Generative Design Market Size, Share & Trends Analysis Report By Deployment (On-premise, Cloud), By Application (Product design and Development, Cost optimization), By End-User Vertical (Automotive, Aerospace and Defense, Architecture and Construction, Industrial Manufacturing, Others) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2022-2030

Market Overview

The global generative design market size is projected to reach USD 7 billion by 2030, from USD 2 billion in 2021, and is anticipated to register a CAGR of 16 % during 2022–2030.

A design exploration approach is known as generative design. Designers or engineers enter design goals, as well as characteristics like performance or spatial requirements, materials, production techniques, and cost limitations, into generative design software. The software swiftly generates design alternatives by exploring all conceivable variants of a solution. It tests and learns what works and what doesn't with each iteration.

Market Dynamics

Global generative design market driver

Advanced technologies to spur market growth of the global generative design market

Companies in a variety of industries are looking for ways to improve production efficiency throughout their manufacturing processes in order to maximize output while minimizing costs. Companies are rapidly using new technology to drive product innovation and production efficiency in order to achieve this.

Advanced technologies minimize industry fragmentation, increase efficiency, and lower the high costs of insufficient interoperability. Advancements in big data, IoT, AI, and machine learning, among other technologies, have propelled this expansion.

Building Information Modeling (BIM) is quickly becoming a standard in the architecture and construction industries. BIM is a virtual design tool for developing a set of interconnected policies, processes, and technologies, as well as a technique for managing significant building design and project data in digital format, with major advantages over traditional computer-aided design (CAD).

The market's growth is being aided by additive manufacturing technology, which provides a variety of opportunities in the production, design, and performance of unique architectural shapes, construction systems, and materials. It is a more innovative, quicker, more agile approach to product development and manufacturing.

The planning and designing stage consumes a large portion of a budget. As a result, any delays in this process result in financial losses and lower production quality. To overcome such obstacles, solid design and planning are essential, which aid in the production's completion on schedule and on budget.

AI is assisting construction companies with better visualizing and planning complex plumbing, mechanical, and electrical tasks that can be easily conceptualized within a 3D modeling design. Logistical issues can be resolved even when planning, saving time and money during the construction phase.

Global generative design market restraint

Complexity in using generative design software to plummet demand for the global generative design market.

Advancements in technology necessitate either equipping existing workers with the technology or replacing them with professional and experienced workers. This is largely due to the difficulty of using the software and other technical tools.

There is currently a skills gap preventing industries from optimizing their production processes. As a result of a scarcity of qualified and skilled applicants, manufacturers across industries are having difficulty operating and deploying generative design software and other technologies.

The use of automated design and planning processes, operation of generative design tools, and job planning are among the core knowledge areas where there is a deficit. There is also a significant disconnect between students' and employers' opinions of the significance of design abilities, problem-solving skills, and expertise in the aforementioned areas. Because of the intricacy involved in using the software, this underscores the fact that the talent gap is a major stumbling block to the market's growth.

Furthermore, even in developing nations, SMEs have a poor adoption rate since training and educating existing designers, or replacing them with professional and competent designers, raises the cost of manufacturing. This prevents SMEs from using such software since they cannot afford the costs. As a result, this aspect is limiting market expansion.

The manufacturing industry's skills requirements have shifted from largely manual work to increasingly skill-intensive programming and control of complicated machinery. Employees with lower qualifications, on the other hand, are at significant risk of being replaced unless they can be retrained.

Global generative design market opportunity

Ai technologies to create profitable opportunities in the global generative design market

The generative design focuses on an enhanced approach to engineering that was previously unavailable in digital contexts. The procedure closely resembles an evolutionary approach to design, taking into account all of the necessary traits. In addition, when high-performance computing and the cloud are combined, the industry has seen previously unimaginable capabilities.

Companies like Under Armour, Airbus, Black & Decker, and others in many areas are rapidly embracing generative design as a trend that is gradually altering the engineering sector's future. It has enabled engineers to delegate the task of finding the best solutions to a set of limitations to the software, considerably enhancing the engineer's creativity.

Furthermore, examples of generative design in many industries are becoming more widespread, and engineers are increasingly incorporating the tool into their processes. These software's creating the future continually augment the way goods are made and engineered. The end of product design and manufacturing is rapidly evolving.

Modern technologies are replacing traditional ways. Digital simulation and analysis software has advanced to the point that designs may now be reviewed in seconds or less. With no operator interaction, complex algorithms can automatically alter the geometry of a part between simulations. These new generative design tools can also explore a considerably bigger universe of possible solutions, comparing the outcomes of millions of simulations to narrow in on a design that can yield the best combination of specified features, thanks to AI approaches.

Regional Analysis

Significant growth in north america and europe 

Based on region, the global network encryption market share is divided into North America, Europe, Asia-Pacific, and the Rest of the World (Latin America and the Middle East & Africa)

The North American region dominated the global generative design market, with revenue forecasted to grow at a CAGR of 15% to USD 3 billion by 2030.

The generative design market is likely to be led by North America, including the United States and Canada. An increase in demand for storage solutions and industrial automation and a greater focus on minimizing infrastructure cost, a growing requirement for business insights, and real-time data availability are driving the market.

GM was the first major automaker in North America to use Autodesk, generative design software, to outperform traditional design optimization techniques in terms of weight reduction. By 2023, GM hopes to have at least 20 electric or fuel-cell vehicles on the market, with generative design assisting in resolving various issues by allowing for lighter vehicles and a shorter supply chain.

The Europe region accounts for the second-largest share of the global generative design market, with a CAGR of 17% expected to generate USD 2 billion in sales by 2030.

The European automotive sector has risen to a significant position in the global automobile industry. Europe is seeing widespread adoption of 3D printing technology for design formulation and R&D applications in the automobile industry, thanks to major multinational car OEMs.

Top automakers such as Mercedes-Benz, Audi, BMW, Jaguar, Land Rover, Volkswagen, and others have created a potential market for 3D printing and technology in Europe. Over the last 25 years, the BMW Group has used 3D printing technology to design various auto parts. It printed nearly 200,000 3D components in 2018, a 42% increase over the previous years.

Segmental Analysis

By deployment, the market is segmented into on-premises and cloud.

The on-premise segment in the Global Generative Design Market refers to the deployment of generative design software and solutions that are installed and operated within a company's own IT infrastructure, rather than through cloud-based platforms. This on-premise deployment allows for much more organizational control over design, data security, and options for customization. This approach is particularly liked by large enterprises or industries with high requirements of data privacy. Aerospace, automotive, and manufacturing industries are a few examples. On-premise solutions integrate generative design tools with already existing systems and infrastructure to provide a more tailored experience and potentially better performance because one will have line-of-sight to local resources. It, however, also embodies higher upfront costs associated with hardware and software, with continued maintenance responsibilities.

The cloud segment is increasingly significant due to the flexibility and scalability offered by cloud-based solutions. Cloud deployment allows for the dynamic allocation of resources and enables users to leverage advanced computational power without needing substantial on-premise infrastructure. Cloud-based generative design platforms provide access to high-performance computing resources and extensive data storage capacity without incurring heavy up-front costs the requirement for running complex design algorithms and huge data sets. With businesses and design professionals continuously reaching out for agile and efficient solutions, the cloud deployment segment is forecasted to grow rapidly on the back of increasing adoption of cloud technologies and a rising demand for innovative design solutions across industries.

By application, the market is segmented into Product Design and Development and Cost optimization

Product design and development is the most dynamic and influential segment in the global generative design market. With advanced algorithms and artificial intelligence at its very core, generative design enables the creation of optimized design solutions to previously set parameters and constraints. This is the reason the segment has seen high growth as industries seek innovative solutions effective in design and cost-efficient. Generative design tools let engineers/designers evaluate more design possibilities of products and thus aid in the problems of material use and performance optimization. This creates enormous value within industries such as automotive, aerospace, consumer electronics, and industrial machinery all of which are currently demanding state-of-the-art, lightweight, and high-performance products.

Cost optimization within this segment seeks to achieve the efficient use of materials and the reduction in manufacturing costs through the reduction of production waste with the help of these algorithms. Using generative design, companies can quickly and economically evaluate a variety of design options to ensure the delivery of a product that does not just meet the performance requirements but also fits within the budget. This is particularly useful in the automotive, aerospace, and manufacturing industries, where cost efficiency and material usage are critical factors. On the other hand, the demand for generative design solutions aimed at cost optimization is likely to rise once organizations have increasingly striven to improve their cost efficiency by streamlining their operations.

By end-user vertical, the industry is divided into automotive, aerospace and defense, architecture and construction, industrial manufacturing, and others.

The automotive sector has also remained one of the major end-user verticals within the global generative design market. Generative design makes use of sophisticated algorithms and AI to come up with optimized and original design solutions tailored to meet the requirements of the automotive sector. In this way, vehicle manufacturers can examine a high number of design possibilities for the optimization of components concerning weight, strength, and performance without ever compromising on stringent industry regulations and safety standards. It will allow automotive companies to speed up development processes, reduce material costs, and eventually increase vehicle performance and efficiency. This competence is especially useful for structural components, interior parts, or models with complex geometries where traditional design methods fall short.

In Aerospace and Defence, technology assumes a relevant role here, as generative design applies highly advanced algorithms and computational power to come up with optimized design solutions. In aerospace, this enables the development of lightweight, high-strength components that help to reduce raw material usage and associated manufacturing costs, all while adhering to stringent performance and safety standards. In the defence field, it is used for developing complex, highly functional parts for military equipment and vehicles to enhance efficiency and durability. Simulation enables a broad range of design scenarios and outcomes, thus allowing aerospace and defense companies to innovate faster and ensure superior design performance.

In Architecture and Construction, generative design tools are altering the architecture and building space by allowing architects and builders to deeply explore design possibilities for buildings while optimizing their performance. Professionals, with the use of computational algorithms, are now able to design structures that not only increase aesthetic appeal and functionality but also contribute towards sustainability. It enables the exploration of complex geometries and efficient use of material resources, hence leading to more energy-efficient and environmentally-friendly buildings. Besides, generative design supports rapid prototyping and iteration, helping smooth out the design process and reducing construction costs.

Industrial Manufacturing it has already begun to benefit immensely from the different capabilities offered by this technology. The concept of generative design in the industry of industrial manufacturing concentrates on the optimization of product design and production processes through advanced computational algorithms. With generative design, manufacturers can make very efficient, lightweight, resilient components that will meet certain performance criteria while minimizing material wastage during their production process. Thanks to this technology, several design alternatives can be investigated and iterated upon quickly to come up with innovative solutions that can enhance the functionality of products while reducing their production costs.

Impact of covid-19

The emergence of COVID-19 prompted the manufacturing industry to rethink its traditional manufacturing procedures, with a focus on digital transformation and advanced manufacturing practices throughout production lines. Robot deployment and the adoption of 3D printing, additive manufacturing, and generative design manufacturing technologies are examples of these practices. 

Manufacturers are also being driven to develop and execute a variety of novel ways to product and quality control. The likelihood of COVID-19 spreading is mainly caused by interior design, occupancy, and ventilation. Over 90% of all illnesses originate in enclosed, heavily populated situations with inadequate air exchange or recycled air.

Due to the pandemic's social distancing needs, there are more obstacles when planning and building public spaces. This is also projected to increase demand for technologies like generative design software.

Australia's national COVID-19 safe workplace principles stipulate that businesses and workers must actively limit COVID-19 transmission while at work and plan for the likelihood of COVID-19 instances in the workplace. They must respond quickly, appropriately, effectively, and efficiently, and by health authority recommendations. The AEC business attempts to design and construct settings with fast-changing aims and restrictions related to the new rules, such as 1.5-meter social distance limits and other client requirements.

Recent Developments

• May 2022- The Board of Directors of Bentley Systems, Incorporated the infrastructure engineering software firm, approved the BSY Stock Repurchase Program today, authorizing the Company to repurchase up to $200 million of BSY Class B common stock through June 30, 2024.
• May 2022- Ansys and the BMW Group are expanding their collaboration to establish the first-ever end-to-end toolchain for developing and validating ADAS and automated/autonomous driving features that is mainly directed by safety principles. The BMW Group is exploiting Ansys' capabilities as part of this collaboration to become one of the first automakers to offer consumers Level 3 (L3) highly autonomous driving. The partnership is critical for resolving ADAS and autonomous vehicle (AV) system reliability soon in order to reduce time to market.
• May 2022- Desktop Metal, Inc. has stated that it will exhibit a collection of over 300 3D printed industrial parts from the past. Production-capable printing platforms for metals, polymers, elastomers, ceramics, composites, and recycled wood materials will be on display at RAPID + TCT, North America's largest and most influential AM exhibition, from May 17-19 in Detroit.
• December 2021- World Programming, a UK-based technology business focusing on data analytics software utilized by many of the world's biggest companies, including financial services and insurance institutions, has been bought by Altair, the global leader in convergence simulation, HPC, and AI. The World Programming platform enables the creation and execution of multi-language software solutions using popular data science languages such as Python, R, and SAS in a single program.