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市场调查报告书
商品编码
1544636

供应链管理市场中的机器学习、机会、成长动力、产业趋势分析与预测，2024-2032

Machine Learning in Supply Chain Management Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032
出版日期: | 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

在电子商务和数位平台扩张的推动下，供应链管理中的机器学习市场规模在 2024 年至 2032 年期间将以超过 29% 的复合年增长率成长。据 Hostinger 称，电子商务市场资料将产生 5.5 兆美元的收入，到 2027 年，销售额预计将占全球零售业的 23%。 ，需要进行处理和分析以提高供应链效率。机器学习技术可以洞察消费者行为、优化库存水准并简化物流。

随着组织应对日益复杂的供应链资料，对复杂资料管理系统的需求从未如此强烈。这些解决方案有助于无缝收集、储存和分析来自不同来源的大量资料，从而实现更准确和可操作的见解。透过利用基于云端的资料平台、资料湖和即时分析等技术，公司可以增强有效管理和利用资料的能力。这种整合提高了营运效率并支援先进的机器学习应用程序，有利于市场成长。

供应链管理行业中的机器学习根据组件、技术、组织规模、部署模式、应用、最终用户和区域进行分类。

到 2032 年，服务领域将快速成长。随着企业越来越多地采用这些服务，他们透过提高预测准确性和增强营运敏捷性来获得竞争优势。机器学习服务的整合使组织能够预测当前趋势、更有效地管理资源并快速回应动态条件。

到 2032 年，无监督细分市场将显着成长，因为无监督学习演算法无需预先定义标籤即可识别资料中隐藏的模式和关係。该技术有助于从复杂且非结构化的供应链资料中发现见解。透过应用无监督学习，企业可以发现以前未被注意到的相关性，优化路线和物流，并增强供应商选择流程。无监督学习演算法对不断变化的资料的适应性使其对供应链非常有价值，其中适应新资讯和市场条件的能力至关重要。

在数位转型和创新策略重点的推动下，欧洲供应链管理产业的机器学习将在 2032 年实现良好成长。欧洲国家正在大力投资研发，促进技术供应商和供应链专业人士之间的合作。此外，欧洲严格的监管环境和对资料隐私的重视正在影响机器学习解决方案的开发和部署，确保合规性，同时最大限度地提高营运效益，并增加市场价值。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

  • 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
    • 终端用户
  • 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
      • 优化运输路线
      • 提高客户满意度
      • 改进需求预测和库存管理
      • 对营运效率的需求不断增长
    • 产业陷阱与挑战
      • 资料安全和隐私问题
      • 与现有系统的整合复杂性
  • 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 年

  • 主要趋势
  • 本地
  • 基于云端

第 9 章：市场估计与预测：按应用分类，2021 - 2032

  • 主要趋势
  • 需求预测
  • 供应商关係管理（SRM）
  • 风险管理
  • 产品生命週期管理
  • 销售和营运规划（S 和 OP）
  • 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：按地区划分，2021 - 2032 年

  • 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
    • 沙乌地阿拉伯
    • 南非
    • 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: 10171

Machine Learning in Supply Chain Management Market Size will grow at over 29% CAGR during 2024-2032, driven by the expansion of e-commerce and digital platforms. According to Hostinger, the e-commerce market is anticipated to generate $5.5 trillion, with sales expected to account for 23% of the global retail sector by 2027. Digital platforms, with their vast reach and customer interaction points, create a wealth of data that needs to be processed and analyzed to enhance supply chain efficiency. Machine learning technologies provide insights into consumer behavior, optimizing inventory levels, and streamlining logistics.

As organizations grapple with increasingly complex supply chain data, the need for sophisticated data management systems has never been greater. These solutions facilitate the seamless collection, storage, and analysis of vast amounts of data from diverse sources, enabling more accurate and actionable insights. By leveraging technologies such as cloud-based data platforms, data lakes, and real-time analytics, companies can enhance their ability to manage and utilize data effectively. This integration improves operational efficiency and supports advanced machine learning applications, favoring market growth.

The machine learning in supply chain management industry is classified based on component, technology, organization size, deployment mode, application, end-user, and region.

The services segment will grow rapidly through 2032. By leveraging machine learning algorithms, companies can optimize inventory management, streamline logistics, and mitigate risks associated with supply chain disruptions. As businesses increasingly adopt these services, they gain a competitive edge through improved accuracy in forecasting and enhanced operational agility. The integration of machine learning services enables organizations to anticipate current trends, manage resources more effectively, and respond swiftly to dynamic conditions.

The unsupervised segment will record significant growth through 2032, as unsupervised learning algorithms identify hidden patterns and relationships within data without predefined labels. This technology is instrumental in discovering insights from complex and unstructured supply chain data. By applying unsupervised learning, businesses can uncover previously unnoticed correlations, optimize routing and logistics, and enhance supplier selection processes. The adaptability of unsupervised learning algorithms to evolving data makes them highly valuable for supply chains, where the ability to adapt to new information and market conditions is crucial.

Europe machine learning in supply chain management industry will witness decent growth through 2032, driven by the strategic focus on digital transformation and innovation. European countries are investing heavily in R and D, fostering collaborations between technology providers and supply chain professionals. Additionally, Europe's stringent regulatory environment and emphasis on data privacy are shaping the development and deployment of machine learning solutions, ensuring compliance while maximizing operational benefits, and adding to market value.

Table of Contents

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 providers
    • 3.2.2 Software provider
    • 3.2.3 Service providers
    • 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 Optimization of transportation routes
      • 3.8.1.2 Enhanced customer satisfaction
      • 3.8.1.3 Improved demand forecasting and inventory management
      • 3.8.1.4 Growing need for operational efficiency
    • 3.8.2 Industry pitfalls and challenges
      • 3.8.2.1 Data security and privacy concerns
      • 3.8.2.2 Integration complexity with existing systems
  • 3.9 Growth potential analysis
  • 3.10 Porter's analysis
  • 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 On-premises
  • 8.3 Cloud-based

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

  • 9.1 Key trends
  • 9.2 Demand forecasting
  • 9.3 Supplier relationship management (SRM)
  • 9.4 Risk management
  • 9.5 Product lifecycle management
  • 9.6 Sales and operations planning (S and OP)
  • 9.7 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 Saudi Arabia
    • 11.6.3 South Africa
    • 11.6.4 Rest of MEA

Chapter 12 Company Profiles

  • 12.1 Amazon Web Services, Inc. (AWS)
  • 12.2 Blue Yonder Group, Inc.
  • 12.3 C.H. Robinson Worldwide, Inc.
  • 12.4 Convoy, Inc.
  • 12.5 Coupa Software Inc.
  • 12.6 DHL Supply Chain
  • 12.7 FedEx Corporation
  • 12.8 Flexport, Inc.
  • 12.9 Google LLC
  • 12.10 Infor, Inc.
  • 12.11 International Business Machines Corporation (IBM)
  • 12.12 Locus Robotics Corporation
  • 12.13 Manhattan Associates, Inc.
  • 12.14 Microsoft Corporation
  • 12.15 Oracle Corporation
  • 12.16 SAP SE
  • 12.17 Trimble Inc.
  • 12.18 Uber Technologies, Inc.
  • 12.19 United Parcel Service, Inc.
  • 12.20 Waymo LLC