between 2020 and 2030, owing in part to the growing incorporation of AI technology.

Government support and industry partnerships are speeding up AI deployment in North America. AI-driven logistics optimization has already produced incredible results, with enterprises reporting a 15% cost savings and a 20% improvement in delivery times. The Canadian government's Strategic Innovation Fund, which has committed CAD 950 million (USD 700 Million) for AI research and development from 2023 to 2025, demonstrates the region's leadership in this field. These characteristics - significant investment, strong government support, and tangible advantages - are propelling AI adoption in North America's logistics and supply chain sectors, establishing the region as a global leader in efficiency and competitiveness.

Asia Pacific:

The Asia-Pacific area is seeing huge growth in AI adoption for logistics and supply chain applications, making it the world's fastest-growing market. This spike is being driven by strong economic growth, increasing e-commerce, and an urgent need to improve supply chain efficiency across complicated networks. According to the Asian Development Bank (ADB), the region's e-commerce sector is expected to reach $2.8 trillion by 2025, with a compound annual growth rate (CAGR) of 18.5%. This vast expansion in online retail is putting huge pressure on logistical networks, forcing businesses to use AI-powered solutions to handle the increasing complexity and transaction volumes. Countries with substantial logistics sectors, such as China and India, are leading the drive, with China reporting that 72% of its large logistics enterprises had already deployed AI by 2023, and the figure is predicted to exceed 85% by 2026.

The region's emphasis on cost reduction and operational efficiency accelerates AI adoption. AI-driven solutions are already demonstrating substantial benefits across the region, with Japanese enterprises reporting an 18% cost reduction and a 25% increase in inventory turnover by 2023. Investments in AI for logistics are also increasing, with Southeast Asia alone experiencing a 45% year-over-year rise in AI spending in 2023, which is expected to treble by 2026. These reasons - rapid e-commerce growth, pressure on supply chains, government initiatives, and demonstrable efficiency - are propelling Asia-Pacific AI adoption, establishing it as a global leader in innovative logistics solutions.

Global AI In Logistics And Supply Chain Market: Segmentation Analysis

The Global AI In Logistics And Supply Chain Market is Segmented on the basis of Offering, Application, End-User, And Geography.

AI In Logistics And Supply Chain Market, By Offering

Hardware
Software

Based on Offering, the market is bifurcated into Hardware, and Software. Software is the fastest-growing segment, driven by rising demand for AI-powered solutions like as predictive analytics, route optimization, and warehouse automation. As logistics organizations seek to improve efficiency and cut costs, AI-based software systems are fast gaining popularity. Hardware dominates market share since AI requires powerful computing infrastructure, sensors, and robotics to work well, notably in automated warehouses and transportation systems. Due to the dependency on physical infrastructure, hardware is an essential component of AI logistics integration.

AI In Logistics And Supply Chain Market, By Application

Supply Chain Planning
Warehouse Management
Demand Forecasting
Inventory Management

Based on Application, the market is segmented into Supply Chain Planning, Warehouse Management, Demand Forecasting, and Inventory Management. Warehouse management is the most dominating segment, as it includes a wide range of AI applications that improve operational efficiency, such as automated inventory tracking, robotic picking systems, and optimized storage solutions. The use of artificial intelligence in warehouse management is critical for optimizing operations and lowering costs, cementing its place as a vital market area. Demand forecasting is the fastest-growing segment, driven by the requirement for precise forecasts to satisfy consumer expectations and optimize inventory levels. Companies are increasingly using AI algorithms to analyze historical data and market trends, which improves their ability to predict demand fluctuations.

AI In Logistics And Supply Chain Market, By End-User

Automotive
Retail
Food and Beverages
Healthcare
Manufacturing

Based on End-User, the market is segmented into Automotive, Retail, Food and Beverages, Healthcare, and Manufacturing. The automotive segment is currently dominating, thanks to the industry's emphasis on streamlining production processes, increasing supply chain efficiency, and integrating autonomous car technologies. The automotive industry relies extensively on artificial intelligence (AI) for inventory management, predictive maintenance, and logistical coordination, making it a critical market player. The retail segment is the fastest-growing, driven by e-commerce's spectacular development and the need for real-time inventory tracking, individualized customer experiences, and demand forecasting. Retailers are increasingly using AI-powered solutions to optimize operations, manage complex supply chains, and boost consumer happiness.

Key Players

The "Global AI In Logistics And Supply Chain Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are IBM Corporation, Microsoft Corporation, Google LLC, Amazon.com, Inc., Intel Corporation, Nvidia Corporation, Oracle Corporation, Samsung, and Lamasoft, Inc. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

AI In Logistics And Supply Chain Market Recent Developments
In March 2024, Oracle's new AI-powered supply chain execution capabilities, Oracle Smart Operations, will be available allowing businesses to boost factory output by increasing productivity, improving quality, minimizing downtime, and improving visibility across operations.
In November 2023, IBM and Amazon expanded their relationship to assist businesses in implementing generative AI in their supply chains. They intend to provide a virtual assistant to help supply chain professionals optimize operations and cut expenses.

1 INTRODUCTION OF GLOBAL AI IN LOGISTICS AND SUPPLY CHAIN MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL AI IN LOGISTICS AND SUPPLY CHAIN MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porters Five Force Model
4.4. Value Chain Analysis

5 GLOBAL AI IN LOGISTICS AND SUPPLY CHAIN MARKET, BY OFFERING

5.1 Overview
5.2 Hardware
5.3 Software

6 GLOBAL AI IN LOGISTICS AND SUPPLY CHAIN MARKET, BY APPLICATION

6.1 Overview
6.2 Supply Chain Planning
6.3 Warehouse Management
6.4 Demand Forecasting
6.5 Inventory Management

7 GLOBAL AI IN LOGISTICS AND SUPPLY CHAIN MARKET, BY END-USER

7.1 Overview
7.2 Automotive
7.3 Retail
7.4 Food and Beverages
7.5 Healthcare
7.6 Manufacturing

8 GLOBAL AI IN LOGISTICS AND SUPPLY CHAIN MARKET, BY GEOGRAPHY

8.1 Overview
8.2 North America
- 8.2.1 U.S.
- 8.2.2 Canada
- 8.2.3 Mexico
8.3 Europe
- 8.3.1 Germany
- 8.3.2 U.K.
- 8.3.3 France
- 8.3.4 Rest of Europe
8.4 Asia Pacific
- 8.4.1 China
- 8.4.2 Japan
- 8.4.3 India
- 8.4.4 Rest of Asia Pacific
8.5 Rest of the World
- 8.5.1 Middle East and Africa
- 8.5.2 South America

9 GLOBAL AI IN LOGISTICS AND SUPPLY CHAIN MARKET COMPETITIVE LANDSCAPE

9.1 Overview
9.2 Company Market Ranking
9.3 Key Development Strategies

10 COMPANY PROFILES

10.1 IBM Corporation
- 10.1.1 Overview
- 10.1.2 Financial Performance
- 10.1.3 Product Outlook
- 10.1.4 Key Developments
10.2 Microsoft Corporation
- 10.2.1 Overview
- 10.2.2 Financial Performance
- 10.2.3 Product Outlook
- 10.2.4 Key Developments
10.3 Google LLC
- 10.3.1 Overview
- 10.3.2 Financial Performance
- 10.3.3 Product Outlook
- 10.3.4 Key Developments
10.4 Amazon.com, Inc.
- 10.4.1 Overview
- 10.4.2 Financial Performance
- 10.4.3 Product Outlook
- 10.4.4 Key Developments
10.5 Intel Corporation
- 10.5.1 Overview
- 10.5.2 Financial Performance
- 10.5.3 Product Outlook
- 10.5.4 Key Developments
10.6 Nvidia Corporation
- 10.6.1 Overview
- 10.6.2 Financial Performance
- 10.6.3 Product Outlook
- 10.6.4 Key Developments
10.7 Oracle Corporation
- 10.7.1 Overview
- 10.7.2 Financial Performance
- 10.7.3 Product Outlook
- 10.7.4 Key Developments
10.8 Samsung
- 10.8.1 Overview
- 10.8.2 Financial Performance
- 10.8.3 Product Outlook
- 10.8.4 Key Developments
10.9 Lamasoft, Inc.
- 10.9.1 Overview
- 10.9.2 Financial Performance
- 10.9.3 Product Outlook
- 10.9.4 Key Developments