ロジスティクスとサプライチェーンにおけるAIの世界市場規模：オファリング別、用途別、エンドユーザー別、地域範囲別および予測

ロジスティクスとサプライチェーンにおけるAI市場規模と予測

ロジスティクスとサプライチェーンにおけるAI市場規模は、2024年に44億5,064万米ドルと評価され、2026～2032年にかけて46.50%のCAGRで成長し、2032年には650億3,934万米ドルに達すると予測されます。

ロジスティクスとサプライチェーンにおけるAIとは、機械学習、予測分析、自動化などの人工知能技術をサプライチェーンの様々なレベルにおける商品、サービス、情報の管理に応用することです。AIは多くの情報源からの膨大なデータを評価し、ルートを最適化し、在庫を管理し、需要を予測することで意思決定を改善します。アプリケーションには、輸送用の自動運転車やドローン、カスタマーサポート用のAI搭載チャットボット、生産性向上のための自動倉庫業務などがあります。この技術は、物流産業における正確性を高め、コストを下げ、人的ミスを減らします。

eコマース、製造業、小売業などの産業では、より柔軟で迅速なサプライチェーンへの需要が高まっており、ロジスティクスとサプライチェーン管理におけるAIは急速に拡大しています。AIの進歩に伴い、サプライチェーンの可視性の向上、リアルタイムの追跡、予測的な資産メンテナンスなど、潜在的な応用が含まれます。

リソースの最適化を通じてリスクや遅延を減らし、持続可能性を高めるAIの能力は、世界の物流ネットワークを変化させる上で重要になると考えられます。この産業におけるAI搭載ソリューションの市場は、物流業務におけるIoT、ビッグデータ、ロボティクスの利用拡大により、急速に拡大すると予測されます。

ロジスティクスとサプライチェーンにおける世界のAI市場力学

世界のロジスティクスとサプライチェーンにおけるAI市場を形成している主要市場力学は以下の通りです。

主要市場促進要因

eコマース導入の増加：eコマースの急速な成長により、米国のeコマース売上高は2021年には2020年比14.2%増の8,708億米ドルに達すると予想され、より効率的なロジスティクスとサプライチェーン管理の需要が高まっています。この急増は、大量の注文の管理、タイムリーな配送の確保、返品処理といった複雑な問題を引き起こしています。AIは、ルートの最適化、倉庫の自動化、需要予測により、これらの困難への対処を支援し、より効率的なオペレーションと顧客満足度の向上をもたらします。

サプライチェーンの可視性と透明性に対する需要の高まり：サプライチェーンの可視性と透明性に対するニーズが高まっている背景には、混乱に対処する必要性があり、Business Continuity Instituteは、2021年には69%の企業が少なくとも1回はサプライチェーンの混乱を経験すると予測しています。企業も消費者も、よりスムーズなオペレーション、より迅速な問題解決、より安定した配送を実現するために、リアルタイムの追跡を求めています。AIは、可視性を改善し、リスクを低減し、サプライチェーン全体の回復力を強化するために必要な予測スキルとリアルタイムのデータ分析を記載しています。

コスト削減と業務効率化の必要性：CSCMPによると、米国企業の物流支出は2020年に1兆6,300億米ドルに達し、GDPの7.4%を占めると予想されています。企業は、プロセスを最適化し、人件費を削減し、業務を合理化するために、人工知能（AI）への依存度を高めています。AIは自動化、予測分析、在庫管理を通じて効率を高め、企業は競争市場で優れたサービスレベルを維持しながらコストを削減できます。

主要課題

高品質データへのアクセス制限：AIは、正確な予測と意思決定を行うために、高品質でよく整理されたデータに依存しています。多くのサプライチェーンは、断片的なデータや不十分なフォーマットのデータで作業しているため、AIのパフォーマンスが不十分です。リアルタイムでクリーンなデータへのアクセスが限られているため、企業がAIの保証を十分に活用することが難しく、業務の最適化におけるAIの有効性が低下しています。

規制とコンプライアンスの課題：ロジスティクスにおけるAIは、地域や産業によって異なる複雑な規制環境の中で運用されています。データプライバシー、労働法、環境要件など、多くの規則を遵守することは困難です。企業は、自社のAIシステムが多数の規制枠組みに準拠していることを検証しなければならず、これが展開の妨げとなり、運用コストを増大させる可能性があります。

データプライバシーとセキュリティの懸念：AIシステムは大量のデータに依存しているため、プライバシーとセキュリティは大きな懸念事項です。企業がサプライチェーン全体で機密情報をやり取りするようになると、データ漏洩の危険性が高まります。より厳格なデータ標準と顧客のプライバシーへの期待により、企業はデータを保護する必要があるが、これはAIの採用を遅らせ、コンプライアンスコストを上昇させています。

主要動向

需要予測のための予測分析：AIを活用した予測分析は、サプライチェーン全体の需要予測に不可欠なツールになりつつあります。AIは、過去のデータや外部要因を調査することで、企業が需要の変動をより的確に予測できるよう支援し、在庫切れや過剰在庫の発生を抑制します。この動向の背景には、開発にリアルタイムで対応し、顧客満足度の向上と無駄の削減を実現する、より俊敏なサプライチェーンへの需要があります。

AIによるラストマイル配送の最適化：AIは、ルートの最適化、燃料使用量の削減、配送時間の短縮により、ラストワンマイル配送に変革をもたらしつつあります。eコマースの登場と、迅速で費用対効果の高い配送に対する消費者の期待により、企業は配送プロセスの最終段階における効率を高めるために人工知能を活用するようになっています。この動向は、物流コストを削減しながら配送スピードと精度を向上させる必要性が高まっていることが背景にあります。

AI主導のリスク管理と混乱緩和：AIは、サプライチェーンの混乱、自然災害、地政学的事件などのリスクを予測・軽減するために急速に活用されつつあります。AIは複数のデータソースを分析することで、将来の中断を予測し、不測の事態に備えることができます。この動向は、サプライチェーンの複雑化と国際化の進展によって推進されており、円滑なオペレーションを確保するためのプロアクティブなリスク管理技術が必要とされています。

AIとモノのインターネット（IoT）の統合：AIとモノのインターネット（IoT）の統合は、よりスマートでコネクテッドな物流システムを可能にすることで、サプライチェーンの自動化を向上させています。IoTセンサがトラック、倉庫、製品からリアルタイムのデータを収集し、AIがこの情報を分析してオペレーションを最適化します。この動向は、自己モニタリングと継続的改善が可能な、よりスマートで効率的な供給ネットワークへの要望が動機となっています。

目次

第1章 世界のロジスティクスとサプライチェーンにおけるAI市場の採用

• 市場概要
• 調査範囲
• 前提条件

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

• データマイニング
• バリデーション
• 一次資料
• データソース一覧

第4章 世界のロジスティクスとサプライチェーンにおけるAI市場展望

• 概要
• 市場力学
  • 促進要因
  • 抑制要因
  • 機会
• ポーターのファイブフォースモデル
• バリューチェーン分析

第5章 ロジスティクスとサプライチェーンにおけるAIの世界市場：オファリング別

• 概要
• ハードウェア
• ソフトウェア

第6章 ロジスティクスとサプライチェーンにおけるAIの世界市場：用途別

• 概要
• サプライチェーンプランニング
• 倉庫管理
• 需要予測
• 在庫管理

第7章 世界のロジスティクスとサプライチェーンにおけるAI市場：エンドユーザー別

• 概要
• 自動車
• 小売
• 飲食品
• ヘルスケア
• 製造業

第8章 ロジスティクスとサプライチェーンにおけるAIの世界市場：地域別

• 概要
• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • その他の欧州
• アジア太平洋
  • 中国
  • 日本
  • インド
  • その他のアジア太平洋
• その他
  • 中東・アフリカ
  • 南米

第9章 世界のロジスティクスとサプライチェーンにおけるAI市場の競合情勢

• 概要
• 各社の市場ランキング
• 主要開発戦略

第10章 企業プロファイル

• IBM Corporation
• Microsoft Corporation
• Google LLC
• Amazon.com, Inc.
• Intel Corporation
• Nvidia Corporation
• Oracle Corporation
• Samsung
• Lamasoft, Inc.

第11章 付録

• 関連調査

AI In Logistics And Supply Chain Market Size And Forecast

AI In Logistics And Supply Chain Market size was valued at USD 4450.64 Million in 2024 and is projected to reach USD 65039.34 Million by 2032, growing at a CAGR of 46.50% from 2026 to 2032.

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.

TABLE OF CONTENTS

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

11 APPENDIX

• 11.1 Related Research