Generative Design in Built Environment Market

Generative Design in Built Environment Market - Global Forecast to 2030

Report Code: UC 6471 Jun, 2024, by marketsandmarkets.com

The generative design in the built environment market is poised for remarkable growth, forecasted to surge from USD 0.2 billion in 2024 to a substantial USD 0.8 billion by 2030, reflecting a robust CAGR of 20.8% between 2024–2030. This trend underscores the increasing recognition and adoption of generative design methodologies in architecture, urban planning, and construction. Fueled by advancements in artificial intelligence, parametric modeling, and sustainability imperatives, the market is witnessing a transformative shift towards innovative, data-driven solutions. As architects and urban planners seek more efficient, sustainable, and aesthetically pleasing designs, the generative design sector is becoming a pivotal force in shaping the future of the built environment.

Generative Design in Built Environment Market

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Market Dynamics

Driver: Increasing demand for sustainable and energy-efficient designs

One significant driver for the global generative design in the built environment market is the increasing demand for sustainable and energy-efficient designs. With a growing emphasis on environmental consciousness, architects and urban planners are turning to generative design to optimize structures for energy efficiency, material use, and overall environmental impact. Generative design tools enable the exploration of innovative solutions that can meet stringent sustainability standards, aligning with global efforts to reduce carbon footprints and promote green building practices.

Restraint: Associated initial costs and the need for specialized expertise

A notable restraint for the adoption of generative design in the built environment is the initial cost and investment required for implementing these advanced technologies. While generative design offers long-term benefits, including efficiency gains and sustainable outcomes, the upfront costs associated with software licenses, training, and hardware infrastructure can be a barrier for smaller firms or projects with limited budgets. Overcoming this restraint requires a strategic approach in highlighting the long-term return on investment and emphasizing the potential for cost savings through optimized designs and reduced resource consumption.

Opportunity: integration of artificial intelligence (AI) and machine learning (ML) technologies

An emerging opportunity in the global generative design market for the built environment lies in integrating artificial intelligence (AI) and machine learning (ML) technologies. The ability of generative design systems to learn and adapt from historical data and project outcomes opens up possibilities for more intelligent, data-driven decision-making in the design process. This can lead to enhanced predictive capabilities, improved performance analyses, and more accurate estimations of project outcomes, providing a competitive edge for firms embracing these advanced technologies.

Challenge: Need for standardized industry practices and regulations

A key challenge facing the widespread adoption of generative design in the built environment is the need for standardized industry practices and regulations. As these technologies become more prevalent, it is necessary to establish common frameworks, guidelines, and standards to ensure interoperability, data security, and ethical use of generative design tools. Overcoming this challenge requires collaboration among industry stakeholders, policymakers, and technology providers to establish a cohesive regulatory environment that fosters innovation while addressing concerns related to data privacy, intellectual property, and ethical considerations in design processes.

Generative Design in the Built Environment Market Ecosystem

Generative Design in Built Environment Market

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By project type, the infrastructure projects segment will account for a significant market size during the forecast period.

The growth of the infrastructure projects segment is propelled by the increasing recognition of its generative design solutions to revolutionize the planning and execution of complex infrastructure endeavors. As infrastructure projects become more intricate, generative design provides a transformative approach by utilizing algorithms and parametric modeling to optimize structural configurations, improve resource efficiency, and enhance overall project performance. This trend is particularly evident in segments such as transportation, utilities, and energy infrastructure, where generative design facilitates the exploration of innovative solutions, streamlines decision-making processes, and contributes to creating more resilient and sustainable infrastructure systems. The demand for advanced and efficient designs, coupled with the potential for substantial time and cost savings, positions generative design as a valuable tool in shaping the future of infrastructure development.

By end-user, the architectural firms segment is slated to witness an impressive growth rate during the forecast period.

Architectural firms increasingly leverage generative design tools to enhance creativity, optimize building performance, and efficiently navigate complex design challenges. This technology enables architects to explore various design possibilities, considering sustainability, energy efficiency, and aesthetic appeal parameters. The trend reflects a shift towards more data-driven and innovative design processes, allowing architects to streamline workflows, iterate designs rapidly, and deliver innovative solutions that align with evolving client demands and industry standards. As generative design demonstrates its potential in fostering design innovation, architecture firms are poised to integrate these tools further into their practices, influencing a dynamic and progressive evolution in the field.

The Asia Pacific region is set to experience market expansion over the forecast period.

The growth of generative design in the built environment market across the Asia-Pacific has been marked by increasing urbanization, rapid economic development, and a growing emphasis on sustainable and innovative design solutions. Countries within APAC, such as China, Japan, Singapore, and India, are experiencing a surge in construction activities, creating a demand for advanced technologies to optimize the design and development processes. Generative design, with its ability to iteratively explore and optimize architectural and engineering solutions, aligns well with the complex challenges posed by the dynamic urban landscapes in the region. Urban planning authorities and regional architectural firms are increasingly turning to generative design tools to address issues such as efficient land use, infrastructure development, and environmental sustainability, fostering a trend of technological adoption in the built environment sector.

The region's governments recognize the potential of generative design in fostering smart cities and sustainable development, leading to increased investment in research, development, and technology infrastructure. As the awareness of generative design benefits, such as optimized resource utilization, energy efficiency, and cost-effectiveness, continues to rise, the regional market is poised for substantial growth in the coming years.

Key Market Players

The generative design in built environment solutions and service providers have implemented various organic and inorganic growth strategies, such as new product launches, product upgrades, partnerships, agreements, business expansions, and mergers and acquisitions, to strengthen their offerings in the market. Some major players in the generative design in built environment market include Altair Engineering (US), Autodesk (US), Bentley Systems (US), PTC (US), and Dassault Systèmes (France), along with SMEs and startups such as Graphisoft (Hungary), MakeT.AI (US), NodeBox (Belgium), PreOptima (US), and SrinSoft (India).

Recent Developments:

  • In November 2023, Autodesk introduced ‘Autodesk AI’, an AI-powered module within the Fusion 360 platform. The module offers a swift means of generating and evaluating multiple design options for linking two or more surfaces in built environment projects. This tool is specifically geared towards enhancing creativity and efficiency in the initial stages of the design process. Additionally, there have been significant enhancements in the ‘Configurations’ feature, allowing users to reuse parametric logic to create diverse design variations.
  • In July 2023, Singapore, a city-state known for its advanced urban solutions, incorporated generative design in its urban planning endeavors. Utilizing generative design tools combined with AI, Singapore's urban planners simulated diverse urban scenarios to forecast the consequences of various planning strategies.
  • In May 2023, PTC unveiled Creo+, a software-as-a-service (SaaS) CAD solution, during the Liveworx conference in Boston. This launch aligned with the introduction of Creo CAD's tenth version and integrated Creo 10 with innovative cloud-based tools, aiming to improve design collaboration and streamline CAD administration.

Frequently Asked Questions (FAQ):

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TABLE OF CONTENTS
 
1 INTRODUCTION 
    1.1. OBJECTIVES OF THE STUDY 
    1.2. MARKET DEFINITION 
           1.2.1. INCLUSIONS AND EXCLUSIONS
    1.3. MARKET SCOPE 
           1.3.1. MARKET SEGMENTATION
           1.3.2. REGIONS COVERED
           1.3.3. YEARS CONSIDERED FOR THE STUDY
    1.4. CURRENCY CONSIDERED 
    1.5. STAKEHOLDERS 
    1.6. SUMMARY OF CHANGES 
           1.6.1. RECESSION IMPACT
 
2 RESEARCH METHODOLOGY 
    2.1. RESEARCH DATA 
           2.1.1. SECONDARY DATA
           2.1.2. PRIMARY DATA
           2.1.3. BREAKUP OF PRIMARY PROFILES
           2.1.4. KEY INDUSTRY INSIGHTS
    2.2. MARKET BREAKUP AND DATA TRIANGULATION 
    2.3. MARKET SIZE ESTIMATION 
           2.3.1. TOP-DOWN APPROACH
           2.3.2. BOTTOM-UP APPROACH
    2.4. MARKET FORECAST 
    2.5. ASSUMPTIONS FOR THE STUDY 
    2.6. LIMITATIONS OF THE STUDY 
    2.7. IMPLICATIONS OF RECESSION ON THE GLOBAL GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET 
 
3 EXECUTIVE SUMMARY 
 
4 PREMIUM INSIGHTS 
    4.1. ATTRACTIVE OPPORTUNITIES IN THE GLOBAL GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET 
    4.2. GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY OFFERING, 2024 VS. 2030 
    4.3. GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY PROJECT TYPE, 2024 VS. 2030 
    4.4. GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY APPLICATION, 2024 VS. 2030 
    4.5. GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY END-USER, 2024 VS. 2030 
    4.6. GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY REGION 
 
5 MARKET OVERVIEW 
    5.1. INTRODUCTION 
    5.2. MARKET DYNAMICS 
           5.2.1. DRIVERS
           5.2.2. RESTRAINTS
           5.2.3. OPPORTUNITIES
           5.2.4. CHALLENGES
    5.3. EVOLUTION OF GENERATIVE DESIGN 
    5.4. SUPPLY/VALUE CHAIN ANALYSIS 
    5.5. ECOSYSTEM /MARKET MAP 
    5.6. INVESTMENT LANDSCAPE 
    5.7. CASE STUDY ANALYSIS 
           5.7.1. CASE STUDY 1
           5.7.2. CASE STUDY 2
           5.7.3. CASE STUDY 3
    5.8. TECHNOLOGY ANALYSIS 
           5.8.1. KEY TECHNOLOGIES
           5.8.2. ADJACENT TECHNOLOGIES
           5.8.3. COMPLIMENTARY TECHNOLOGIES
    5.9. TARIFF AND REGULATORY LANDSCAPE 
           5.9.1. TARIFF RELATED TO GENERATIVE DESIGN SOLUTIONS
           5.9.2. REGULATORY BODIES, GOVERNMENT AGENCIES AND OTHER ORGANIZATIONS
                    5.9.2.1. NORTH AMERICA
                    5.9.2.2. EUROPE
                    5.9.2.3. ASIA PACIFIC
                    5.9.2.4. MIDDLE EAST AND AFRICA
                    5.9.2.5. LATIN AMERICA
    5.10. PATENT ANALYSIS
    5.11. PRICING ANALYSIS
    5.12. TRADE ANALYSIS
    5.13. KEY CONFERENCES AND EVENTS, 2024-2025
    5.14. PORTER’S FIVE FORCES’ ANALYSIS
           5.14.1. THREAT FROM NEW ENTRANTS
           5.14.2. THREAT OF SUBSTITUTES
           5.14.3. BARGAINING POWER OF SUPPLIERS
           5.14.4. BARGAINING POWER OF BUYERS
           5.14.5. INTENSITY OF COMPETITION RIVALRY
    5.15. GENERATIVE DESIGN IN BUILT ENVIRONMENT TECHNOLOGY ROADMAP
           5.15.1. SHORT-TERM ROADMAP (FOR NEXT 5 YEARS)
           5.15.2. LONG-TERM ROADMAP (FOR NEXT 5+ YEARS)
    5.16. GENERATIVE DESIGN IN BUILT ENVIRONMENT BUSINESS MODELS
    5.17. TRENDS/DISRUPTIONS IMPACTING BUYER/CLIENTS
    5.18. KEY STAKEHOLDERS AND BUYING CRITERIA
           5.18.1. KEY STAKEHOLDERS IN BUYING PROCESS
           5.18.2. BUYING CRITERIA
 
6 GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY OFFERING 
    6.1. INTRODUCTION 
           6.1.1. OFFERING: GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET DRIVERS
    6.2. SOFTWARE, BY TYPE 
           6.2.1. ARCHITECTURAL DESIGN SOFTWARE
                    6.2.1.1. CONCEPTUAL DESIGN TOOLS
                    6.2.1.2. 3D MODELING SOFTWARE
                    6.2.1.3. RENDERING AND VISUALIZATION TOOLS
                    6.2.1.4. OTHERS
           6.2.2. CONSTRUCTION SIMULATION SOFTWARE
                    6.2.2.1. CONSTRUCTION PROCESS OPTIMIZATION TOOLS
                    6.2.2.2. PROJECT SCHEDULING AND PLANNING TOOLS
                    6.2.2.3. SITE LOGISTICS SIMULATION TOOLS
                    6.2.2.4. COST ESTIMATION & ANALYSIS TOOLS
                    6.2.2.5. SAFETY SIMULATION PLATFORMS
                    6.2.2.6. OTHERS
           6.2.3. BUILDING INFORMATION MODELING (BIM) SOFTWARE
                    6.2.3.1. BIM AUTHORING TOOLS
                    6.2.3.2. CLASH DETECTION & COORDINATION SOFTWARE
                    6.2.3.3. BIM COLLABORATION PLATFORMS
                    6.2.3.4. OTHERS
           6.2.4. URBAN PLANNING AND DESIGN SOFTWARE
                    6.2.4.1. MASTER PLANNING TOOLS
                    6.2.4.2. ZONING & LAND USE PLANNING SOFTWARE
                    6.2.4.3. TRANSIT-ORIENTED DEVELOPMENT (TOD) PLATFORMS
                    6.2.4.4. URBAN DESIGN VISUALIZATION SOFTWARE
                    6.2.4.5. OTHERS
           6.2.5. PARAMETRIC DESIGN SOFTWARE
                    6.2.5.1. ALGORITHMIC DESIGN TOOLS
                    6.2.5.2. PARAMETRIC MODELING TOOLS
                    6.2.5.3. COMPUTATIONAL DESIGN PLATFORMS
                    6.2.5.4. OTHERS
           6.2.6. OTHER SOFTWARE
    6.3. SOFTWARE, BY DEPLOYMENT MODE 
           6.3.1. CLOUD
                    6.3.1.1. PUBLIC CLOUD
                    6.3.1.2. PRIVATE CLOUD
                    6.3.1.3. HYBRID CLOUD
           6.3.2. ON-PREMISES
    6.4. SERVICES 
           6.4.1. PROFESSIONAL SERVICES
                    6.4.1.1. SYSTEM INTEGRATION & IMPLEMENTATION SERVICES
                    6.4.1.2. TRAINING & CONSULTING SERVICES
                    6.4.1.3. SUPPORT & MAINTENANCE SERVICES
           6.4.2. MANAGED SERVICES
 
7 GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY PROJECT TYPE 
    7.1. INTRODUCTION 
           7.1.1. PROJECT TYPE: MARKET DRIVERS
    7.2. CONSTRUCTION PROJECTS 
           7.2.1. RESIDENTIAL CONSTRUCTION
           7.2.2. COMMERCIAL BUILDINGS
           7.2.3. INDUSTRIAL FACILITIES
           7.2.4. GOVERNMENT BUILDINGS
    7.3. INFRASTRUCTURE PROJECTS 
           7.3.1. TRANSPORTATION INFRASTRUCTURE
           7.3.2. UTILITIES & ENERGY
           7.3.3. PUBLIC SPACES & PARKS
    7.4. GREEN BUILDING PROJECTS 
           7.4.1. LEED-CERTIFIED BUILDINGS
           7.4.2. NET-ZERO ENERGY PROJECTS
           7.4.3. ECO-DISTRICT PROJECTS
 
8 GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY APPLICATION 
    8.1. INTRODUCTION 
           8.1.1. APPLICATION: MARKET DRIVERS
    8.2. PRODUCT DESIGN AND DEVELOPMENT 
           8.2.1. ARCHITECTURAL PRODUCT DESIGN
           8.2.2. CONSTRUCTION PRODUCT DESIGN
           8.2.3. URBAN INFRASTRUCTURE PRODUCT DESIGN
           8.2.4. OTHERS
    8.3. COST OPTIMIZATION 
           8.3.1. CONSTRUCTION COST ESTIMATION
           8.3.2. LIFE CYCLE COST OPTIMIZATION
           8.3.3. PROJECT BUDGETING AND CONTROL
           8.3.4. OTHERS
    8.4. EFFICIENCY AND WORKFLOW OPTIMIZATION 
           8.4.1. CONSTRUCTION WORKFLOW OPTIMIZATION
           8.4.2. PROJECT MANAGEMENT EFFICIENCY
           8.4.3. OTHERS
    8.5. ADAPTIVE DESIGN AND RESPONSIVE PLANNING 
           8.5.1. ADAPTIVE ARCHITECTURE
           8.5.2. URBAN PLANNING RESPONSIVENESS
           8.5.3. OTHERS
    8.6. SUSTAINABILITY AND ENVIRONMENTAL IMPACT 
           8.6.1. GREEN BUILDING DESIGN
           8.6.2. ENVIRONMENTAL IMPACT ASSESSMENT
           8.6.3. RENEWABLE ENERGY INTEGRATION
           8.6.4. OTHERS
    8.7. OTHER APPLICATIONS 
 
9 GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY END-USER 
    9.1. INTRODUCTION 
           9.1.1. END-USER: MARKET DRIVERS
    9.2. ARCHITECTURE FIRMS 
           9.2.1. LARGE ARCHITECTURAL PRACTICES
           9.2.2. MEDIUM-SIZED ARCHITECTURAL FIRMS
           9.2.3. SMALL AND BOUTIQUE ARCHITECTURAL STUDIOS
    9.3. CONSTRUCTION COMPANIES 
           9.3.1. GENERAL CONTRACTORS
           9.3.2. SPECIALIZED CONSTRUCTION FIRMS
           9.3.3. BUILDING CONTRACTORS
    9.4. REAL ESTATE DEVELOPERS 
           9.4.1. RESIDENTIAL DEVELOPERS
           9.4.2. COMMERCIAL DEVELOPERS
           9.4.3. INDUSTRIAL DEVELOPERS
    9.5. ENGINEERING FIRMS 
           9.5.1. CIVIL ENGINEERING FIRMS
           9.5.2. STRUCTURAL ENGINEERING FIRMS
           9.5.3. ENVIRONMENTAL ENGINEERING FIRMS
    9.6. OTHER END-USERS 
 
10 GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET, BY REGION 
     10.1. INTRODUCTION 
     10.2. NORTH AMERICA 
               10.2.1. NORTH AMERICA: MARKET DRIVERS
               10.2.2. NORTH AMERICA: IMPACT OF RECESSION
               10.2.3. UNITED STATES
               10.2.4. CANADA
     10.3. EUROPE 
               10.3.1. EUROPE: MARKET DRIVERS
               10.3.2. EUROPE: IMPACT OF RECESSION
               10.3.3. UK
               10.3.4. GERMANY
               10.3.5. FRANCE
               10.3.6. ITALY
               10.3.7. SPAIN
               10.3.8. REST OF EUROPE
     10.4. ASIA PACIFIC 
               10.4.1. ASIA PACIFIC: GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET DRIVERS
               10.4.2. ASIA PACIFIC: IMPACT OF RECESSION
               10.4.3. CHINA
               10.4.4. INDIA
               10.4.5. JAPAN
               10.4.6. SOUTH KOREA
               10.4.7. SINGAPORE
               10.4.8. AUSTRALIA & NEW ZEALAND
               10.4.9. REST OF ASIA PACIFIC
     10.5. MDDLE EAST AND AFRICA 
               10.5.1. MDDLE EAST AND AFRICA: MARKET DRIVERS
               10.5.2. MDDLE EAST AND AFRICA: IMPACT OF RECESSION
               10.5.3. GCC
               10.5.4. SOUTH AFRICA
               10.5.5. TURKEY
               10.5.6. REST OF MDDLE EAST AND AFRICA
     10.6. LATIN AMERICA 
               10.6.1. LATIN AMERICA: GENERATIVE DESIGN IN BUILT ENVIRONMENT MARKET DRIVERS
               10.6.2. LATIN AMERICA: IMPACT OF RECESSION
               10.6.3. BRAZIL
               10.6.4. MEXICO
               10.6.5. ARGENTINA
               10.6.6. REST OF LATIN AMERICA
 
11 COMPETITIVE LANDSCAPE 
     11.1. OVERVIEW 
     11.2. STRATEGIES ADOPTED BY KEY PLAYERS 
     11.3. REVENUE ANALYSIS FOR KEY PLAYERS 
               11.3.1. BUSINESS SEGMENT REVENUE ANALYSIS
     11.4. MARKET SHARE ANALYSIS 
     11.5. BRAND/PRODUCT COMPARATIVE ANALYSIS 
     11.6. COMPANY EVALUATION MATRIX, 2023 
               11.6.1. STARS
               11.6.2. EMERGING LEADERS
               11.6.3. PERVASIVE PLAYERS
               11.6.4. PARTICIPANTS
               11.6.5. COMPANY FOOTPRINT
     11.7. START-UP/SME EVALUATION MATRIX, 2023 
               11.7.1. PROGRESSIVE COMPANIES
               11.7.2. RESPONSIVE COMPANIES
               11.7.3. DYNAMIC COMPANIES
               11.7.4. STARTING BLOCKS
               11.7.5. COMPETITIVE BENCHMARKING
     11.8. VALUATION AND FINANCIAL METRICS OF KEY VENDORS 
     11.9. COMPETITIVE SCENARIO AND TRENDS 
               11.9.1. PRODUCT LAUNCHES AND ENHANCEMENTS
               11.9.2. DEALS
               11.9.3. OTHERS
 
12 COMPANY PROFILES 
     12.1. INTRODUCTION 
     12.2. KEY PLAYERS 
               12.2.1. ALTAIR ENGINEERING
               12.2.2. ANSYS
               12.2.3. AUTODESK
               12.2.4. BENTLEY SYSTEMS
               12.2.5. CARACOL
               12.2.6. DASSAULT SYSTÈMES
               12.2.7. DESKTOP METAL
               12.2.8. DIABATIX
               12.2.9. ESI GROUP
               12.2.10. HEXAGON
               12.2.11. NTOPOLOGY
               12.2.12. PARAMATTERS
               12.2.13. PTC
     12.3. OTHER KEY PLAYERS 
               12.3.1. ARCHISOFT
               12.3.2. ARUP GROUP
               12.3.3. CLEAR EDGE 3D
               12.3.4. EVOLVE LAB
               12.3.5. GRAPHISOFT
               12.3.6. MAKET.AI
               12.3.7. NODEBOX
               12.3.8. PREOPTIMA
               12.3.9. RHINO
               12.3.10. SOLIQUID
               12.3.11. SRINSOFT
               12.3.12. VECTORWORKS

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