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人工智能 DL/ML

市场调查报告书

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1544622

物流市场机器学习、机会、成长动力、产业趋势分析与预测，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个工作天内

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简介目录

由于对提高营运效率和节省成本的强烈需求，预计 2024 年至 2032 年间，物流市场规模中的机器学习复合年增长率将超过 23%。透过利用机器学习 (ML) 演算法，物流公司可以分析大量资料集来预测需求、完善路线规划并增强库存管理。

透过机器学习，物流提供者可以提供精确的交货估计，即时监控货运情况，并根据客户历史记录和偏好来客製化服务。蓬勃发展的电子商务产业，加上对快速、可靠的交付的需求不断增长，加剧了对能够增强回应能力和敏捷性的机器学习解决方案的需求。例如，2024 年 1 月，劳埃德·李斯特情报公司 (Lloyd List Intelligence) 推出了用于全球商业航运的「空中交通管制」系统，及时提供船舶到达、出发和停泊时间的资料，以缓解供应链挑战。

整个产业分为组件、技术、组织规模、部署模型、应用程式、最终用户和区域。

从组成部分来看，服务领域的机器学习在物流市场规模中预计将在 2024 年至 2032 年期间出现显着增长，因为它在物流领域实施、管理和优化机器学习解决方案方面发挥关键作用。咨询、系统整合和管理等服务对于企业熟练实施机器学习、客製化解决方案并将其与现有系统整合至关重要。

预计到 2032 年，车队管理领域的机器学习物流市场价值将大幅成长。机器学习演算法分析来自各种来源（例如 GPS、远端资讯处理和驾驶员行为）的资料，以增强路线规划、监控车辆性能并预测维护需求。

在经济快速发展、电子商务蓬勃发展以及对供应链完善的关注的推动下，预计到 2032 年，亚太地区机器学习在物流行业的规模将大幅成长。随着城市化和工业成长的不断发展，亚太地区国家越来越多地转向先进的物流解决方案，以熟练地管理该地区错综复杂的供应链和大量货物。

产业生态系统分析
供应商格局
- 平台提供者
- 软体供应商
- 服务提供者
- 配销通路
- 最终用户
利润率分析
技术与创新格局
专利分析
重要新闻和倡议
监管环境
衝击力
- 成长动力
  - 进一步优化供应链营运
  - 仓储作业自动化
  - 电子商务产业的成长
  - 对增强客户体验的需求不断增长
- 产业陷阱与挑战
  - 数据品质和整合问题
  - 与遗留系统集成
成长潜力分析
波特的分析
PESTEL分析

第 4 章：竞争格局

介绍
公司市占率分析
竞争定位矩阵
战略展望矩阵

第 5 章：市场估计与预测：按组成部分，2021 - 2032 年

主要趋势
软体
服务
- 託管
- 专业的

第 6 章：市场估计与预测：按技术分类，2021 - 2032 年

主要趋势
监督学习
无监督学习

第 7 章：市场估计与预测：按组织规模，2021 - 2032 年

主要趋势
大型企业
中小企业 (SME)

第 8 章：市场估计与预测：按部署模型，2021 - 2032

主要趋势
基于云端
本地

第 9 章：市场估计与预测：按应用分类，2021 - 2032

主要趋势
库存管理
供应链规划
运输管理
仓库管理
车队管理
风险管理与安全
其他的

第 10 章：市场估计与预测：按最终用户划分，2021 - 2032 年

主要趋势
零售与电子商务
製造业
卫生保健
汽车
食品和饮料
消费品
其他的

第 11 章：市场估计与预测：按地区，2021 - 2032

主要趋势
北美洲
- 我们
- 加拿大
欧洲
- 英国
- 德国
- 法国
- 义大利
- 西班牙
- 俄罗斯
- 北欧人
- 欧洲其他地区
亚太地区
- 中国
- 印度
- 日本
- 澳洲
- 韩国
- 东南亚
- 亚太地区其他地区
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 拉丁美洲其他地区
MEA
- 阿联酋
- 南非
- 沙乌地阿拉伯
- MEA 的其余部分

第 12 章：公司简介

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

简介目录

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