Artificial Intelligence In Manufacturing Market
Artificial Intelligence In Manufacturing Market Forecasts to 2030 - Global Analysis By Component (Hardware, Software and Services), Deployment Mode (On-Premise and Cloud), Technology, Application, End User and By Geography
According to Stratistics MRC, the Global Artificial Intelligence in Manufacturing Market is accounted for $4.59 billion in 2024 and is expected to reach $22.86 billion by 2030 growing at a CAGR of 45.6% during the forecast period. Artificial Intelligence (AI) in manufacturing refers to the use of advanced algorithms and machine learning models to optimize production processes, improve efficiency, and reduce costs. AI can enhance areas such as predictive maintenance, quality control, supply chain management, and robotics automation. By analyzing vast amounts of data in real-time, AI-driven systems help manufacturers identify inefficiencies, predict equipment failures, and make data-driven decisions.
According to Capgemini’s report of 2019, European manufacturers are leading in implementing AI technology, Germany is at the top rank in adoption.
Market Dynamics:
Driver:
Adoption of industry 4.0
Industry 4.0 emphasizes the use of AI, the internet of things (IoT), robotics, and big data to create highly interconnected and intelligent production systems. AI enables real-time monitoring, predictive maintenance, and process optimization, enhancing efficiency and reducing downtime. As manufacturers shift towards smart factories and digitalization, AI becomes essential for automating decision-making, improving product quality, and achieving operational flexibility, thus driving the overall growth of AI in the manufacturing market.
Restraint:
Data privacy and security concerns
Data privacy and security concerns in Artificial Intelligence (AI) in manufacturing arise because AI systems rely on vast amounts of sensitive data from machines, processes, and networks. This data is often stored and processed in connected environments, making it vulnerable to cyberattacks, unauthorized access, and breaches. These risks deter some manufacturers from adopting AI solutions, as they may be cautious about potential data vulnerabilities, thus hampering the growth of AI in the manufacturing market.
Opportunity:
Increased demand for automation
AI-powered automation reduces human intervention, streamlines operations, and minimizes errors, leading to cost savings and improved productivity. Manufacturers are adopting AI for tasks such as robotic automation, predictive maintenance, and quality control, which enhance speed and precision. Automation also addresses labour shortages by filling skill gaps and handling repetitive tasks. As industries aim to boost output and remain competitive, the demand for AI-driven automation continues to rise, fuelling market growth.
Threat:
High implementation costs
AI in manufacturing has high implementation costs due to the need for advanced hardware, software, and specialized infrastructure, including sensors, data processing systems, and machine learning algorithms. Additionally, integrating AI with existing legacy systems requires significant customization, time, and skilled personnel, further increasing expenses. These upfront costs, along with ongoing maintenance and updates, present financial barriers, especially for small and medium-sized enterprises (SMEs).
Covid-19 Impact
The covid-19 pandemic accelerated the adoption of Artificial Intelligence in manufacturing as companies sought to overcome supply chain disruptions, labour shortages, and operational challenges. AI-driven automation, predictive maintenance, and demand forecasting became critical for maintaining production efficiency and adapting to fluctuating market conditions. However, initial investments slowed due to economic uncertainty and reduced capital expenditures. Despite this, the long-term impact has been positive, with increased focus on AI solutions for resilience, flexibility, and improved operational efficiency in manufacturing.
The supply chain management segment is expected to be the largest during the forecast period
The supply chain management segment is estimated to be the largest during the forecast period. Artificial Intelligence (AI) in manufacturing is revolutionizing supply chain management by optimizing operations, enhancing demand forecasting, and improving inventory management. AI-driven systems analyze large datasets to predict demand patterns, detect supply chain disruptions, and streamline logistics. Predictive analytics enable manufacturers to reduce excess inventory and prevent stockouts, while AI-powered automation helps in scheduling and resource allocation.
The electronics segment is expected to have the highest CAGR during the forecast period
The electronics segment is anticipated to witness the highest CAGR during the forecast period. Artificial Intelligence (AI) in electronics manufacturing enhances efficiency and precision by automating tasks such as defect detection, predictive maintenance, and quality control. AI-powered computer vision systems enable real-time inspection, ensuring higher product quality and reducing human error. Machine learning algorithms optimize production processes, minimizing downtime and waste. AI also supports supply chain optimization and inventory management, improving operational flexibility.
Region with largest share:
Asia Pacific is projected to have the largest market share during the forecast period driven by strong industrial development, government initiatives promoting automation, and the rise of smart factories. Countries like China, Japan, and South Korea are leading in AI adoption, with significant investments in robotics, machine learning, and predictive analytics to enhance production efficiency. The region's robust manufacturing sector, combined with technological advancements and increasing demand for higher productivity and cost reduction, positions Asia Pacific as a key hub for AI-driven industrial transformation.
Region with highest CAGR:
North America is projected to have the highest CAGR over the forecast period, driven by advanced technology adoption, a focus on smart manufacturing, and the region's push for digital transformation. The U.S. leads the way, with manufacturers leveraging AI for predictive maintenance, quality control, and process optimization. The regions highly developed industrial sector, coupled with investments in automation and machine learning, supports increased efficiency and innovation. AI-powered solutions in robotics and data analytics are helping North American manufacturers improve productivity, reduce operational costs, and enhance competitiveness in global markets.
Key players in the market
Some of the key players profiled in the Artificial Intelligence in Manufacturing Market include Siemens, General Electric (GE), IBM, Rockwell Automation, ABB, Honeywell, Microsoft, Bosch, Schneider Electric, SAP, NVIDIA, Intel, PTC, Oracle, Fujitsu, Sandvik, Teradyne, Zebra Technologies and Autodesk.
Key Developments:
In June 2024, Sandvik launched AI in the “Manufacturing Copilot”, the manufacturing software in alliance with Microsoft. This will provide customers a simple and more accessible experience with 24/7 intelligent customer assistance. The Copilot offers real-time updates and enables informed choices. This is the first step in the AI roadmap to enhance the customer experience.
In April 2024, Microsoft announced new industrial AI innovations from the cloud to the factory floor. This AI-driven shift is prompting many organizations to fundamentally alter their business models and re-evaluate how to address industry-wide challenges like data siloes from disparate data estates and legacy products, supply chain visibility issues, labor shortages, and the need for upskilling employees.
Components Covered:
• Hardware
• Software
• Services
Deployment Modes Covered:
• On-Premise
• Cloud
Technologies Covered:
• Machine Learning (ML)
• Natural Language Processing (NLP)
• Computer Vision
• Context-aware Computing
• Deep Learning
• Other Technologies
Applications Covered:
• Predictive Maintenance
• Machinery Inspection
• Quality Control
• Production Planning
• Inventory Optimization
• Supply Chain Management
• Yield Optimization
• Other Applications
End Users Covered:
• Automotive
• Electronics
• Energy & Power
• Pharmaceuticals
• Chemicals
• Food & Beverages
• Aerospace & Defense
• Other End Users
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global Artificial Intelligence In Manufacturing Market, By Component
5.1 Introduction
5.2 Hardware
5.2.1 Processors
5.2.2 Memory Devices
5.2.3 Network Components
5.3 Software
5.3.1 Artificial Intelligence Platforms
5.3.2 Computer Vision Tools
5.4 Services
5.4.1 Managed Services
5.4.2 Professional Services
6 Global Artificial Intelligence In Manufacturing Market, By Deployment Mode
6.1 Introduction
6.2 On-Premise
6.3 Cloud
7 Global Artificial Intelligence In Manufacturing Market, By Technology
7.1 Introduction
7.2 Machine Learning (ML)
7.3 Natural Language Processing (NLP)
7.4 Computer Vision
7.5 Context-aware Computing
7.6 Deep Learning
7.7 Other Technologies
8 Global Artificial Intelligence In Manufacturing Market, By Application
8.1 Introduction
8.2 Predictive Maintenance
8.3 Machinery Inspection
8.4 Quality Control
8.5 Production Planning
8.6 Inventory Optimization
8.7 Supply Chain Management
8.8 Yield Optimization
8.9 Other Applications
9 Global Artificial Intelligence In Manufacturing Market, By End User
9.1 Introduction
9.2 Automotive
9.3 Electronics
9.4 Energy & Power
9.5 Pharmaceuticals
9.6 Chemicals
9.7 Food & Beverages
9.8 Aerospace & Defense
9.9 Other End Users
10 Global Artificial Intelligence In Manufacturing Market, By Geography
10.1 Introduction
10.2 North America
10.2.1 US
10.2.2 Canada
10.2.3 Mexico
10.3 Europe
10.3.1 Germany
10.3.2 UK
10.3.3 Italy
10.3.4 France
10.3.5 Spain
10.3.6 Rest of Europe
10.4 Asia Pacific
10.4.1 Japan
10.4.2 China
10.4.3 India
10.4.4 Australia
10.4.5 New Zealand
10.4.6 South Korea
10.4.7 Rest of Asia Pacific
10.5 South America
10.5.1 Argentina
10.5.2 Brazil
10.5.3 Chile
10.5.4 Rest of South America
10.6 Middle East & Africa
10.6.1 Saudi Arabia
10.6.2 UAE
10.6.3 Qatar
10.6.4 South Africa
10.6.5 Rest of Middle East & Africa
11 Key Developments
11.1 Agreements, Partnerships, Collaborations and Joint Ventures
11.2 Acquisitions & Mergers
11.3 New Product Launch
11.4 Expansions
11.5 Other Key Strategies
12 Company Profiling
12.1 Siemens
12.2 General Electric (GE)
12.3 IBM
12.4 Rockwell Automation
12.5 ABB
12.6 Honeywell
12.7 Microsoft
12.8 Bosch
12.9 Schneider Electric
12.10 SAP
12.11 NVIDIA
12.12 Intel
12.13 PTC
12.14 Oracle
12.15 Fujitsu
12.16 Sandvik
12.17 Bosch
12.18 Teradyne
12.19 Zebra Technologies
12.20 Autodesk
List of Tables
1 Global Artificial Intelligence In Manufacturing Market Outlook, By Region (2022-2030) ($MN)
2 Global Artificial Intelligence In Manufacturing Market Outlook, By Component (2022-2030) ($MN)
3 Global Artificial Intelligence In Manufacturing Market Outlook, By Hardware (2022-2030) ($MN)
4 Global Artificial Intelligence In Manufacturing Market Outlook, By Processors (2022-2030) ($MN)
5 Global Artificial Intelligence In Manufacturing Market Outlook, By Memory Devices (2022-2030) ($MN)
6 Global Artificial Intelligence In Manufacturing Market Outlook, By Network Components (2022-2030) ($MN)
7 Global Artificial Intelligence In Manufacturing Market Outlook, By Software (2022-2030) ($MN)
8 Global Artificial Intelligence In Manufacturing Market Outlook, By Artificcial Intelligence Platforms (2022-2030) ($MN)
9 Global Artificial Intelligence In Manufacturing Market Outlook, By Computer Vision Tools (2022-2030) ($MN)
10 Global Artificial Intelligence In Manufacturing Market Outlook, By Services (2022-2030) ($MN)
11 Global Artificial Intelligence In Manufacturing Market Outlook, By Managed Services (2022-2030) ($MN)
12 Global Artificial Intelligence In Manufacturing Market Outlook, By Professional Services (2022-2030) ($MN)
13 Global Artificial Intelligence In Manufacturing Market Outlook, By Deployment Mode (2022-2030) ($MN)
14 Global Artificial Intelligence In Manufacturing Market Outlook, By On-Premise (2022-2030) ($MN)
15 Global Artificial Intelligence In Manufacturing Market Outlook, By Cloud (2022-2030) ($MN)
16 Global Artificial Intelligence In Manufacturing Market Outlook, By Technology (2022-2030) ($MN)
17 Global Artificial Intelligence In Manufacturing Market Outlook, By Machine Learning (ML) (2022-2030) ($MN)
18 Global Artificial Intelligence In Manufacturing Market Outlook, By Natural Language Processing (NLP) (2022-2030) ($MN)
19 Global Artificial Intelligence In Manufacturing Market Outlook, By Computer Vision (2022-2030) ($MN)
20 Global Artificial Intelligence In Manufacturing Market Outlook, By Context-aware Computing (2022-2030) ($MN)
21 Global Artificial Intelligence In Manufacturing Market Outlook, By Deep Learning (2022-2030) ($MN)
22 Global Artificial Intelligence In Manufacturing Market Outlook, By Other Technologies (2022-2030) ($MN)
23 Global Artificial Intelligence In Manufacturing Market Outlook, By Application (2022-2030) ($MN)
24 Global Artificial Intelligence In Manufacturing Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
25 Global Artificial Intelligence In Manufacturing Market Outlook, By Machinery Inspection (2022-2030) ($MN)
26 Global Artificial Intelligence In Manufacturing Market Outlook, By Quality Control (2022-2030) ($MN)
27 Global Artificial Intelligence In Manufacturing Market Outlook, By Production Planning (2022-2030) ($MN)
28 Global Artificial Intelligence In Manufacturing Market Outlook, By Inventory Optimization (2022-2030) ($MN)
29 Global Artificial Intelligence In Manufacturing Market Outlook, By Supply Chain Management (2022-2030) ($MN)
30 Global Artificial Intelligence In Manufacturing Market Outlook, By Yield Optimization (2022-2030) ($MN)
31 Global Artificial Intelligence In Manufacturing Market Outlook, By Other Applications (2022-2030) ($MN)
32 Global Artificial Intelligence In Manufacturing Market Outlook, By End User (2022-2030) ($MN)
33 Global Artificial Intelligence In Manufacturing Market Outlook, By Automotive (2022-2030) ($MN)
34 Global Artificial Intelligence In Manufacturing Market Outlook, By Electronics (2022-2030) ($MN)
35 Global Artificial Intelligence In Manufacturing Market Outlook, By Energy & Power (2022-2030) ($MN)
36 Global Artificial Intelligence In Manufacturing Market Outlook, By Pharmaceuticals (2022-2030) ($MN)
37 Global Artificial Intelligence In Manufacturing Market Outlook, By Chemicals (2022-2030) ($MN)
38 Global Artificial Intelligence In Manufacturing Market Outlook, By Food & Beverages (2022-2030) ($MN)
39 Global Artificial Intelligence In Manufacturing Market Outlook, By Aerospace & Defense (2022-2030) ($MN)
40 Global Artificial Intelligence In Manufacturing Market Outlook, By Other End Users (2022-2030) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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