表紙:建設向け人工知能(AI)の世界市場-2023年~2030年
市場調査レポート
商品コード
1360030

建設向け人工知能(AI)の世界市場-2023年~2030年

Global Artificial Intelligence (AI) in Construction Market - 2023-2030

出版日: | 発行: DataM Intelligence | ページ情報: 英文 182 Pages | 納期: 約2営業日

● お客様のご希望に応じて、既存データの加工や未掲載情報(例:国別セグメント)の追加などの対応が可能です。  詳細はお問い合わせください。

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=151.55円
建設向け人工知能(AI)の世界市場-2023年~2030年
出版日: 2023年10月11日
発行: DataM Intelligence
ページ情報: 英文 182 Pages
納期: 約2営業日
ご注意事項 :
本レポートは最新情報反映のため適宜更新し、内容構成変更を行う場合があります。ご検討の際はお問い合わせください。
  • 全表示
  • 概要
  • 目次
概要

概要:

世界の建設向け人工知能(AI)市場は、2022年に6億米ドルに達し、2023年から2030年の予測期間中にCAGR 33.7%で成長し、2030年には78億米ドルに達すると予測されています。

建設業界におけるAI研究開発への投資の増加は、技術革新と新しいAI搭載ツールやソリューションの開発を促進しています。都市化の進行は建設プロジェクトの需要を増大させており、AIはこうした需要をより効率的に満たすのに役立ちます。AIは、建設プロジェクトが建築基準法や規制を確実に順守するよう支援し、コストのかかる法的問題のリスクを低減します。

例えば、2022年11月7日、トリンブルとエクシン・テクノロジーズは自律型建設測量技術の開発で協業しています。このソリューションは、ボストン・ダイナミクスのSpotロボット、ExynAIを搭載したExynのExynPak、トリンブルのX7トータルステーションを組み合わせ、複雑な建設環境内での完全自律ミッションを可能にします。収集されたデータは分析され、品質と進捗状況を監視するためのビルディング・インフォメーション・モデルと比較することができます。

アジア太平洋地域は、世界の建設向け人工知能(AI)市場の1/4以上を占める成長地域のひとつであり、同地域の多くの国では急速な都市化が進み、建設プロジェクトの急増につながっています。AIは、こうした大規模プロジェクトを効率的に管理・最適化するのに役立ちます。自動化と人工知能(AI)技術は、労働集約的な反復作業を処理することで、このギャップを埋める可能性があります。この地域の政府は、交通、エネルギー、住宅のインフラ建設に多額の投資を行っています。

ダイナミクス:

生産コスト削減による市場促進

AI機能を備えたロボットや機械は、時間のかかる反復作業を自動化できるため、手作業の需要とそれに伴う人件費を下げることができます。AIアルゴリズムはデータ分析を活用し、消耗品、設備、労働力の配分方法を最適化することで、無駄を省き、より効果的な資源利用を実現することができます。人工知能は建設機械の健康状態を追跡し、メンテナンスの必要性を予測することで、コストのかかる故障やダウンタイムを減らすことができます。

アクセンチュアの最近の調査によると、AIの導入により、建設業界の利益は2035年までに71%増加する可能性があります。アクセンチュアは、建設業界でAIを採用することの潜在的なメリットの大きさを強調しています。AIには、建設プロジェクトの効率化、コスト削減、安全性の向上、意思決定の強化を実現する力があります。

エマソンの調査では、設計・建設段階で作成された初期データの30%が、プロジェクト終了までに失われていることが明らかになっています。これにより、プロジェクトチームは実際の費用と予算額を比較し、プロジェクトが財政的制約の範囲内に収まるようにすることができます。コストを最適化または削減できる領域を特定することで、大幅なコスト削減とプロジェクトの収益性向上につながります。継続的なコスト・モニタリングは、潜在的なコスト超過や財務リスクを洞察し、プロアクティブなリスク管理戦略を可能にします。

安全対策強化の需要

AI、特に機械学習アルゴリズムは、過去のデータを分析して潜在的な安全問題を予測することができます。パターンや動向を認識することで、AIは事故や安全でない状況を予測し、予防措置を講じることができます。高度なセンサーやモノのインターネット(IoT)デバイスの普及により、建設現場では作業員の活動、機械の操作、環境などに関する豊富なリアルタイム情報が利用できるようになり、これらのデータをAIで処理・分析することで、潜在的な危険やセキュリティ侵害を発見することができます。

2021年のNCCERによると、ロボティック・プロセス・オートメーション(RPA)は、機械との相互作用を通じて作業の自動化を可能にします。建設業では、AIによる自動化が危険な作業を排除し、手作業に伴うリスクを軽減するのに役立ちます。大規模なデータセットを分析し、インテリジェントな結論を導き出すAIの能力は、機械、作業指示、サプライチェーンを評価するために活用され、この予測分析能力は、ワークフローの最適化と安全対策に貴重な洞察を提供します。

市場における機械学習・深層学習アルゴリズムの進歩の高まり

機械学習・深層学習アルゴリズムの進歩により、AIシステムは膨大な量の建設データを分析できるようになり、パターンの特定、プロセスの最適化、意思決定のための貴重な洞察を提供する能力が高まっています。デバイスやネットワークのエッジでローカルにデータを処理するエッジAIは、遠隔地やリソースに制約のある建設環境におけるAIシステムの応答性と効率を高めます。

例えば、2021年8月17日には、人工知能建設技術の新興企業であるTogal.aiが市場に参入し、建設における見積もりプロセスに革命を起こすことを目指しています。Togal.aiは、各部屋の大きさを正確に測定し、建設費の価格設定を行うことで、見積もりプロセスを自動化・迅速化できるとしています。

限られた履歴データと労働力の蓄積

AIシステムは、高品質で関連性の高いデータに大きく依存しています。建設業界では、多様な情報源、データ形式の違い、AIアルゴリズムを学習するための限られた過去のデータにより、クリーンで一貫性のあるデータを入手することが困難な場合があります。建設プロジェクトには機密情報や専有情報が含まれることが多いです。このデータをサイバー脅威から保護し、データプライバシー規制の遵守を確保することは、AIソリューションを導入する際の重要な課題となり得ます。

Associated Builders and Contractorsによると、2022年には約66万5,000人の建設労働者が不足すると予測されており、この予測はABCによる業界の現状調査と、インフレや建設支出などを考慮した独自のモデルに基づいています。120万人の建設労働者が職を失うと予測されることが、この不足の大きな要因であり、この減少が、業界におけるすでに危機的な技能労働者不足を悪化させています。

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
      • 生産コスト削減による市場促進
      • 安全対策強化の需要
      • 市場における機械学習・深層学習アルゴリズムの進歩の高まり
    • 抑制要因
      • 限られた履歴データと労働力の蓄積
    • 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
  • ロシア・ウクライナ戦争の影響分析
  • DMIの見解

第6章 COVID-19分析

第7章 提供別

  • ソリューション
  • サービス

第8章 展開タイプ別

  • クラウド
  • オンプレミス

第9章 組織規模別

  • 中小企業
  • 大企業

第10章 エンドユーザー別

  • 住宅
  • 公共施設
  • 商業施設
  • その他

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
    • ロシア
    • その他欧州
  • 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

  • Building System Planning, Inc.
    • 企業概要
    • 製品ポートフォリオと説明
    • 財務概要
    • 主な動向
  • SAP SE
  • Autodesk, Inc.
  • NVIDIA Corporation
  • International Business Machines Corp
  • Microsoft Corporation, Inc.
  • Oracle Corporation
  • Dassault Systems SE
  • Aurora Computer Services Limited
  • PTC Inc.

第14章 付録

目次
Product Code: ICT7001

Overview:

Global Artificial Intelligence (AI) in Construction Market reached US$ 0.6 billion in 2022 and is expected to reach US$ 7.8 billion by 2030, growing with a CAGR of 33.7% during the forecast period 2023-2030.

Increased investment in AI research and development within the construction industry is driving innovation and the development of new AI-powered tools and solutions. The ongoing trend of urbanization is increasing the demand for construction projects and AI can help meet these demands more efficiently. AI assists in ensuring that construction projects adhere to building codes and regulations, reducing the risk of costly legal issues.

For instance, on 7 November 2022, Trimble and Exyn Technologies are collaborating on the development of autonomous construction surveying technology. The solution will combine Boston Dynamics' Spot robot, Exyn's ExynPak powered by ExynAI and Trimble's X7 total station to enable fully autonomous missions within complex construction environments. The collected data can be analyzed and compared to Building Information Models for quality and progress monitoring.

Asia-Pacific is among the growing regions in the global artificial intelligence (AI) in construction market covering more than 1/4th of the market and many countries in the region are experiencing rapid urbanization, leading to a surge in construction projects. AI can help manage and optimize these large-scale projects efficiently. Automation and artificial intelligence (AI) technologies may bridge this gap by handling labor-intensive, repetitive tasks. Governments in the area are making significant investments in the construction of transportation, energy and housing infrastructure.

Dynamics:

Reducing Production Costs Drives the Market

Robots and machinery with AI capabilities can automate time-consuming and repetitive operations, hence lowering the demand for manual labor and the accompanying labor costs. AI algorithms may utilize data analysis to optimize how supplies, equipment and labor are allocated, resulting in less waste and more effective resource use. Artificial intelligence can track the health of construction equipment and forecast maintenance requirements, reducing costly breakdowns and downtime.

According to a recent study by Accenture, the adoption of AI can potentially increase the construction industry's profits by 71% by 2035. Accenture highlights the significant potential benefits of adopting AI in the construction industry. AI has the power to increase efficiency, reduce costs, improve safety and enhance decision-making in construction projects.

Emerson's study revealed that 30% of initial data created during the design and construction phases is lost by the time the project ends. It allows project teams to compare actual expenses with the budgeted costs, ensuring that the project remains within financial constraints. Identifying areas where costs can be optimized or reduced can lead to significant cost savings and improved project profitability. Continuous cost monitoring provides insights into potential cost overruns or financial risks, enabling proactive risk management strategies.

The Demand for Enhanced Safety Measures

AI, particularly machine learning algorithms, can analyze historical data to predict potential safety issues. By recognizing patterns and trends, AI can anticipate accidents or unsafe conditions, allowing for preventive measures. An abundance of real-time information about worker activities, machine operation, the environment and more is made available at construction sites because of the spread of sophisticated sensors and Internet of Things (IoT) devices and this data can be processed and analyzed by AI to find potential dangers and security breaches.

According to NCCER in 2021, Robotic process automation(RPA) enables the automation of tasks through interactions with machines. In construction, AI-driven automation helps eliminate dangerous tasks, reducing the risks associated with manual labor. AI's ability to analyze large datasets and draw intelligent conclusions is leveraged to assess machinery, work orders and supply chains and this predictive analytics capability provides valuable insights into workflow optimization and safety measures..

Rising Advancements in Machine Learning and Deep Learning Algorithms in the Market

Advancements in machine learning and deep learning algorithms have enabled AI systems to analyze vast amounts of construction data, making them more capable of identifying patterns, optimizing processes and providing valuable insights for decision-making. Edge AI which processes data locally on devices or at the edge of the network, enhances the responsiveness and efficiency of AI systems in remote or resource-constrained construction environments.

For instance, on 17 August 2021, Togal.ai, an Artificial Intelligence construction technology startup, entered the market, aiming to revolutionize the estimating process in construction. The company claims its software can automate and expedite the estimating process by accurately measuring the size of each room and pricing the cost of construction, a task that typically takes weeks but can be completed in seconds with Togal.

Limited Historical Data and Storage of Labours

AI systems rely heavily on high-quality and relevant data. In construction, obtaining clean and consistent data can be challenging due to the diverse sources of information, variations in data formats and limited historical data for training AI algorithms. Construction projects often involve sensitive and proprietary information. Protecting this data from cyber threats and ensuring compliance with data privacy regulations can be a significant challenge when implementing AI solutions.

According to the Associated Builders and Contractors in 2022, there is a shortage of about 665,000 construction workers is anticipated and this forecast is based on a study of the industry's state by ABC and a unique model that takes into account things like inflation and construction spending. The predicted 1.2 million construction employees who are anticipated to abandon their positions is a significant factor in this shortfall and this attrition exacerbates the already critical shortage of skilled labor in the industry.

Segment Analysis:

The global artificial intelligence (AI) in construction market is segmented based on offerings, deployment type, organization size, end-user and region.

Scalability of Cloud-Based AI Platforms Boosts the Growth of the Market

Cloud-based AI platforms can easily scale to accommodate the needs of construction projects of varying sizes and this scalability allows construction companies to adapt AI resources to their specific requirements. Cloud solutions often work on a pay-as-you-go basis, reducing the need for substantial capital expenditures upfront. Because of their affordability, AI technology is now available to a wider spectrum of construction enterprises.

For instance, on 9 September 2023, U.S. technology company Nvidia formed partnerships with two major Indian conglomerates, Reliance Industries and Tata Group, to establish artificial intelligence infrastructure in India. Nvidia will provide the necessary computing power to Reliance for constructing a cloud-based AI infrastructure platform, with Jio overseeing infrastructure management and customer engagement. Additionally, Tata Consultancy Services in collaboration with Nvidia, will develop generative AI applications and a supercomputer.

Geographical Penetration:

Adoption of Digital Platform Boosts the Market

North America is dominating the global artificial Intelligence in construction market and the region is home to some of the world's leading tech companies and research institutions, making it a hub for AI development and this access to cutting-edge technology fuels innovation in construction. The construction industry is undergoing a digital transformation, with AI playing a crucial role. Companies are increasingly recognizing the value of AI-driven solutions for efficiency, cost savings and competitiveness.

For instance, on 06 May 2021, Procore Technologies, a prominent construction management software provider, acquired INDUS.AI, a company known for its AI-powered analytics platform tailored for the construction industry and this acquisition enhances Procore's capabilities by introducing computer vision technology, aiming to improve efficiency, safety and profitability for owners, general contractors and specialty contractors.

Competitive Landscape

The major global players in the market include: Building System Planning, Inc., SAP SE, Autodesk, Inc., NVIDIA Corporation, International Business Machines Corp, Microsoft Corporation, Inc. oracle Corporation, Dassault Systems SE, Aurora Computer Services Limited and PTC Inc.

COVID-19 Impact Analysis

The pandemic accelerated the construction industry's digital transformation efforts. To minimize disruptions caused by lockdowns and social distancing measures, many construction companies turned to AI and digital technologies to enable remote work, collaboration and project management. AI-powered tools for project planning, scheduling and monitoring became essential in ensuring projects continued despite the challenges posed by the pandemic.

Safety concerns heightened during the pandemic, leading to an increased focus on AI-driven safety solutions. AI-based systems for monitoring social distancing, mask-wearing and site occupancy helped construction companies adhere to health and safety guidelines. AI also played a role in contactless site access control and temperature screening. The pandemic exposed vulnerabilities in global supply chains, affecting the availability and delivery of construction materials.

The pandemic exposed vulnerabilities in global supply chains, affecting the availability and delivery of construction materials. AI-powered supply chain management tools helped construction firms adapt to changing conditions by providing real-time visibility into material availability and alternative sourcing options. Travel restrictions and limited on-site personnel, AI-enabled remote inspection and monitoring solutions gained importance. Drones, equipped with AI-powered cameras, were used for site inspections and progress monitoring.

AI Impact

AI analyzes architectural designs to optimize energy efficiency, material use and cost-effectiveness, leading to environmentally friendly and cost-saving designs. AI algorithms can assess potential risks and uncertainties in construction projects, helping project managers make informed decisions. AI-driven project management tools can optimize project schedules, allocate resources efficiently and manage project budgets, reducing delays and cost overruns.

In order to provide insights into project performance and enable data-driven decision-making, AI can evaluate project data in real-time. Real-time monitoring of building sites by AI-powered technologies can assist in identifying safety risks and avert accidents. AI can analyze historical data to predict potential risks and issues, allowing for proactive risk mitigation. AI-based computer vision systems can perform real-time quality inspections, ensuring that construction work meets quality standards and reducing the cost of rework.

For instance, on 14 December 2022, PCL Construction entered a multi-year partnership with AI Clearing, focusing on its Solar division. This partnership aims to enhance the management of solar projects by implementing AI Clearing's AI Surveyor solution. AI Surveyor is a construction technology platform powered by artificial intelligence and advanced GIS analytics. It automates the progress reporting of construction infrastructure, using drone-captured data to provide daily progress reports, monitor Key Performance Indicators and flag potential deviations.

Russia- Ukraine War Impact

The conflict may disrupt supply chains for construction materials and equipment, leading to delays and shortages. AI-driven supply chain management systems may become more critical in navigating these disruptions by providing real-time visibility into material availability and alternative sourcing options. The geopolitical instability resulting from the war can create economic uncertainty, affecting construction projects' funding and investment.

The conflict's impact on the global economy can affect construction projects worldwide. Economic slowdowns can lead to budget cuts for construction projects, impacting the adoption of AI technologies. International conflicts can strain research collaborations between countries, affecting the exchange of knowledge and expertise in AI for construction. Government priorities in both Russia and Ukraine may shift towards defense and security, potentially reducing investments in civil infrastructure and technology sectors, including AI for construction.

By Offerings

  • Solutions
  • Services

By Deployment Type

  • Cloud
  • On-Premise

By Organization Size

  • Small and Medium-sized Enterprises
  • Large Enterprises

By End-User

  • Residential
  • Institutional
  • Commercials
  • Others

By Region

  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Russia
    • 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

Key Developments

  • In April 2023, Autodesk, Inc. and VietNam National Construction Consultants signed a Memorandum of Understanding to establish a strategic partnership. Autodesk, Inc. will provide guidance and assistance to VC Group in adopting digital design and construction consultancy technologies, in alignment with the Vietnamese Government's decision to promote the use of Building Information Modeling (BIM) in the construction sector.
  • In June 2022, Siemens and NVIDIA have expanded their partnership to enable the industrial metaverse and enhance the use of AI-driven digital twin technology in industrial automation. They plan to connect Siemens Xcelerator, an open digital business platform, with NVIDIA Omniverse, a platform for 3D design and collaboration.
  • In July 2020, Autodesk, Inc. signed a definitive agreement to acquire Pype, a cloud-based construction project management software provider. Pype's suite of software uses artificial intelligence and machine learning to automate critical construction workflows, such as submittals and closeouts.

Why Purchase the Report?

  • To visualize the global artificial intelligence (AI) in construction market segmentation based on offerings, deployment type, organization size, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of artificial intelligence (AI) in construction market-level with 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 artificial intelligence (AI) in construction market report would provide approximately 69 tables, 65 figures and 182 Pages.

Target Audience 2023

  • 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 Offerings
  • 3.2. Snippet by Deployment Type
  • 3.3. Snippet by Organization Size
  • 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. Reducing Production Costs Drives the Market
      • 4.1.1.2. The Demand for Enhanced Safety Measures
      • 4.1.1.3. Rising Advancements in Machine Learning and Deep Learning Algorithms in the Market
    • 4.1.2. Restraints
      • 4.1.2.1. Limited Historical Data and Storage of Labours
    • 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. Russia-Ukraine War Impact Analysis
  • 5.6. DMI Opinion

6. COVID-19 Analysis

  • 6.1. Analysis of COVID-19
    • 6.1.1. Scenario Before COVID
    • 6.1.2. Scenario During COVID
    • 6.1.3. Scenario Post COVID
  • 6.2. Pricing Dynamics Amid COVID-19
  • 6.3. Demand-Supply Spectrum
  • 6.4. Government Initiatives Related to the Market During Pandemic
  • 6.5. Manufacturers Strategic Initiatives
  • 6.6. Conclusion

7. By Offerings

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
    • 7.1.2. Market Attractiveness Index, By Offerings
  • 7.2. Solutions*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Services

8. By Deployment Type

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 8.1.2. Market Attractiveness Index, By Deployment Type
  • 8.2. Cloud*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. On-Premise

9. By Organization Size

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 9.1.2. Market Attractiveness Index, By Organization Size
  • 9.2. Small and Medium-sized Enterprises*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Large Enterprises

10. By End-User

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.1.2. Market Attractiveness Index, By End-User
  • 10.2. Residential*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Institutional
  • 10.4. Commercials
  • 10.5. Others

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 Offerings
    • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 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. U.S.
      • 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 Offerings
    • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 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. Russia
      • 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 Offerings
    • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 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 Offerings
    • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 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 Offerings
    • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
    • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 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. Building System Planning, 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. SAP SE
  • 13.3. Autodesk, Inc.
  • 13.4. NVIDIA Corporation
  • 13.5. International Business Machines Corp
  • 13.6. Microsoft Corporation, Inc.
  • 13.7. Oracle Corporation
  • 13.8. Dassault Systems SE
  • 13.9. Aurora Computer Services Limited
  • 13.10. PTC Inc.

LIST NOT EXHAUSTIVE

14. Appendix

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