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水管理におけるAIの世界市場：2025年～2032年

Global AI in Water Management Market - 2025-2032

出版日
ページ情報
英文 190 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
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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=147.05円
水管理におけるAIの世界市場：2025年～2032年
出版日: 2025年02月13日
発行: DataM Intelligence
ページ情報: 英文 190 Pages
納期: 即日から翌営業日
GIIご利用のメリット
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  • 概要
  • 目次
概要

水管理におけるAIの世界市場は、2024年に75億4,000万米ドルに達し、2032年までには538億5,000万米ドルに達すると予測され、予測期間中の2025年から2032年のCAGRは27.85%で成長する見込みです。

世界の水管理におけるAI市場は、水不足への対応や業務効率の向上を目的としたスマートテクノロジーの採用が増加していることを背景に、急速な成長を遂げています。都市化の進展、インフラの老朽化、気候変動が水管理の課題を悪化させ、人工知能（AI）ソリューションの機会を生み出しています。Siemensによると、AI駆動型テクノロジーは潜在的な問題の90％を特定するため、従来の方法よりも3倍効果的だといいます。この事実は、世界の持続可能性の目標をサポートし、UNESCOによると2050年までに55％増加すると予測される水需要の増大に対処します。

AI対応システムは、予知保全や自動漏水検知を通じて水インフラ管理に革命をもたらしています。米国環境保護庁（EPA）によると、米国では漏水による水の損失が年間総水使用量の約14％から18％を占めています。スマートセンサーと機械学習アルゴリズムにより、ユーティリティ企業は漏水を迅速に検出して対処することができ、何十億ガロンもの水を節約することができます。AIを統合した灌漑システムは、水の消費量を最大25％削減することができ、持続可能な農業を促進し、水不足が食糧生産に与える影響を緩和します。

アジア太平洋は水管理におけるAIで最も急成長している市場です。インド、中国、インドネシアなどの国々では、急速な都市化、産業の拡大、深刻な水不足がAI技術の採用を促進しています。アジア開発銀行（ADB）によると、アジアの75％以上が水不足に陥っており、同地域の人口の90％以上が居住する国々はすでに差し迫った水危機に直面しています。この地域の各国政府は、スマートな水インフラに多額の投資を行っており、インドの「Jal Shakti Abhiyan」は、AIを活用した節水イニシアチブを推進しています。

ダイナミクス

水不足と効率的資源管理

水不足は世界の危機となっています。国連の報告によると、23億人が水不足の国に住んでおり、人口増加、都市化、気候変動により、この数字は増加すると予想されています。水の使用と分配を最適化するAIの能力は、変革的な解決策を提供します。IoTデバイスと統合されたAI技術は、水質、圧力、流量をリアルタイムで監視することを可能にします。IBM Corporationによると、リアルタイムのデータ分析により、水道システムの異常への事前対応を可能にすることで、水の無駄を最大15％削減できるといいます。

配水網の監視には、クラウド情報管理によるAIベースの漏水検知システムを導入し、世界最低レベルの漏水率を達成しています。同様に、機械学習モデルは過去のデータを分析して水需要を予測し、システム障害を未然に防ぎます。例えば、EurEauは、水処理、配水、廃水収集・処理を最適化するデジタル環境とデータ処理能力の開発により、欧州市民の95％が高品質の飲料水を利用できる一方、82％が廃水処理に接続されていると報告しています。

企業の持続可能性目標を高める

米国では水道インフラの老朽化が大きな問題となっており、米国土木学会（ASCE）は、パイプラインの老朽化により毎日約60億ガロンの処理水が失われていると報告しています。この課題に対処するため、AI技術、特にデジタルツインが水道システムの挙動シミュレーションに活用されています。これにより、ユーティリティ企業は修理やアップグレードの優先順位をより効果的に決定し、業務効率と資源管理を強化することができます。さらに、AIは漏水検知と予知保全において重要な役割を果たしており、ユーティリティ企業はインフラをプロアクティブに管理してダウンタイムを削減し、最終的には水道資産の寿命を延ばすことができます。

気候変動が激化する中、AIは水管理システムを異常気象に適応させるためにますます不可欠になっています。例えば、日本の国土交通省は洪水リスクの予測にAIを採用し、水システムの災害関連障害を減らすことに成功しています。リアルタイムのデータと高度な分析を活用することで、AIは公共事業が資源配分を最適化し、気候が引き起こす課題に対する回復力を向上させるのに役立っています。このようなAIの統合は、水道サービスの信頼性と持続可能性を高めるだけでなく、増大する需要や環境圧力に対応した近代化の緊急ニーズにも対応しています。

高い導入コストを伴うデータ品質の問題

水管理におけるAI市場の統合には、その有効性と普及を妨げるいくつかの重大な抑制要因があります。データ品質の課題は最も重要です。AIシステムは高品質のデータ入力に大きく依存していますが、多くの水道事業体は、旧式のインフラや不十分なモニタリング能力により、一貫性のないデータや不完全なデータセットという課題に直面しています。このような信頼できるデータの不足は、AIモデルの予測能力を著しく制限し、水資源管理の最適化における全体的な効果を低下させる可能性があります。

さらに、特に予算上の制約が多い開発途上地域では、高い導入コストが多くの公益事業にとって障壁となっています。ハードウェア、ソフトウェア、トレーニングなど、AI技術に必要な初期投資は、組織がこれらのソリューションを採用することを躊躇させる可能性があります。さらに、既存のインフラの多くは最新のテクノロジーと互換性がないため、レガシー・システムとの統合がAI主導のプロセスへの移行を複雑にしています。最後に、AIシステムを効果的に導入・活用するための技術的スキルや知識が従業員に不足していることも多く、水道事業体がAIを活用した経営改善で直面する課題をさらに悪化させています。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • 多様な産業からの水の再利用とリサイクルの導入
      • 企業の持続可能性目標の強化
    • 抑制要因
      • 高い初期投資額
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • 持続可能な分析
  • DMIの見解

第6章 オファリング別

  • 水道メーター
  • ソリューション
    • スマート灌漑管理
    • 高度な圧力管理
    • モバイルワークフォース管理
    • ネットワーク管理
    • CISと請求
    • 漏れ検出
    • その他のソリューション
  • サービス
    • 統合と展開
    • サポートとメンテナンス
    • コンサルティング

第7章 容量別

  • 25,000L未満
  • 25,001L～50,000L
  • 50,001L～100,000L
  • 100,001L以上

第8章 技術別

  • モノのインターネット（IoT）デバイス
  • リモートセンシングと衛星画像
  • データ分析とビッグデータ
  • クラウドコンピューティングソリューション
  • その他

第9章 エンドユーザー別

  • 市営
    • 都市水管理
    • 農村給水プログラム
  • 産業
    • 発電
    • 食品・飲料
    • 医薬品
    • 繊維・皮革
    • パルプ・紙
    • 石油・ガス
    • 鉱業・金属
    • その他
  • 商業用
  • その他

第10章 サスティナビリティ分析

  • 環境分析
  • 経済分析
  • ガバナンス分析

第11章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他欧州地域
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋
  • 中東・アフリカ

第12章 競合情勢

  • 競合シナリオ
  • 市況・シェア分析
  • M&A分析

第13章 企業プロファイル

  • Honeywell International Inc
    • 会社概要
    • 製品ポートフォリオと概要
    • 財務概要
    • 主な発展
  • Schneider Electric
  • IBM Corp.
  • ABB
  • TATA Consultancy Services Limited
  • Oracle
  • SUEZ
  • Landis+Gyr
  • Siemens
  • Larsen & Toubro

第14章 付録

目次
Product Code: ICT9102

Global AI in Water Management Market reached US$ 7.54 billion in 2024 and is expected to reach US$ 53.85 billion by 2032, growing with a CAGR of 27.85% during the forecast period 2025-2032.

The global AI in water management market is experiencing rapid growth, driven by the increasing adoption of smart technologies to address water scarcity and improve operational efficiencies. Rising urbanization, aging infrastructure and climate change exacerbate water management challenges, creating opportunities for artificial intelligence (AI) solutions. According to Siemens AI-driven technologies identify 90% of potential issues, which makes it three times more effective than traditional methods. This fact supports global sustainability goals and addresses the growing water demand projected to rise by 55% by 2050, according to UNESCO.

AI-enabled systems are revolutionizing water infrastructure management through predictive maintenance and automated leak detection. According to the U.S. Environmental Protection Agency (EPA), water loss from leaks accounts for approximately 14-18% of total water usage annually in US. Smart sensors and machine learning algorithms enable utilities to detect and address leaks promptly, saving billions of gallons of water. AI-integrated irrigation systems can reduce water consumption by up to 25%, promoting sustainable agricultural practices and mitigating the impacts of water scarcity on food production.

Asia-Pacific is the fastest-growing market for AI in water management. Rapid urbanization, industrial expansion and severe water scarcity in countries like India, China and Indonesia are driving the adoption of AI technologies. According to the Asian Development Bank (ADB), over 75% of Asia is water insecure, with countries that are home to more than 90% of the region's population already facing an imminent water crisis. Governments in the region are investing heavily in smart water infrastructure, with India's "Jal Shakti Abhiyan" promoting AI-based water conservation initiatives.

Dynamics

Water Scarcity and Efficient Resource Management

Water scarcity is a growing global crisis. The United Nations reports that 2.3 billion people live in water-stressed countries, with the figure expected to rise due to population growth, urbanization and climate change. AI's ability to optimize water usage and distribution offers a transformative solution. AI technologies integrated with IoT devices allow for real-time monitoring of water quality, pressure and flow rates. According to the IBM Corporation, real-time data analytics can reduce water waste by up to 15% by enabling proactive responses to irregularities in water systems.

AI-based water leak detection system with cloud information management for monitoring its water distribution network, achieving a leakage rate, among the lowest globally. Similarly, machine learning models analyze historical data to predict water demand and preempt system failures. For instance, the EurEau reports that 95% of European citizens have access to high-quality drinking water while 82% of them are connected to wastewater treatment, by developing a digital environment and data treatment capabilities to optimize water treatment, water distribution and wastewater collection and treatment

Increasing Corporate Sustainability Goals

The aging water infrastructure in the U.S. is a significant concern, with the American Society of Civil Engineers (ASCE) reporting that approximately 6 billion gallons of treated water are lost daily due to deteriorating pipelines. To address this challenge, AI technologies, particularly digital twins, are being utilized to simulate the behavior of water systems. This allows utilities to prioritize repairs and upgrades more effectively, enhancing operational efficiency and resource management. Additionally, AI plays a critical role in leak detection and predictive maintenance, enabling utilities to proactively manage infrastructure and reduce downtime, ultimately extending the lifespan of water assets.

As climate change intensifies, AI is increasingly vital for adapting water management systems to extreme weather events. For instance, Japan's Ministry of Land, Infrastructure, Transport and Tourism employs AI to predict flood risks, successfully reducing disaster-related failures in water systems. By leveraging real-time data and advanced analytics, AI helps utilities optimize resource allocation and improve resilience against climate-induced challenges. This integration of AI not only enhances the reliability and sustainability of water services but also addresses the urgent need for modernization in response to growing demands and environmental pressures.

Data Quality Issues with High Implementation Costs

The integration of AI in the water management market presents several significant restraints that can hinder its effectiveness and widespread adoption. Data Quality Issues are paramount; AI systems rely heavily on high-quality data inputs, yet many water utilities face challenges with inconsistent or incomplete datasets due to outdated infrastructure and insufficient monitoring capabilities. This lack of reliable data can severely limit the predictive capabilities of AI models, reducing their overall effectiveness in optimizing water resource management.

Additionally, high implementation costs pose a barrier for many utilities, particularly in developing regions where budget constraints are prevalent. The initial investments required for AI technologies, including hardware, software and training, can deter organizations from adopting these solutions. Furthermore, integration with legacy systems complicates the transition to AI-driven processes, as many existing infrastructures are not compatible with modern technologies. Lastly, there is often a lack of technical skills and knowledge within the workforce to effectively implement and utilize AI systems, which further exacerbates the challenges faced by water utilities in leveraging AI for improved management.

Segment Analysis

The global AI in water management market is segmented based on offering, technology, capacity, end-user and region.

Rising Demand for AI-based Water Management in Power Generation Industry

The smart water utilities sector is rapidly evolving, driven by the need for enhanced operational efficiency and reduced water losses. Artificial Intelligence (AI) plays a crucial role in optimizing water treatment and distribution processes. Wastewater treatment plant uses AI to reduce aeration energy use by 30%. Furthermore, AI systems are instrumental in addressing the challenge of non-revenue water (NRW), which represents substantial losses in the water sector.

A notable example is a pilot project in South Africa's eThekwini Municipality, where AI helped decrease NRW in two years, demonstrating the technology's effectiveness in detecting unauthorized usage and leaks. Additionally, AI enhances customer engagement through smart billing systems that provide real-time insights into water consumption. AI-driven portals have improved customer satisfaction significantly while minimizing billing discrepancies.

The integration of AI into water management not only fosters operational improvements but also facilitates data-driven decision-making for long-term resource planning and allocation. By leveraging advanced analytics and predictive capabilities, utilities can optimize their operations, respond to demand fluctuations and ultimately contribute to sustainable water management practices..

Geographical Penetration

Availability of Advanced Technological Eco-Systemin North America

North America is leading the global AI-driven water management market, primarily due to its advanced technological ecosystem and supportive regulatory environment. The AI-integrated water systems have enhanced efficiency in urban utilities. Cities such as Las Vegas and Los Angeles are utilizing AI to optimize water distribution, resulting in significant reductions in per capita water consumption.

Furthermore, government initiatives like the EPA's Water Infrastructure Finance and Innovation Act (WIFIA) are providing essential funding for smart water projects, facilitating the adoption of AI technologies. In Canada, the "Smart Water Canada" initiative has contributed to an improvement in municipal water management efficiency since 2020.

The industrial sector is also witnessing robust AI adoption in water management. For example, GM's new system can recycle and reuse 85% of wastewater, cutting the complex's daily groundwater extraction in half while increasing operations across its North American facilities. This trend is indicative of a broader movement towards sustainable practices, driven by increasing concerns over water scarcity and aging infrastructure.

As North America continues to invest heavily in smart water solutions, it is expected to maintain its dominance in the market, with projected growth fueled by advancements in digital technologies and regulatory support aimed at enhancing efficiency and sustainability in water resource management.

Competitive Landscape

The major global players in the market include Honeywell International Inc, Schneider Electric, IBM Corp., ABB, TATA Consultancy Services Limited oracle, SUEZ, Landis+Gyr, Siemens and Larsen & Toubro.

Sustainable Analysis

The integration of artificial intelligence (AI) in water management is significantly contributing to the achievement of the United Nations Sustainable Development Goal 6, which focuses on clean water and sanitation. AI systems enhance water conservation by optimizing usage, leading to a reported 22% reduction in water wastage across various pilot projects globally, according to the International Institute for Sustainable Development (IISD).

Additionally, these systems improve energy efficiency in water treatment processes; the AI-enabled wastewater treatment solutions saved millions of kWh of water annually in pilot initiatives. By utilizing predictive analytics, AI also enhances pollution control by improving wastewater treatment efficiency, with the European Commission's Horizon 2020 initiative indicating a 15% improvement in water quality standards through AI-driven monitoring. Beyond efficiency gains, AI technologies are reshaping how utilities manage water resources. They enable real-time data analysis to predict water demand, optimize supply and reduce waste effectively.

Furthermore, AI can identify leaks and other infrastructure issues before they escalate into crises, allowing for timely repairs and maintenance. This proactive approach not only conserves water but also minimizes operational costs and environmental impacts. As these technologies continue to evolve, their role in ensuring sustainable water management becomes increasingly vital, particularly in addressing global challenges such as climate change and population growth that threaten water security.

Integration of IoT and AI in AI in Water Management

The integration of IoT (Internet of Things) and AI (Artificial Intelligence) in water management systems is transforming how utilities operate by providing real-time data and actionable insights. According to the World Economic Forum, IoT-connected sensors can reduce operational inefficiencies by up to 30% in water systems. These technologies enable automation in critical areas such as irrigation, leak detection and water quality monitoring. For instance, Israel's Mekorot National Water Company successfully reduced irrigation water usage, through the integration of AI and IoT.

Furthermore, AI enhances decision-making capabilities by analyzing large datasets, with a significant percent reduction in response times to water crises in pilot implementations. The benefits of these technologies extend beyond efficiency gains; they also facilitate better resource management and sustainability. IoT systems provide continuous monitoring that allows for early leak detection and optimized water use, which can significantly lower maintenance costs.

Additionally, AI-driven analytics help predict infrastructure failures, enabling proactive maintenance and minimizing downtime. As a result, water utilities can respond more effectively to challenges such as aging infrastructure and changing environmental conditions, ultimately leading to improved service delivery and conservation efforts across the sector.

Key Developments

  • In October 2024, FIDO AI Ltd, a software company dedicated to reducing non-revenue water (NRW) through AI-led solutions and curated water data insights, announced the successful closing of its Series B funding round. This investment will enable FIDO AI to scale its operations and continue its mission to support global water resilience.
  • In July 2023, SUEZ, a leader in sustainable water and waste services, joined forces with Schneider Electric to drive the adoption of digital solutions focused on energy efficiency, resource conservation and minimizing the carbon footprint of water cycle management systems. This collaboration emphasizes the importance of digital transformation in addressing water management challenges.
  • In June 2023, SUEZ also partnered with Northumbrian Water Ltd. (NWL) on the Hydro Powered Concentric Smart Meter Project, which was awarded the Ofwat Innovation Fund's Catalyst Stream prize. This groundbreaking project aims to revolutionize water usage monitoring and management through innovative smart metering technology.

By Offering

  • Water Meter
  • Solutions
    • Smart Irrigation Management
    • Advanced Pressure Management
    • Mobile Workforce Management
    • Network Management
    • CIS and Billing
    • Leak Detection
    • Other Solutions
  • Services
    • Integration & Deployment
    • Support & Maintenance
    • Consulting

By Capacity

  • Less than 25,000L,
  • 25,001 TO 50,000 L
  • 50,001 L TO 100,000 L
  • More than 100,001 L

By Technology

  • Internet of Things (IoT) Devices
  • Remote Sensing and Satellite Imagery
  • Data Analytics and Big Data
  • Cloud Computing Solutions
  • Others

By End-User

  • Municipal
    • Urban Water Management
    • Rural Water Supply Programs
  • Industrial
    • Power Generation
    • Food and Beverage
    • Pharmaceuticals
    • Textiles and Leather
    • Pulp and Paper
    • Oil and Gas
    • Mining and Metals
    • Others
  • Commercial
  • Others

By Region

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Spain
    • Rest of Europe
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa

Why Purchase the Report?

  • To visualize the global AI in water management market segmentation based on offering, technology, capacity, end-user and region.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points at the AI in water management market level for all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The global AI in water management market report would provide approximately 70 tables, 66 figures and 190 pages.

Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Offering
  • 3.2. Snippet by Capacity
  • 3.3. Snippet by Technology
  • 3.4. Snippet by End-User
  • 3.5. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Adoption of the Water Reuse and Recycle from Diverse Industries
      • 4.1.1.2. Increasing Corporate Sustainability Goals
    • 4.1.2. Restraints
      • 4.1.2.1. High Initial Investment
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. Sustainable Analysis
  • 5.6. DMI Opinion

6. By Offering

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
    • 6.1.2. Market Attractiveness Index, By Offering
  • 6.2. Water Meter*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Solutions
    • 6.3.1. Smart Irrigation Management
    • 6.3.2. Advanced Pressure Management
    • 6.3.3. Mobile Workforce Management
    • 6.3.4. Network Management
    • 6.3.5. CIS and Billing
    • 6.3.6. Leak Detection
    • 6.3.7. Other Solutions
  • 6.4. Services
    • 6.4.1. Integration & Deployment
    • 6.4.2. Support & Maintenance
    • 6.4.3. Consulting

7. By Capacity

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
    • 7.1.2. Market Attractiveness Index, By Capacity
  • 7.2. Less than 25,000L*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. 25,001 TO 50,000 L
  • 7.4. 50,001 L TO 100,000 L
  • 7.5. More than 100,001 L

8. By Technology

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 8.1.2. Market Attractiveness Index, By Technology
  • 8.2. Internet of Things (IoT) Devices*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Remote Sensing and Satellite Imagery
  • 8.4. Data Analytics and Big Data
  • 8.5. Cloud Computing Solutions
  • 8.6. Others

9. By End-User

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 9.1.2. Market Attractiveness Index, By End-User
  • 9.2. Municipal*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
      • 9.2.2.1. Urban Water Management
      • 9.2.2.2. Rural Water Supply Programs
  • 9.3. Industrial
    • 9.3.1. Power Generation
    • 9.3.2. Food and Beverage
    • 9.3.3. Pharmaceuticals
    • 9.3.4. Textiles and Leather
    • 9.3.5. Pulp and Paper
    • 9.3.6. Oil and Gas
    • 9.3.7. Mining and Metals
    • 9.3.8. Others
  • 9.4. Commercial
  • 9.5. Others

10. Sustainability Analysis

  • 10.1. Environmental Analysis
  • 10.2. Economic Analysis
  • 10.3. Governance Analysis

11. By Region

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 11.1.2. Market Attractiveness Index, By Region
  • 11.2. North America
    • 11.2.1. Introduction
    • 11.2.2. Key Region-Specific Dynamics
    • 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
    • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
    • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.2.7.1. US
      • 11.2.7.2. Canada
      • 11.2.7.3. Mexico
  • 11.3. Europe
    • 11.3.1. Introduction
    • 11.3.2. Key Region-Specific Dynamics
    • 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
    • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
    • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.3.7.1. Germany
      • 11.3.7.2. UK
      • 11.3.7.3. France
      • 11.3.7.4. Italy
      • 11.3.7.5. Spain
      • 11.3.7.6. Rest of Europe
  • 11.4. South America
    • 11.4.1. Introduction
    • 11.4.2. Key Region-Specific Dynamics
    • 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
    • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
    • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.4.7.1. Brazil
      • 11.4.7.2. Argentina
      • 11.4.7.3. Rest of South America
  • 11.5. Asia-Pacific
    • 11.5.1. Introduction
    • 11.5.2. Key Region-Specific Dynamics
    • 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
    • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
    • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.5.7.1. China
      • 11.5.7.2. India
      • 11.5.7.3. Japan
      • 11.5.7.4. Australia
      • 11.5.7.5. Rest of Asia-Pacific
  • 11.6. Middle East and Africa
    • 11.6.1. Introduction
    • 11.6.2. Key Region-Specific Dynamics
    • 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
    • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
    • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

  • 12.1. Competitive Scenario
  • 12.2. Market Positioning/Share Analysis
  • 12.3. Mergers and Acquisitions Analysis

13. Company Profiles

  • 13.1. Honeywell International Inc*
    • 13.1.1. Company Overview
    • 13.1.2. Product Portfolio and Description
    • 13.1.3. Financial Overview
    • 13.1.4. Key Developments
  • 13.2. Schneider Electric
  • 13.3. IBM Corp.
  • 13.4. ABB
  • 13.5. TATA Consultancy Services Limited
  • 13.6. Oracle
  • 13.7. SUEZ
  • 13.8. Landis+Gyr
  • 13.9. Siemens
  • 13.10. Larsen & Toubro

LIST NOT EXHAUSTIVE

14. Appendix

  • 14.1. About Us and Services
  • 14.2. Contact Us