AI in Industrial Machinery Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032

Description

AI in industrial machinery market size is anticipated to witness a 27.2% CAGR between 2024 and 2032 driven by the need for increased operational efficiency and productivity in manufacturing processes. By leveraging technologies like machine learning (ML) and predictive analytics, AI empowers machinery to conduct real-time data analyses, optimize production schedules, and foresee equipment failures. Such predictive maintenance not only curtails downtime but also trims maintenance expenses. Furthermore, AI-driven automation amplifies both precision and speed in manufacturing. For example, in May 2024, Composable unveiled a No-Code UI platform, enabling engineers to train AI agents directly by integrating operator expertise into real-world scenarios.

The ascent of smart manufacturing and the push towards Industry 4.0 is set to further propel the market growth. As enterprises gravitate towards interconnected and automated production landscapes, AI plays a pivotal role in fostering seamless interactions among machines, sensors, and control systems. This enhanced connectivity not only allows for real-time monitoring but also agile decision-making in manufacturing.

The overall industry is divided into component, technology, application, end use, and region.

Based on technology, the AI in industrial machinery market size from the computer version segment is slated to witness significant growth during 2024-2032 driven by its role in advanced visual analysis and quality control. When integrated with AI, computer vision technologies empower machinery to accurately interpret and analyze visual data from sensors and cameras. This precision bolsters defect detection, quality assurance, and automated inspections, ensuring high production standards and minimizing waste.

AI in industrial machinery market from the quality control application segment is anticipated to expand through 2032. AI-driven quality control systems harness advanced algorithms and ML to scrutinize product data, identify defects, and uphold rigorous quality benchmarks. Their ability to swiftly and accurately process vast data volumes surpasses human inspectors, leading to reduced errors and diminished waste.

Asia Pacific AI in industrial machinery industry is anticipated to grow at a significant pace over 2024-2032. This surge is fueled by swift industrialization and technological advancements. As manufacturing powerhouses like China, India, and Japan bolster their capabilities, the demand for AI technologies to drive efficiency and innovation in industrial processes is on the rise.

Product Code: 5774

Chapter 1 Methodology and Scope

1.1 Market scope and definitions
1.2 Base estimates and calculations
1.3 Forecast calculations
1.4 Data sources
- 1.4.1 Primary
- 1.4.2 Secondary
  - 1.4.2.1 Paid sources
  - 1.4.2.2 Public sources

Chapter 2 Executive Summary

2.1 Industry 360° synopsis, 2021-2032

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
- 3.1.1 Factors affecting the value chain
- 3.1.2 Profit margin analysis
- 3.1.3 Disruptions
- 3.1.4 Future outlook
- 3.1.5 Manufacturers
- 3.1.6 Distributors
3.2 Supplier landscape
3.3 Profit margin analysis
3.4 Technological overview
3.5 Regulatory landscape
3.6 Impact forces
- 3.6.1 Growth drivers
  - 3.6.1.1 Rising adoption of Al in manufacturing sector
  - 3.6.1.2 Integration with IOT and cloud computing
  - 3.6.1.3 Advanced analytics and decision-making
- 3.6.2 Industry pitfalls and challenges
  - 3.6.2.1 High implementation costs
  - 3.6.2.2 Skill Gap and Workforce Adaptation
3.7 Growth potential analysis
3.8 Porter's analysis
3.9 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix

Chapter 5 Market Estimates and Forecast, By Component, 2021-2032 (USD million)

5.1 Key trends
5.2 Hardware
5.3 Software
5.4 Services

Chapter 6 Market Estimates and Forecast, By Technology, 2021-2032 (USD million))

6.1 Key trends
6.2 Machine learning
6.3 Computer vision
6.4 Context awareness
6.5 Natural language processing

Chapter 7 Market Estimates and Forecast, By Application, 2021-2032 (USD million)

7.1 Key trends
7.2 Predictive maintenance
7.3 Quality control
7.4 Process optimization
7.5 Supply chain optimization
7.6 Intelligent robotics
7.7 Autonomous vehicles and guided systems
7.8 Energy management
7.9 Human-machine interfaces
7.10 Others

Chapter 8 Market Estimates and Forecast, By End Use, 2021-2032 (USD million)

8.1 Key trends
8.2 Agriculture
8.3 Construction
8.4 Packaging
8.5 Food processing
8.6 Mining
8.7 Semiconductor

Chapter 9 Market Estimates and Forecast, By Region, 2021-2032 (USD million)

9.1 Key trends
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
9.3 Europe
- 9.3.1 UK
- 9.3.2 Germany
- 9.3.3 France
- 9.3.4 Italy
- 9.3.5 Spain
- 9.3.6 Russia
- 9.3.7 Rest of Europe
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 India
- 9.4.3 Japan
- 9.4.4 South Korea
- 9.4.5 Australia
- 9.4.6 Rest of Asia Pacific
9.5 Latin America
- 9.5.1 Brazil
- 9.5.2 Mexico
- 9.5.3 Rest of Latin America
9.6 MEA
- 9.6.1 South Africa
- 9.6.2 Saudi Arabia
- 9.6.3 UAE
- 9.6.4 Rest of MEA

Chapter 10 Company Profiles

10.1 ABB Ltd.
10.2 Amazon Web Services (AWS)
10.3 Cisco Systems, Inc.
10.4 FANUC Corporation
10.5 Google LLC
10.6 Hitachi, Ltd.
10.7 Honeywell International Inc.
10.8 IBM Corporation
10.9 Intel Corporation
10.10 Microsoft Corporation
10.11 NVIDIA Corporation
10.12 Qualcomm Technologies
10.13 Rockwell Automation, Inc.
10.14 Schneider Electric SE
10.15 Siemens AG