Dubai, UAE, Aug. 18, 2023 (GLOBE NEWSWIRE) -- As per the latest report released by Kings Research, the Global Machine Learning in Manufacturing Market size was recorded at USD 921.32 Mn in 2022 and is estimated to grow to USD 8.77 Bn by 2030, exhibiting a CAGR of 33.35% during the forecast period of 2023-2030. The manufacturing industry is using machine learning models more frequently for product inspection and quality control, which is fueling market expansion. The surging need to integrate machine learning into current industrial workflows and production lines is anticipated to be a key driver for accelerated market growth.
Using machine learning methods, sensor data from a digital twin of a manufacturing process can be analyzed to find patterns that may be signs of equipment failure or other problems. This can assist manufacturers in identifying potential issues before they arise and taking corrective action, thus minimizing downtime and increasing efficiency. Additionally, production data analysis using machine learning can help enhance operations and detect inefficient regions.
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Trending Now: Senvol Exhibits Machine Learning Approach for Advancing Material Allowables Development for Additive Manufacturing
Senvol, based in New York City, has shown how to design material allowables using a machine learning approach. According to reports, this method for developing material allowables was found to be similar to the traditional (in this case, MMPDS) method while being more adaptable, affordable, and time-efficient.
The study was done under a US Government contract W911NF-20-9-0009 that Senvol received in order to use Senvol ML, its machine learning software, to enable the quick generation of material property allowables for additive manufacturing.
Competitive Landscape
An increasing number of companies operating in the global machine learning in manufacturing market are focusing on partnering with other businesses in order to expand their global reach. For instance, in December 2021, Sight Machine and NVIDIA partnered to extract valuable insights from factory data by using machine learning, with the ultimate goal of enhancing production processes.
Key firms competing in the market include:
- Rockwell Automation
- SAP SE
- IBM Corporation
- Robert Bosch GmbH
- Intel Corporation
- Siemens
- General Electric Company
- Microsoft
- Sight Machine
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By Production Stage
- Pre-Production
- Post-Production
Utilizing Machine Learning in Pre-production to Boost Efficiency
The pre-production segment dominated the machine learning in manufacturing market in 2022 and is likely to maintain its top position in the near future, accounting for a valuation of USD 6,106.2 million by 2030. Machine learning can significantly impact operations in both manufacturing and pre-production. It can also help increase worker safety, lower costs, improve quality control, and optimize production efficiency in the manufacturing industry.
Furthermore, machine learning can be used in pre-production to analyze market data and consumer feedback for product creation. It can also be used to predict maintenance requirements, monitor equipment performance, and streamline supply chain management. These factors are anticipated to fuel the growth of the segment.
By Application
- Semiconductors and Electronics
- Heavy Metals & Machine Manufacturing
- Pharmaceuticals
- Automobile
- Energy & Power
- Food & Beverages
- Others
Using Machine Learning in Semiconductors and Electronics to Cut Costs
The semiconductors and electronics segment led the global machine learning in manufacturing market in 2022 and is likely to dominate the segmental share with a valuation of USD 2,559.0 million by 2030. On manufacturing procedures in the semiconductor and electronics industries, machine learning can have a considerable impact as it helps to identify production-related issues, which is expected to increase its use.
Large datasets created during the manufacturing of semiconductors can be analyzed using machine learning techniques to find patterns and anomalies. This enables speedier detection and fixing of production issues. Real-time decision-making in industrial processes can also benefit from it. Overall, the semiconductor and electronics industries can benefit from adopting machine learning in terms of higher production, lower costs, and quicker innovation.
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Rising Adoption of Smart Manufacturing to Drive Market Expansion
As machine learning algorithms can be used to process the data produced by smart manufacturing processes to increase productivity and efficiency, smart manufacturing and machine learning are closely related concepts. This can decrease downtime and increase overall equipment efficacy. Additionally, manufacturing data can be analyzed using machine learning to spot trends that can be used to optimize production processes for better efficiency.
Additionally, a variety of machine learning approaches may be used in smart manufacturing processes to stimulate the market. The optimization of smart manufacturing processes could also use reinforcement learning algorithms to evaluate and optimize the controls and parameters for the best possible outcome. Overall, machine learning in smart manufacturing has significant potential for increased effectiveness, quality, and productivity, helping firms compete in a more digital and data-driven market.
Increasing Demand for Advanced Technologies in North America to Foster Market Progress
North America is expected to be the largest market for machine learning in manufacturing and is estimated to reach a valuation of USD 3,268.2 million by 2030. This growth can be majorly attributed to the widespread integration of machine learning in manufacturing, a trend that has been rapidly gaining momentum in recent times.
The White House's National Strategy for Advanced Manufacturing, which was published in 2022, places a strong emphasis on the need for cutting-edge technology, especially machine learning, to maintain the competitiveness of the U.S. manufacturing sector. In the U.S., machine learning is being used in a variety of manufacturing operations, from supply chain management and quality control to production process optimization.
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Rapid Evolution of Industries in APAC to Propel Demand for Machine Learning
Asia Pacific is projected to emerge as the second-largest market for machine learning in manufacturing, recording a valuation of USD 2,998.4 million. Machine learning and computer vision tools are increasingly being deployed in the region's manufacturing sector for conducting machinery inspections.
Additionally, industries are utilizing AI extensively to minimize downtime, boost production, and lower operating expenses. Other factors augmenting regional market growth include rising venture capital investments, surging automation demand, and the rapid evolution of industries and industrial IoT.
Table of Content
1 Introduction of The Global Machine Learning in Manufacturing Market
1.1 Market Definition
1.2 Market Segmentation
1.3 Research Timelines
1.4 Limitations
1.5 Assumptions
2 Executive Summary
3 Research Methodology
3.1 Data Collection
3.1.1 Secondary Sources
3.1.2 Primary Sources
3.1.3 Research Flow
3.2 Subject Matter Expert Advice
3.3 Quality Check
3.4 Final Review
3.5 Bottom-Up Approach
3.6 Top-down Approach
4 Global Machine Learning in Manufacturing Market Outlook
4.1 Market Evolution
4.2 Overview
4.3 Market Dynamics
4.3.1 Drivers
4.3.2 Restraints
4.3.3 Opportunities
4.3.4 Challenges
4.4 Pricing Analysis
4.5 Porter’s Five Forces Analysis
4.6 Value Chain Analysis
4.7 Macroeconomic Analysis
5 Impact of Russia-Ukraine War
6 Global Machine Learning in Manufacturing Market, By Production Stage
7 Global Machine Learning in Manufacturing Market, By Job Function
8 Global Machine Learning in Manufacturing Market, By Application
9 Global Machine Learning in Manufacturing Market, By Geography
10 North America
11 Europe
12 Asia Pacific
13 Middle East & Africa
14 Latin America
15 Global Machine Learning in Manufacturing Market Competitive Landscape
16 Company Profiles
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