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物流における生成AI市場：タイプ別、コンポーネント別、展開モード別、用途別、エンドユーザー別、地域別 - 世界の動向分析、競合情勢および予測（2019年～2031年）

Generative AI in Logistics Market, By Type; By Component; By Deployment Mode; By Application; By End User; By Region, Global Trend Analysis, Competitive Landscape & Forecast, 2019-2031

出版日
ページ情報
英文 511 Pages
納期
2～3営業日
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=146.08円
物流における生成AI市場：タイプ別、コンポーネント別、展開モード別、用途別、エンドユーザー別、地域別 - 世界の動向分析、競合情勢および予測（2019年～2031年）
出版日: 2025年01月08日
発行: Blueweave Consulting
ページ情報: 英文 511 Pages
納期: 2～3営業日
GIIご利用のメリット
  • 全表示
  • 概要
  • 目次
概要

世界の物流における生成AI市場は、サプライチェーンプロセスを最適化するための自動化とAI技術の採用が増加し、ロジスティクス業務における意思決定能力を強化する必要性が高まっていることから、活況を呈しています。

世界の物流における生成AIの市場規模は、2024年に11億米ドルとなりました。2025年から2031年にかけての予測期間中、44.20%の堅調なCAGRで拡大し、2031年には156億米ドルに達すると予測されています。手順を標準化し、ラストマイル配送を改善するための人工知能（AI）への投資の増加は、世界の物流における生成AI市場の主要な成長要因の1つです。物流業界は、サプライチェーンの自動化、需要予測、倉庫管理、在庫管理、ルート最適化など、さまざまな方法で生成AIの恩恵を受け、企業関係者が情報に基づいた選択を即座に行えるようになります。

AI技術への投資の増加とAI技術の進化は、世界の物流における生成AI市場に有利な成長機会をもたらすと予測されます。AIモデルは現在、物流業務におけるIoTデバイス、GPS、その他のセンサーから生成される膨大な量のデータを活用できるようになっており、これらのデータを使用してシステムを訓練し、精度の高い予測と最適化を生成することができます。さらに、機械学習（ML）アルゴリズム、自然言語処理（NLP）、ニューラルネットワークの進歩は、膨大なデータセットを分析し、意思決定を自動化する生成AIの能力を常に向上させています。こうした進歩は、物流企業にとって生成AIをより身近で効果的なものとし、この分野での急速な導入につながっています。

地政学的緊張の激化は、世界の物流における生成AI市場の成長を促進する可能性があります。世界のサプライチェーンは、貿易制限、国境閉鎖、輸送遅延のために地政学的紛争によって混乱します。こうした混乱は、これらの障害に対処するため、物流業界における生成AIの使用を後押ししました。ルートの最適化、需要変動の予測、他のサプライヤーやルートの発見を通じて、生成AIはこうした中断を予測し、軽減するために活用されています。しかし、地政学的紛争は、AIシステムの訓練に必要なリアルタイムの消費者データが乏しいため、AIモデルの精度に影響を及ぼす可能性があり、生成AI業界にとって深刻な障害となる可能性もあります。

当レポートでは、世界の物流における生成AI市場について調査し、市場の概要とともに、タイプ別、コンポーネント別、展開モード別、用途別、エンドユーザー別、地域別動向、競合情勢、および市場に参入する企業のプロファイルなどを提供しています。

目次

第1章 調査の枠組み

第2章 エグゼクティブサマリー

第3章 世界の物流における生成AI市場の洞察

  • 業界バリューチェーン分析
  • DROC分析
    • 成長促進要因
    • 成長抑制要因
    • 機会
    • 課題
  • 技術の進歩/最近の動向
  • 規制の枠組み
  • ポーターのファイブフォース分析

第4章 世界の物流における生成AI市場：マーケティング戦略

第5章 世界の物流における生成AI市場：地域分析

  • 世界の物流における生成AI市場、地域分析、2024年
  • 世界の物流における生成AI市場、市場の魅力分析、2024年～2031年

第6章 世界の物流における生成AI市場概要

  • 市場規模と予測、2019年～2031年
  • 市場シェアと予測
    • タイプ別
    • コンポーネント別
    • 展開モード別
    • 用途別
    • エンドユーザー別
    • 地域別

第7章 北米の物流における生成AI市場

第8章 欧州の物流における生成AI市場

第9章 アジア太平洋の物流における生成AI市場

第10章 ラテンアメリカの物流における生成AI市場

第11章 中東・アフリカの物流における生成AI市場

第12章 競合情勢

  • 主要参入企業とその提供内容のリスト
  • 物流企業における世界生成AI市場シェア分析、2024年
  • 経営パラメータ別競合ベンチマーキング
  • 主要な戦略的展開（合併、買収、提携）

第13章 地政学的緊張の高まりが世界の物流における生成AI市場に与える影響

第14章 企業プロファイル（企業概要、財務マトリクス、競合情勢、主要人員、主な競合企業、連絡先、戦略的展望、SWOT分析）

  • Blue Yonder
  • C. H. Robinson
  • FedEx Corp
  • Google Cloud
  • IBM
  • Microsoft
  • PackageX
  • Salesforce
  • Deutsche Post AG
  • Schneider Electric
  • A.P. Moller-Maersk
  • その他

第15章 主要な戦略的提言

第16章 調査手法

目次
Product Code: BWC25017

Global Generative AI in Logistics Market Zooming 14X to Touch USD 16 Billion by 2031

Global Generative AI in Logistics Market is flourishing because of the rising adoption of automation and AI technologies to optimize supply chain processes and growing need for enhanced decision-making capabilities in logistics operations.

BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated Global Generative AI in Logistics Market size at USD 1.10 billion in 2024. During the forecast period between 2025 and 2031, BlueWeave expects Global Generative AI in Logistics Market size to expand at a robust CAGR of 44.20% reaching a value of USD 15.60 billion by 2031. Increasing investments in artificial intelligence (AI) to standardize procedures and improve last-mile delivery is one of the key growth drivers for Global Generative AI in Logistics Market. The logistics industry benefits from generative AI in a number of ways, including supply chain automation, demand forecasting, warehousing and inventory management, and route optimization, which enables business actors to make informed choices instantly.

Opportunity - Advancements in AI Technology and Data Availability

Rising investments in and evolution of AI technologies are projected to present lucrative growth opportunities for Global Generative AI in Logistics Market. AI models are now able to leverage vast amounts of data being generated from IoT devices, GPS, and other sensors in logistics operations that can be used to train these systems and generate highly accurate predictions and optimizations. Furthermore, advancements in machine learning (ML) algorithms, natural language processing (NLP), and neural networks are constantly improving the ability of generative AI to analyze vast datasets and automate decision-making. These advancements make generative AI more accessible and effective for logistics companies, leading to their rapid adoption across the sector.

Impact of Escalating Geopolitical Tensions on Global Generative AI in Logistics Market

Intensifying geopolitical tensions could propel the growth of Global Generative AI in Logistics Market. The global supply chain is disrupted by geopolitical conflicts because of trade restrictions, border closures, and delays in transit. These disruptions pushed the use of generative AI in the logistics industry to address these obstacles. Through route optimization, demand fluctuation predictions, and the discovery of other suppliers and routes, generative AI is being utilized to anticipate and lessen these interruptions. Geopolitical conflicts, however, may also present serious obstacles for the generative AI industry because of the scarcity of real-time consumer data needed to train these AI systems, which might affect the accuracy of AI models.

Route Optimization Leads Global Generative AI Logistics Market

The route optimization segment holds the largest share of Global Generative AI in Logistics Market. In the logistics industry, generative AI is frequently used to improve routes by analyzing historical data, current traffic conditions, and other variables. In order to cut down on delivery times and transportation expenses, the analysis is then utilized to create effective transportation strategies. The demand forecasting segment also covers substantial market share. Supply chain managers may use generative AI to automate ordering plans to keep inventory levels up to date and forecast future trends based on historical data.

North America Dominates Global Generative AI in Logistics Market

North America holds a major market share in Global Generative AI in Logistics Market. The adoption of generative AI in the logistics sector is directly fueled by the presence of industry giants in this field, such as Google, AWS, OpenAI, and IBM in the region. Logistics companies in the United States are employing modern technologies, such as generative AI, for numerous objectives, such as tracking customer behavior and historical sales data, optimizing production planning, and conducting risk anticipation. Such cases increase the logistics industry's operational resilience and productivity, which encourages this sector to incorporate generative AI into their operations.

Competitive Landscape

The major industry players of global Generative AI in Logistics market include Blue Yonder, C. H. Robinson, FedEx Corp., Google Cloud, IBM, Microsoft, PackageX, Salesforce, Deutsche Post AG, Schneider Electric, and A.P. Moller - Maersk. The presence of high number of companies intensify the market competition as they compete to gain a significant market share. These companies employ various strategies, including mergers and acquisitions, partnerships, joint ventures, license agreements, and new product launches to further enhance their market share.

The in-depth analysis of the report provides information about growth potential, upcoming trends, and Global Generative AI in Logistics Market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in Global Generative AI in Logistics Market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyzes the growth drivers, challenges, and competitive dynamics of the market.

Table of Contents

1. Research Framework

  • 1.1. Research Objective
  • 1.2. Product Overview
  • 1.3. Market Segmentation

2. Executive Summary

3. Global Generative AI in Logistics Market Insights

  • 3.1. Industry Value Chain Analysis
  • 3.2. DROC Analysis
    • 3.2.1. Growth Drivers
      • 3.2.1.1. Rising Adoption of Automation and AI Technologies to Optimize Supply Chain Processes
      • 3.2.1.2. Growing Need for Enhanced Decision-Making Capabilities in Logistics Operations
      • 3.2.1.3. Increasing Use of Predictive Analytics for Demand Forecasting and Route Optimization
    • 3.2.2. Restraints
      • 3.2.2.1. High Implementation Costs of Generative AI Solutions for Logistics Companies
      • 3.2.2.2. Limited AI Expertise and Skilled Workforce to Operate and Manage AI Technologies
    • 3.2.3. Opportunities
      • 3.2.3.1. Integration of Generative AI with IoT, Blockchain, and Robotics to Enhance Supply Chain Efficiency
      • 3.2.3.2. Development of AI-driven Autonomous Vehicles and Drones for Logistics Operations
      • 3.2.3.3. Growing Adoption of Generative AI in Warehouse Management and Inventory Optimization
    • 3.2.4. Challenges
      • 3.2.4.1. Managing Data Quality and Standardization Across Fragmented Supply Chain Networks.
      • 3.2.4.2. Data Privacy and Cybersecurity Concerns in Handling Sensitive Logistics Data
  • 3.3. Technological Advancements/Recent Developments
  • 3.4. Regulatory Framework
  • 3.5. Porter's Five Forces Analysis
    • 3.5.1. Bargaining Power of Suppliers
    • 3.5.2. Bargaining Power of Buyers
    • 3.5.3. Threat of New Entrants
    • 3.5.4. Threat of Substitutes
    • 3.5.5. Intensity of Rivalry

4. Global Generative AI in Logistics Market: Marketing Strategies

5. Global Generative AI in Logistics Market: Geographical Analysis

  • 5.1. Global Generative AI in Logistics Market, Geographical Analysis, 2024
  • 5.2. Global Generative AI in Logistics Market, Market Attractiveness Analysis, 2024-2031

6. Global Generative AI in Logistics Market Overview

  • 6.1. Market Size & Forecast, 2019-2031
    • 6.1.1. By Value (USD Billion)
  • 6.2. Market Share & Forecast
    • 6.2.1. By Type
      • 6.2.1.1. Variational Autoencoder (VAE)
      • 6.2.1.2. Generative Adversarial Networks (GANs)
      • 6.2.1.3. Recurrent Neural Networks (RNNs)
      • 6.2.1.4. Long Short-Term Memory (LSTM) networks
      • 6.2.1.5. Others
    • 6.2.2. By Component
      • 6.2.2.1. Software
      • 6.2.2.2. Services
    • 6.2.3. By Deployment Mode
      • 6.2.3.1. Cloud
      • 6.2.3.2. On-premises
    • 6.2.4. By Application
      • 6.2.4.1. Route Optimization
      • 6.2.4.2. Demand Forecasting
      • 6.2.4.3. Warehouse & Inventory Management
      • 6.2.4.4. Supply Chain Automation
      • 6.2.4.5. Predictive Maintenance
      • 6.2.4.6. Risk Management
      • 6.2.4.7. Customized Logistics Solutions
      • 6.2.4.8. Others
    • 6.2.5. By End User
      • 6.2.5.1. Road Transportation
      • 6.2.5.2. Railway Transportation
      • 6.2.5.3. Aviation
      • 6.2.5.4. Shipping & Ports
    • 6.2.6. By Region
      • 6.2.6.1. North America
      • 6.2.6.2. Europe
      • 6.2.6.3. Asia Pacific (APAC)
      • 6.2.6.4. Latin America (LATAM)
      • 6.2.6.5. Middle East and Africa (MEA)

7. North America Generative AI in Logistics Market

  • 7.1. Market Size & Forecast, 2019-2031
    • 7.1.1. By Value (USD Billion)
  • 7.2. Market Share & Forecast
    • 7.2.1. By Type
    • 7.2.2. By Component
    • 7.2.3. By Deployment Mode
    • 7.2.4. By Application
    • 7.2.5. By End User
    • 7.2.6. By Country
      • 7.2.6.1. United States
      • 7.2.6.1.1. By Type
      • 7.2.6.1.2. By Component
      • 7.2.6.1.3. By Deployment Mode
      • 7.2.6.1.4. By Application
      • 7.2.6.1.5. By End User
      • 7.2.6.2. Canada
      • 7.2.6.2.1. By Type
      • 7.2.6.2.2. By Component
      • 7.2.6.2.3. By Deployment Mode
      • 7.2.6.2.4. By Application
      • 7.2.6.2.5. By End User

8. Europe Generative AI in Logistics Market

  • 8.1. Market Size & Forecast, 2019-2031
    • 8.1.1. By Value (USD Billion)
  • 8.2. Market Share & Forecast
    • 8.2.1. By Type
    • 8.2.2. By Component
    • 8.2.3. By Deployment Mode
    • 8.2.4. By Application
    • 8.2.5. By End User
    • 8.2.6. By Country
      • 8.2.6.1. Germany
      • 8.2.6.1.1. By Type
      • 8.2.6.1.2. By Component
      • 8.2.6.1.3. By Deployment Mode
      • 8.2.6.1.4. By Application
      • 8.2.6.1.5. By End User
      • 8.2.6.2. United Kingdom
      • 8.2.6.2.1. By Type
      • 8.2.6.2.2. By Component
      • 8.2.6.2.3. By Deployment Mode
      • 8.2.6.2.4. By Application
      • 8.2.6.2.5. By End User
      • 8.2.6.3. Italy
      • 8.2.6.3.1. By Type
      • 8.2.6.3.2. By Component
      • 8.2.6.3.3. By Deployment Mode
      • 8.2.6.3.4. By Application
      • 8.2.6.3.5. By End User
      • 8.2.6.4. France
      • 8.2.6.4.1. By Type
      • 8.2.6.4.2. By Component
      • 8.2.6.4.3. By Deployment Mode
      • 8.2.6.4.4. By Application
      • 8.2.6.4.5. By End User
      • 8.2.6.5. Spain
      • 8.2.6.5.1. By Type
      • 8.2.6.5.2. By Component
      • 8.2.6.5.3. By Deployment Mode
      • 8.2.6.5.4. By Application
      • 8.2.6.5.5. By End User
      • 8.2.6.6. Belgium
      • 8.2.6.6.1. By Type
      • 8.2.6.6.2. By Component
      • 8.2.6.6.3. By Deployment Mode
      • 8.2.6.6.4. By Application
      • 8.2.6.6.5. By End User
      • 8.2.6.7. Russia
      • 8.2.6.7.1. By Type
      • 8.2.6.7.2. By Component
      • 8.2.6.7.3. By Deployment Mode
      • 8.2.6.7.4. By Application
      • 8.2.6.7.5. By End User
      • 8.2.6.8. The Netherlands
      • 8.2.6.8.1. By Type
      • 8.2.6.8.2. By Component
      • 8.2.6.8.3. By Deployment Mode
      • 8.2.6.8.4. By Application
      • 8.2.6.8.5. By End User
      • 8.2.6.9. Rest of Europe
      • 8.2.6.9.1. By Type
      • 8.2.6.9.2. By Component
      • 8.2.6.9.3. By Deployment Mode
      • 8.2.6.9.4. By Application
      • 8.2.6.9.5. By End User

9. Asia Pacific Generative AI in Logistics Market

  • 9.1. Market Size & Forecast, 2019-2031
    • 9.1.1. By Value (USD Billion)
  • 9.2. Market Share & Forecast
    • 9.2.1. By Type
    • 9.2.2. By Component
    • 9.2.3. By Deployment Mode
    • 9.2.4. By Application
    • 9.2.5. By End User
    • 9.2.6. By Country
      • 9.2.6.1. China
      • 9.2.6.1.1. By Type
      • 9.2.6.1.2. By Component
      • 9.2.6.1.3. By Deployment Mode
      • 9.2.6.1.4. By Application
      • 9.2.6.1.5. By End User
      • 9.2.6.2. India
      • 9.2.6.2.1. By Type
      • 9.2.6.2.2. By Component
      • 9.2.6.2.3. By Deployment Mode
      • 9.2.6.2.4. By Application
      • 9.2.6.2.5. By End User
      • 9.2.6.3. Japan
      • 9.2.6.3.1. By Type
      • 9.2.6.3.2. By Component
      • 9.2.6.3.3. By Deployment Mode
      • 9.2.6.3.4. By Application
      • 9.2.6.3.5. By End User
      • 9.2.6.4. South Korea
      • 9.2.6.4.1. By Type
      • 9.2.6.4.2. By Component
      • 9.2.6.4.3. By Deployment Mode
      • 9.2.6.4.4. By Application
      • 9.2.6.4.5. By End User
      • 9.2.6.5. Australia & New Zealand
      • 9.2.6.5.1. By Type
      • 9.2.6.5.2. By Component
      • 9.2.6.5.3. By Deployment Mode
      • 9.2.6.5.4. By Application
      • 9.2.6.5.5. By End User
      • 9.2.6.6. Indonesia
      • 9.2.6.6.1. By Type
      • 9.2.6.6.2. By Component
      • 9.2.6.6.3. By Deployment Mode
      • 9.2.6.6.4. By Application
      • 9.2.6.6.5. By End User
      • 9.2.6.7. Malaysia
      • 9.2.6.7.1. By Type
      • 9.2.6.7.2. By Component
      • 9.2.6.7.3. By Deployment Mode
      • 9.2.6.7.4. By Application
      • 9.2.6.7.5. By End User
      • 9.2.6.8. Singapore
      • 9.2.6.8.1. By Type
      • 9.2.6.8.2. By Component
      • 9.2.6.8.3. By Deployment Mode
      • 9.2.6.8.4. By Application
      • 9.2.6.8.5. By End User
      • 9.2.6.9. Vietnam
      • 9.2.6.9.1. By Type
      • 9.2.6.9.2. By Component
      • 9.2.6.9.3. By Deployment Mode
      • 9.2.6.9.4. By Application
      • 9.2.6.9.5. By End User
      • 9.2.6.10. Rest of APAC
      • 9.2.6.10.1. By Type
      • 9.2.6.10.2. By Component
      • 9.2.6.10.3. By Deployment Mode
      • 9.2.6.10.4. By Application
      • 9.2.6.10.5. By End User

10. Latin America Generative AI in Logistics Market

  • 10.1. Market Size & Forecast, 2019-2031
    • 10.1.1. By Value (USD Billion)
  • 10.2. Market Share & Forecast
    • 10.2.1. By Type
    • 10.2.2. By Component
    • 10.2.3. By Deployment Mode
    • 10.2.4. By Application
    • 10.2.5. By End User
    • 10.2.6. By Country
      • 10.2.6.1. Brazil
      • 10.2.6.1.1. By Type
      • 10.2.6.1.2. By Component
      • 10.2.6.1.3. By Deployment Mode
      • 10.2.6.1.4. By Application
      • 10.2.6.1.5. By End User
      • 10.2.6.2. Mexico
      • 10.2.6.2.1. By Type
      • 10.2.6.2.2. By Component
      • 10.2.6.2.3. By Deployment Mode
      • 10.2.6.2.4. By Application
      • 10.2.6.2.5. By End User
      • 10.2.6.3. Argentina
      • 10.2.6.3.1. By Type
      • 10.2.6.3.2. By Component
      • 10.2.6.3.3. By Deployment Mode
      • 10.2.6.3.4. By Application
      • 10.2.6.3.5. By End User
      • 10.2.6.4. Peru
      • 10.2.6.4.1. By Type
      • 10.2.6.4.2. By Component
      • 10.2.6.4.3. By Deployment Mode
      • 10.2.6.4.4. By Application
      • 10.2.6.4.5. By End User
      • 10.2.6.5. Rest of LATAM
      • 10.2.6.5.1. By Type
      • 10.2.6.5.2. By Component
      • 10.2.6.5.3. By Deployment Mode
      • 10.2.6.5.4. By Application
      • 10.2.6.5.5. By End User

11. Middle East & Africa Generative AI in Logistics Market

  • 11.1. Market Size & Forecast, 2019-2031
    • 11.1.1. By Value (USD Billion)
  • 11.2. Market Share & Forecast
    • 11.2.1. By Type
    • 11.2.2. By Component
    • 11.2.3. By Deployment Mode
    • 11.2.4. By Application
    • 11.2.5. By End User
    • 11.2.6. By Country
      • 11.2.6.1. Saudi Arabia
      • 11.2.6.1.1. By Type
      • 11.2.6.1.2. By Component
      • 11.2.6.1.3. By Deployment Mode
      • 11.2.6.1.4. By Application
      • 11.2.6.1.5. By End User
      • 11.2.6.2. UAE
      • 11.2.6.2.1. By Type
      • 11.2.6.2.2. By Component
      • 11.2.6.2.3. By Deployment Mode
      • 11.2.6.2.4. By Application
      • 11.2.6.2.5. By End User
      • 11.2.6.3. Qatar
      • 11.2.6.3.1. By Type
      • 11.2.6.3.2. By Component
      • 11.2.6.3.3. By Deployment Mode
      • 11.2.6.3.4. By Application
      • 11.2.6.3.5. By End User
      • 11.2.6.4. Kuwait
      • 11.2.6.4.1. By Type
      • 11.2.6.4.2. By Component
      • 11.2.6.4.3. By Deployment Mode
      • 11.2.6.4.4. By Application
      • 11.2.6.4.5. By End User
      • 11.2.6.5. South Africa
      • 11.2.6.5.1. By Type
      • 11.2.6.5.2. By Component
      • 11.2.6.5.3. By Deployment Mode
      • 11.2.6.5.4. By Application
      • 11.2.6.5.5. By End User
      • 11.2.6.6. Nigeria
      • 11.2.6.6.1. By Type
      • 11.2.6.6.2. By Component
      • 11.2.6.6.3. By Deployment Mode
      • 11.2.6.6.4. By Application
      • 11.2.6.6.5. By End User
      • 11.2.6.7. Algeria
      • 11.2.6.7.1. By Type
      • 11.2.6.7.2. By Component
      • 11.2.6.7.3. By Deployment Mode
      • 11.2.6.7.4. By Application
      • 11.2.6.7.5. By End User
      • 11.2.6.8. Rest of MEA
      • 11.2.6.8.1. By Type
      • 11.2.6.8.2. By Component
      • 11.2.6.8.3. By Deployment Mode
      • 11.2.6.8.4. By Application
      • 11.2.6.8.5. By End User

12. Competitive Landscape

  • 12.1. List of Key Players and Their Offerings
  • 12.2. Global Generative AI in Logistics Company Market Share Analysis, 2024
  • 12.3. Competitive Benchmarking, By Operating Parameters
  • 12.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships)

13. Impact of Escalating Geopolitical Tensions on Global Generative AI in Logistics Market

14. Company Profile (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, SWOT Analysis)

  • 14.1. Blue Yonder
  • 14.2. C. H. Robinson
  • 14.3. FedEx Corp
  • 14.4. Google Cloud
  • 14.5. IBM
  • 14.6. Microsoft
  • 14.7. PackageX
  • 14.8. Salesforce
  • 14.9. Deutsche Post AG
  • 14.10. Schneider Electric
  • 14.11. A.P. Moller - Maersk
  • 14.12. Other Prominent Players

15. Key Strategic Recommendations

16. Research Methodology

  • 16.1. Qualitative Research
    • 16.1.1. Primary & Secondary Research
  • 16.2. Quantitative Research
  • 16.3. Market Breakdown & Data Triangulation
    • 16.3.1. Secondary Research
    • 16.3.2. Primary Research
  • 16.4. Breakdown of Primary Research Respondents, By Region
  • 16.5. Assumptions & Limitations

*Financial information of non-listed companies can be provided as per availability.

**The segmentation and the companies are subject to modifications based on in-depth secondary research for the final deliverable