As per the report published by Kings Research, the global Machine Learning in Manufacturing Market was valued at USD 921.3 million in 2022 and is projected to reach USD 8,776.7 million by 2030, growing at a CAGR of 33.35% from 2023 to 2030.

Machine learning algorithms can be used to analyze sensor data from a digital twin of a manufacturing process to identify patterns that are indicative of equipment failure or other issues. This can help manufacturers pinpoint potential issues before they occur and take corrective action, thereby reducing downtime and improving efficiency. Machine learning can also be used to analyze production data to optimize workflows and identify areas where efficiency can be improved.

Machine learning models are increasingly being employed in product inspection and quality control in the manufacturing sector. Specifically, ML-based computer vision algorithms can leverage historical data to differentiate between good and faulty products, thereby automating the inspection and supervision processes. Notably, these algorithms require only a set of high-quality samples for training, eliminating the need to create a library of possible defects. Alternatively, an algorithm can be developed that compares samples against the most common types of defects. In addition to facilitating the inspection and supervision processes, the implementation of machine learning in visual quality control in manufacturing offers significant cost savings. The aforementioned factors are propelling the growth of the market.

Furthermore, with the incorporation of IoT sensors into production processes, manufacturers can track and monitor equipment performance and analyse real-time data on various aspects of production, from machine health to supply chain management. Overall, the integration of IoT technology in manufacturing processes has the potential to unlock significant value and benefits for companies operating in various industrial sectors, from automotive and aerospace to energy and chemical manufacturers.

Key Insights

• Based on the offering, the pre-production segment is expected to grow at the fastest CAGR of 29.67% during the forecast period
• Based on deployment, the R&D segment generated the highest revenue of USD 335.2 million in 2022
• Based on vertical, the automobile segment is anticipated to grow at a rapid CAGR of 42.50% during the forecast period
• On the basis of region, Asia-Pacific is projected to grow at the fastest CAGR of 37.81% during the forecast period

SAS Partnership with CESMII to Drive the Adoption of Cloud Solutions

February 2023: SAS has partnered with CESMII, the Smart Manufacturing Institute, to drive the widespread adoption of cutting-edge analytics combined with extensive AI knowledge across the manufacturing sector.

Smart Manufacturing to Fuel Market Growth by Improving Efficiency

Smart manufacturing and machine learning are closely related, as machine learning algorithms can be applied to the data generated in smart manufacturing processes to improve efficiency and productivity. This can improve overall equipment effectiveness and reduce downtime. Additionally, machine learning can identify patterns in production data that allow production processes to be optimized for increased efficiency and reduced waste. Furthermore, several machine-learning techniques may be applied to smart manufacturing processes to drive the market. Moreover, reinforcement learning algorithms could be implemented in the optimization of smart manufacturing processes, evaluating and optimizing the controls and parameters for the best performance. Overall, machine learning in smart manufacturing offers great potential for improved efficiency, quality, and productivity, allowing manufacturers to remain competitive in an increasingly digital and data-driven industry.

Increasing Adoption of Autonomous Vehicles is a Vital Trend

Integrating machine learning into the manufacturing process can lead to better decision-making and increased efficiency, as demonstrated by the adoption of autonomous vehicles. Machine learning algorithms can analyze large streams of sensor data to detect trends and anomalies, leading to predictive maintenance and improved performance. Additionally, machine learning can optimize routes, improve product quality through predictive quality control, and enhance supply chain visibility by analyzing data on shipping routes and demand patterns. As a result, the adoption of machine learning technology will enable manufacturing to become more efficient, cost-effective, and competitive.

Asia Pacific Emerged as the Fastest Growing Region Exhibiting a Significant CAGR of 37.81% in 2022

The manufacturing industry is witnessing a rise in demand for machine learning technology and computer vision to conduct machinery inspections. In addition, factories are increasingly adopting AI to reduce machine downtime, improve productivity, and decrease operational costs. Such trends are reflected in the growing preference of factories to leverage these technologies. The regional market is further fueled by several factors, such as increasing venture capital investments, growing demand for automation, and the rapid evolution of industries and industrial IoT.

Market Players Concentrating on Innovative Product Launches to Drive Market Development

The global machine learning in the manufacturing market is fragmented with key players such as Rockwell Automation, SAP SE, IBM Corporation, Robert Bosch GmbH, Intel Corporation, Siemens, General Electric Company, Microsoft, and Sight Machine. Companies employ a variety of strategic measures such as acquisitions, mergers, partnerships, product introductions, and collaborations to expand their business globally and enhance their competitive position.