Smart Manufacturing Market Size is valued at USD 106.75 Bn in 2023 and is predicted to reach USD 359.3 Bn by the year 2031 at a 16.56% CAGR during the forecast period for 2024-2031.
Robotics, Artificial Intelligence, And Machine Learning are examples of smart manufacturing technologies that enable automation, lowering the need for manual labour and expanding production efficiency. Robots, for example, can execute monotonous jobs more accurately and quickly, while artificial intelligence and machine learning may analyse data to optimise manufacturing processes and suggest areas for improvement. Rising Industry 4.0 adoption, increased government involvement in supporting industrial automation, increased stress on industrial automation in production processes, surging demand for software systems that save time and money, increasing supply chain complexities, and increased emphasis on regulatory compliances are all driving the market growth.
However, the spread of COVID-19 had a significant impact on the performance of various sectors and economies around the world. Governments were obliged to close factories and suspend import-export operations. As a result, some industries halted ongoing production, disrupting supply chains and affecting the demand-supply balance. However, the COVID-19 pandemic has unevenly influenced the smart manufacturing industry. Many organisations used automation technology faster than before because of labour shortages and social distancing requirements.
The segmentation of Smart Manufacturing Market includes information technology, enabling technology, and industry. According to information technology, the market is segmented as human-machine interface (HMI), plant asset management (PAM), Manufacturing execution system (MES), and warehouse management system (WMS). By enabling technology, the market is segmented into industrial 3D printing, robots, industrial sensors, AI in manufacturing, machine condition monitoring, industrial machine vision, industrial cyber-security, digital twin, automated guided vehicles, augmented reality & virtual reality, and 5G industrial IoT.
The industry type segment includes process industries and discrete industries. By process industries, the market is segmented into oil & gas, food & beverages, pharmaceuticals, chemicals, energy & power, metal & mining, pulp & paper, and others. By discrete industries, the market is segmented into automotive, aerospace, semiconductor & electronics, medical devices, machine manufacturing, and others.
The Manufacturing execution system (MES) category is expected to hold a major share in the global Smart Manufacturing Market in 2024. Manufacturing Execution Systems (MES) are becoming increasingly important in smart manufacturing. These systems aid in supervising and managing numerous manufacturing processes, assuring smooth operations, quality control, and efficient production. The manufacturing execution system (MES) integrates data from all stages of the production process, allowing for real-time monitoring, tracking, and optimisation. This increases production, decreases errors, and adds to the progress of smart manufacturing practices in general.
The software segment is estimated a rapid growth in the global Smart Manufacturing Market. As the sector moves towards full automation, the software component is the industry's backbone. Advanced software will assist in operating robots, drones, and other technologies without human intervention, lowering the possibility of error. The market may make significant advances in development and research for newer and more versatile solutions with software.
North American Smart Manufacturing Market is expected to record the maximum market share in revenue in the near future. Over the forecast period, the region is likewise expected to be the fastest growing. The market for innovation and automation in North America is being aggressively penetrated, resulting in the normalisation of smart manufacturing. The location is also close to and accessible to various raw resources, which aids in smart manufacturing and propels market expansion.
The Asia Pacific Smart Manufacturing Market is projected to grow rapidly in the forecasting period. Developing countries like India and China have tremendous untapped prospects in smart manufacturing and aim for full automation. These countries aspire to be self-sufficient in terms of production and manufacturing. Hence, they are heavily investing in Industry 4.0.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 106.75 Bn |
| Revenue Forecast In 2031 | USD 359.3 Bn |
| Growth Rate CAGR | CAGR of 16.3% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Information Technology, By Enabling Technology, By Industry |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico ; France; Italy; Spain; South Korea; South East |
| Competitive Landscape | 3D System, Inc. (US), ABB (Switzerland), Cisco System, Inc. (US), Emerson Electric Co. (US), General Electric (US), Honeywell International Inc. (US), IBM (US), Mitsubishi Electric Corporation (Japan), Rockwell Automation (US), Schneider Electric (France), Siemens (Germany), Oracle (US), SAP (Germany), Stratasys (US), and Yokogawa Electric Corporation(Japan), Cognex Corporation, Google, Intel Corporation, Keyence Corporation, Nvidia Corporation, PTC, Samsung, Sony, Universal Robots A/S, Omron Corporation |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
Smart Manufacturing Market By Information Technology-
Smart Manufacturing Market By Enabling Technology-
Smart Manufacturing Market By Industry-
By Region-
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This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.