查看購物車
  • Simplified Chinese
  • English
  • Japanese
封面
市場調查報告書
商品編碼
1544636

供應鏈管理市場中的機器學習、機會、成長動力、產業趨勢分析與預測，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個工作天內

價格
簡介目錄

在電子商務和數位平台擴張的推動下，供應鏈管理中的機器學習市場規模在 2024 年至 2032 年期間將以超過 29% 的複合年成長率成長。據 Hostinger 稱，電子商務市場資料將產生 5.5 兆美元的收入，到 2027 年，銷售額預計將佔全球零售業的 23%。 ，需要進行處理和分析以提高供應鏈效率。機器學習技術可以洞察消費者行為、最佳化庫存水準並簡化物流。

隨著組織應對日益複雜的供應鏈資料，對複雜資料管理系統的需求從未如此強烈。這些解決方案有助於無縫收集、儲存和分析來自不同來源的大量資料，從而實現更準確和可操作的見解。透過利用基於雲端的資料平台、資料湖和即時分析等技術，公司可以增強有效管理和利用資料的能力。這種整合提高了營運效率並支援先進的機器學習應用程式，有利於市場成長。

供應鏈管理行業中的機器學習根據組件、技術、組織規模、部署模式、應用、最終用戶和區域進行分類。

到 2032 年，服務領域將快速成長。隨著企業擴大採用這些服務，他們透過提高預測準確性和增強營運敏捷性來獲得競爭優勢。機器學習服務的整合使組織能夠預測當前趨勢、更有效地管理資源並快速回應動態條件。

到 2032 年，無監督細分市場將顯著成長，因為無監督學習演算法無需預先定義標籤即可識別資料中隱藏的模式和關係。該技術有助於從複雜且非結構化的供應鏈資料中發現見解。透過應用無監督學習，企業可以發現以前未被注意到的相關性，最佳化路線和物流，並增強供應商選擇流程。無監督學習演算法對不斷變化的資料的適應性使其對供應鏈非常有價值，其中適應新資訊和市場條件的能力至關重要。

在數位轉型和創新策略重點的推動下，歐洲供應鏈管理產業的機器學習將在 2032 年實現良好成長。歐洲國家正在大力投資研發，促進技術供應商和供應鏈專業人士之間的合作。此外，歐洲嚴格的監管環境和對資料隱私的重視正在影響機器學習解決方案的開發和部署，確保合規性，同時最大限度地提高營運效益，並增加市場價值。

目錄

第 1 章：方法與範圍

第 2 章：執行摘要

第 3 章：產業洞察

  • 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
    • 終端用戶
  • 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
      • 最佳化運輸路線
      • 提高客戶滿意度
      • 改進需求預測和庫存管理
      • 對營運效率的需求不斷成長
    • 產業陷阱與挑戰
      • 資料安全和隱私問題
      • 與現有系統的整合複雜性
  • 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 年

  • 主要趨勢
  • 本地
  • 基於雲端

第 9 章：市場估計與預測：按應用分類，2021 - 2032

  • 主要趨勢
  • 需求預測
  • 供應商關係管理（SRM）
  • 風險管理
  • 產品生命週期管理
  • 銷售和營運規劃（S 和 OP）
  • 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：按地區分類，2021 - 2032 年

  • 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
    • 沙烏地阿拉伯
    • 南非
    • 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: 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