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人工智能 DL/ML

市場調查報告書

商品編碼

1544622

物流市場機器學習、機會、成長動力、產業趨勢分析與預測，2024-2032

Machine Learning in Logistics Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032

出版日期: 2024年07月09日 | 出版商:

Global Market Insights Inc. | 英文 265 Pages | 商品交期: 2-3個工作天內

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簡介目錄

由於對提高營運效率和節省成本的強烈需求，預計 2024 年至 2032 年間，物流市場規模中的機器學習複合年成長率將超過 23%。透過利用機器學習 (ML) 演算法，物流公司可以分析大量資料集來預測需求、完善路線規劃並增強庫存管理。

透過機器學習，物流提供者可以提供精確的交貨估計，即時監控貨運情況，並根據客戶歷史記錄和偏好來客製化服務。蓬勃發展的電子商務產業，加上對快速、可靠的交付的需求不斷成長，加劇了對能夠增強回應能力和敏捷性的機器學習解決方案的需求。例如，2024 年 1 月，勞埃德·李斯特情報公司 (Lloyd List Intelligence) 推出了用於全球商業航運的「空中交通管制」系統，及時提供船舶到達、出發和停泊時間的資料，以緩解供應鏈挑戰。

整個產業分為組件、技術、組織規模、部署模型、應用程式、最終用戶和區域。

從組成部分來看，服務領域的機器學習在物流市場規模中預計將在 2024 年至 2032 年期間出現顯著成長，因為它在物流領域實施、管理和最佳化機器學習解決方案方面發揮關鍵作用。諮詢、系統整合和管理等服務對於企業熟練實施機器學習、客製化解決方案並將其與現有系統整合至關重要。

預計到 2032 年，車隊管理領域的機器學習物流市場價值將大幅成長。機器學習演算法分析來自各種來源（例如 GPS、遠端資訊處理和駕駛員行為）的資料，以增強路線規劃、監控車輛性能並預測維護需求。

在經濟快速發展、電子商務蓬勃發展以及對供應鏈完善的關注的推動下，預計到 2032 年，亞太地區機器學習在物流行業的規模將大幅成長。隨著城市化和工業成長的不斷發展，亞太地區國家擴大轉向先進的物流解決方案，以熟練地管理該地區錯綜複雜的供應鏈和大量貨物。

產業生態系統分析
供應商格局
- 平台提供者
- 軟體供應商
- 服務提供者
- 配銷通路
- 最終用戶
利潤率分析
技術與創新格局
專利分析
重要新聞和舉措
監管環境
衝擊力
- 成長動力
  - 進一步最佳化供應鏈營運
  - 倉儲作業自動化
  - 電子商務產業的成長
  - 對增強客戶體驗的需求不斷成長
- 產業陷阱與挑戰
  - 數據品質和整合問題
  - 與遺留系統整合
成長潛力分析
波特的分析
PESTEL分析

第 4 章：競爭格局

介紹
公司市佔率分析
競爭定位矩陣
戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

主要趨勢
軟體
服務
- 託管
- 專業的

第 6 章：市場估計與預測：按技術分類，2021 - 2032 年

主要趨勢
監督學習
無監督學習

第 7 章：市場估計與預測：按組織規模，2021 - 2032 年

主要趨勢
大型企業
中小企業 (SME)

第 8 章：市場估計與預測：按部署模型，2021 - 2032

主要趨勢
基於雲端
本地

第 9 章：市場估計與預測：按應用分類，2021 - 2032

主要趨勢
庫存管理
供應鏈規劃
運輸管理
倉庫管理
車隊管理
風險管理與安全
其他

第 10 章：市場估計與預測：按最終用戶分類，2021 - 2032 年

主要趨勢
零售與電子商務
製造業
衛生保健
汽車
食品和飲料
消費品
其他

第 11 章：市場估計與預測：按地區，2021 - 2032

主要趨勢
北美洲
- 美國
- 加拿大
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 俄羅斯
- 北歐人
- 歐洲其他地區
亞太地區
- 中國
- 印度
- 日本
- 澳洲
- 韓國
- 東南亞
- 亞太地區其他地區
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 拉丁美洲其他地區
MEA
- 阿拉伯聯合大公國
- 南非
- 沙烏地阿拉伯
- MEA 的其餘部分

第 12 章：公司簡介

Amazon Web Services, Inc. (AWS)
Blue Yonder Group, Inc.
C.H. Robinson Worldwide, Inc.
Convoy, Inc.
Coupa Software Inc.
DHL Supply Chain
FedEx Corporation
Flexport, Inc.
Google LLC
Infor, Inc.
International Business Machines Corporation (IBM)
Locus Robotics Corporation
Manhattan Associates, Inc.
Microsoft Corporation
Oracle Corporation
SAP SE
Trimble Inc.
Uber Technologies, Inc.
United Parcel Service, Inc.
Waymo LLC

簡介目錄

Product Code: 10157

Machine learning in logistics market size is anticipated to witness over 23% CAGR between 2024 and 2032 led by strong demand for improved operational efficiency and cost savings. By leveraging machine learning (ML) algorithms, logistics firms can analyze extensive data sets to forecast demand, refine route planning, and enhance inventory management.

With machine learning, logistics providers can deliver precise delivery estimates, monitor shipments in real-time, and customize services based on customer history and preferences. The booming e-commerce sector, coupled with rising demands for swift and reliable deliveries, intensifies the need for ML solutions that bolster responsiveness and agility. For example, in January 2024, Lloyd List Intelligence unveiled an 'air traffic control' system for global commercial shipping, offering timely data on vessel arrivals, departures, and berth times to mitigate supply chain challenges.

The overall industry is divided into component, technique, organization size, deployment model, application, end user, and region.

Based on component, the machine learning in logistics market size from the services segment is slated to witness significant growth during 2024-2032 due to its critical role in implementing, managing, and optimizing ML solutions within the logistics sector. Services like consulting, system integration, and management are vital for firms to adeptly implement machine learning, customize solutions, and integrate them with pre-existing systems.

Machine learning in logistics market value from the fleet management segment will foresee considerable growth up to 2032. This is driven by the need for harnessing advanced analytics to optimize vehicle operations and improve overall efficiency. ML algorithms analyze data from various sources, such as GPS, telematics, and driver behavior, to enhance route planning, monitor vehicle performance, and predict maintenance needs.

Asia Pacific machine learning in logistics industry size is anticipated to witness substantial growth through 2032, fueled by swift economic progress, surging e-commerce, and a focus on supply chain refinement. With urbanization and industrial growth on the rise, APAC nations are increasingly turning to advanced logistics solutions to adeptly manage intricate supply chains and high goods volumes in the region.

Chapter 1 Methodology and Scope

1.1 Market scope and definition
1.2 Research design
- 1.2.1 Research approach
- 1.2.2 Data collection methods
1.3 Base estimates and calculations
- 1.3.1 Base year calculation
- 1.3.2 Key trends for market estimation
1.4 Forecast model
1.5 Primary research and validation
- 1.5.1 Primary sources
- 1.5.2 Data mining sources

Chapter 2 Executive Summary

2.1 Industry 360° synopsis, 2021 - 2032

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Supplier landscape
- 3.2.1 Platform provider
- 3.2.2 Software provider
- 3.2.3 Service Provider
- 3.2.4 Distribution channel
- 3.2.5 End user
3.3 Profit margin analysis
3.4 Technology and innovation landscape
3.5 Patent analysis
3.6 Key news and initiatives
3.7 Regulatory landscape
3.8 Impact forces
- 3.8.1 Growth drivers
  - 3.8.1.1 Increased optimization of supply chain operations
  - 3.8.1.2 Automation of warehousing operations
  - 3.8.1.3 Growth of e-commerce sector
  - 3.8.1.4 Rising need for enhanced customer experience
- 3.8.2 Industry pitfalls and challenges
  - 3.8.2.1 Data quality and integration concern
  - 3.8.2.2 Integration with legacy systems
3.9 Growth potential analysis
3.10 Porter's analysis
- 3.10.1 Supplier power
- 3.10.2 Buyer power
- 3.10.3 Threat of new entrants
- 3.10.4 Threat of substitutes
- 3.10.5 Industry rivalry
3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix

Chapter 5 Market Estimates and Forecast, By Component, 2021 - 2032 ($Bn)

5.1 Key trends
5.2 Software
5.3 Services
- 5.3.1 Managed
- 5.3.2 Professional

Chapter 6 Market Estimates and Forecast, By Technique, 2021 - 2032 ($Bn)

6.1 Key trends
6.2 Supervised learning
6.3 Unsupervised learning

Chapter 7 Market Estimates and Forecast, By Organization Size, 2021 - 2032 ($Bn)

7.1 Key trends
7.2 Large enterprises
7.3 Small and medium-sized enterprises (SMEs)

Chapter 8 Market Estimates and Forecast, By Deployment Model, 2021 - 2032 ($Bn)

8.1 Key trends
8.2 Cloud-based
8.3 On-premises

Chapter 9 Market Estimates and Forecast, By Application, 2021 - 2032 ($Bn)

9.1 Key trends
9.2 Inventory management
9.3 Supply chain planning
9.4 Transportation management
9.5 Warehouse management
9.6 Fleet management
9.7 Risk management and security
9.8 Others

Chapter 10 Market Estimates and Forecast, By End User, 2021 - 2032 ($Bn)

10.1 Key trends
10.2 Retail and e-commerce
10.3 Manufacturing
10.4 Healthcare
10.5 Automotive
10.6 Food and beverage
10.7 Consumer goods
10.8 Others

Chapter 11 Market Estimates and Forecast, By Region, 2021 - 2032 ($Bn)

11.1 Key trends
11.2 North America
- 11.2.1 U.S.
- 11.2.2 Canada
11.3 Europe
- 11.3.1 UK
- 11.3.2 Germany
- 11.3.3 France
- 11.3.4 Italy
- 11.3.5 Spain
- 11.3.6 Russia
- 11.3.7 Nordics
- 11.3.8 Rest of Europe
11.4 Asia Pacific
- 11.4.1 China
- 11.4.2 India
- 11.4.3 Japan
- 11.4.4 Australia
- 11.4.5 South Korea
- 11.4.6 Southeast Asia
- 11.4.7 Rest of Asia Pacific
11.5 Latin America
- 11.5.1 Brazil
- 11.5.2 Mexico
- 11.5.3 Argentina
- 11.5.4 Rest of Latin America
11.6 MEA
- 11.6.1 UAE
- 11.6.2 South Africa
- 11.6.3 Saudi Arabia
- 11.6.4 Rest of MEA