Global Artificial Intelligence in Manufacturing Market Size, Share & Trends Analysis Report, By offering (hardware, software, and services), By technology (machine learning, natural language processing, computer vision, and others), By application (predictive maintenance and machinery inspection, supply chain optimization, quality control, production planning and optimization, and others), By end-use industry (automotive, electronics and semiconductors, aerospace and defense, healthcare, food and beverage, and others), By Region (North America, Europe, APAC, and Others), and Segment Forecasts, 2023 – 2030
  • Published Date: Nov, 2023
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  • Pages: 200
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  • Report Summary
  • Table of Contents
  • Segmentation
  • Methodology
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The AI in manufacturing market refers to the sector dedicated to the development, integration, and utilization of artificial intelligence technologies and solutions within the manufacturing industry. This market encompasses a wide range of AI applications, including predictive maintenance, quality control, supply chain optimization, process automation, and more. AI technologies, such as machine learning, natural language processing, computer vision, and robotics, are leveraged to enhance operational efficiency, reduce costs, improve product quality, and respond to dynamic market demands. The AI in manufacturing market plays a pivotal role in revolutionizing traditional manufacturing practices, making them more adaptive, data-driven, and competitive in an increasingly digital and interconnected global landscape.

Some of the benefits of designing a Artificial Intelligence in Manufacturing Market include:

  • Improved Operational Efficiency: AI fine-tunes manufacturing processes, minimizing waste and optimizing resource utilization, resulting in streamlined operations, reduced costs, and heightened productivity.
  • Proactive Maintenance: AI-powered predictive maintenance anticipates equipment malfunctions, slashing downtime and preventing costly unscheduled repairs, contributing to uninterrupted production.
  • Enhanced Product Quality: AI-driven quality control systems promptly detect defects and irregularities, guaranteeing product adherence to specifications while minimizing the need for manual inspections.
  • Streamlined Supply Chains: AI analytics and optimization tools refine supply chain management, enabling manufacturers to adapt to fluctuating demand, discover alternative suppliers, and fine-tune inventory management.
  • Tailored Production: AI facilitates efficient customization, catering to individual consumer preferences, thus elevating customer satisfaction by delivering precisely what each customer desires.
  • Remote Oversight: AI empowers remote monitoring of machinery and processes, ensuring operational continuity from any location and reducing the necessity for on-site personnel.
  • Automation of Repetitive Tasks: AI takes care of routine and labor-intensive duties, granting human workers the freedom to engage in more intricate and innovative aspects of production.
  • Informed Decision-Making: AI swiftly processes vast datasets, furnishing actionable insights for data-driven choices, enhancing product development, and refining process optimization.

Global Artificial Intelligence in Manufacturing Market was valued at US $ 17.5 Billion in 2022 and is expected to reach US $267.3 Billion by 2030 growing at a CAGR of 40.6% during the forecast period 2023 – 2030.

COVID -19 Impact

The COVID-19 pandemic brought significant changes to the AI in manufacturing market, reshaping the industry's landscape. As manufacturing companies faced disruptions in supply chains, workforce availability, and production processes, the adoption of AI technologies accelerated. Manufacturers swiftly turned to AI and automation to bolster operational resilience and adapt to the new normal. Remote monitoring and predictive maintenance became essential, allowing companies to manage equipment and predict maintenance requirements from afar, minimizing downtime and reducing the reliance on on-site personnel. AI-powered quality control systems ensured product quality and consistency in an environment with limited physical inspections.

Supply chain challenges prompted manufacturers to use AI-driven analytics and optimization tools to adapt to shifting demand, identify alternative suppliers, and optimize inventory. Remote operations, enabled by AI, ensured business continuity during lockdowns and social distancing measures.

The pandemic heightened awareness of the value of AI in manufacturing, leading to increased investment in AI solutions. Moreover, it emphasized the need for resilience planning, with AI-driven simulations and scenario analysis helping manufacturers prepare for future disruptions. In conclusion, COVID-19 accelerated the adoption of AI in manufacturing, highlighting the importance of adaptability, efficiency, and remote capabilities in a world marked by uncertainty. The crisis positioned AI as a pivotal tool in addressing manufacturing challenges and preparing the industry for future disruptions.

Factors Driving the Market

Drivers

Increasing adoption of Industry 4.0 technologies

The increasing adoption of Industry 4.0 technologies serves as a dynamic catalyst for market growth in the manufacturing sector. These technologies, which encompass AI, IoT, data analytics, and automation, usher in an era of enhanced efficiency and productivity. By harnessing real-time data and predictive maintenance capabilities, manufacturers can streamline processes, reduce downtime, and ensure product quality. Industry 4.0 technologies optimize supply chains, enable mass customization, and empower businesses to swiftly adapt to evolving market demands, resulting in a competitive edge. Moreover, they align with environmental sustainability goals by reducing waste and energy consumption. As these technologies gain global traction, they create new market opportunities, foster economic growth, and offer job creation, collectively propelling the industry forward.

Growing need for data-driven decision-making

The growing need for data-driven decision-making is a transformative force in today's business landscape, significantly boosting market growth and reshaping industries across the globe. In an increasingly data-rich environment, organizations are recognizing the imperative of harnessing the power of data to make informed and strategic choices. This trend is particularly evident in the rise of artificial intelligence (AI) and advanced analytics, which have become essential tools for data-driven decision-making. One of the most notable impacts of this shift is the enhancement of decision accuracy. With AI and data analytics at their disposal, decision-makers can access comprehensive, real-time data that empowers them to make more precise, informed, and less error-prone choices. Whether in finance, healthcare, manufacturing, or retail, data-driven decision-making is reducing the margin for error and driving improvements in business performance.

Operational efficiency is another key benefit of data-driven decision-making. By closely monitoring and analyzing operational data, organizations can identify areas for improvement. This optimization of processes leads to cost savings, streamlined operations, and improved resource utilization, making companies more agile and competitive in their respective industries. The competitive advantage conferred by data-driven decision-making cannot be overstated. In rapidly evolving markets, businesses that respond swiftly to changing customer preferences, market dynamics, and emerging trends gain an edge. AI and data analytics provide the tools necessary to analyze market data, identify opportunities, and pivot in response to new insights. This agility enables businesses to remain competitive and adapt to evolving market conditions.

Product innovation is also heavily influenced by data-driven insights. Understanding consumer behavior and market trends is paramount for product development. By leveraging AI and data analysis, companies can extract valuable insights that inform product design and development, resulting in innovative, consumer-centric products and services. Resource optimization is a significant driver of cost savings. Data-driven decision-making allows businesses to allocate resources more efficiently, reducing unnecessary expenses and optimizing resource utilization. This is especially critical in manufacturing, where precise management of resources such as raw materials, energy, and labor can have a profound impact on costs and competitiveness.

Additionally, data-driven decision-making supports risk mitigation. By analyzing historical data and identifying patterns, AI can assist in assessing and mitigating risks. This is particularly valuable in financial decisions, supply chain management, and overall business strategy, as it enables organizations to proactively manage and reduce potential risks. Customer satisfaction and loyalty are enhanced by data-driven decision-making. Personalized recommendations and services are made possible through data analysis, resulting in a higher level of customer satisfaction and long-term loyalty. Moreover, data-driven decision-making instills a culture of continuous improvement within organizations. By regularly evaluating performance and identifying areas for enhancement, businesses are better equipped to adapt to changing market conditions and continuously optimize their processes and strategies.

Challenges

Data security and privacy concerns

Data security and privacy concerns exert a notable impact on market growth within the AI landscape. These concerns, when unaddressed, can erode consumer trust, hindering the adoption of AI solutions. Stricter data protection regulations necessitate compliance measures that can increase operational costs and create barriers to entry, particularly for smaller businesses. Data sharing and collaboration between organizations may be limited, affecting the development of new AI applications. Furthermore, the risk of data breaches or privacy violations poses substantial reputational risks, potentially deterring users and investors alike. The cost of compliance, investments in cybersecurity, and constraints on data usage can burden AI companies. Nevertheless, businesses that prioritize data security, demonstrate ethical data practices, and navigate the regulatory landscape effectively can stand to benefit from these concerns, shaping the market's growth trajectory and fostering a culture of responsible AI use.

Trends

Development of new AI applications

The development of new AI applications plays a pivotal role in accelerating market growth. As innovative AI solutions continue to emerge, they create a ripple effect across various industries. These applications not only expand market opportunities but also grant businesses a competitive edge by offering tailored, efficient, and data-driven solutions. The introduction of fresh AI applications attracts greater adoption, particularly when they address specific challenges, further propelling market expansion. Moreover, this phenomenon contributes to overall market growth by attracting investment, fostering a dynamic innovation ecosystem, and generating job opportunities. New AI applications, often designed to improve efficiency and enhance user experiences, not only benefit businesses but also hold the potential to address global challenges, making AI a driving force in technological and economic advancement. Additionally, the evolving landscape may lead to regulatory adaptations, ensuring responsible AI use and providing a clear framework for sustained market growth.

Market Segmentation

By Technology

  • The Machine Learning Is The Fastest Growing Segment, Growing At A CAGR Of 36.42% During The Forecast Period.

By Technology, the global Artificial Intelligence in Manufacturing Market is divided into Machine Learning, Natural Language Processing, Computer Vision, And Others
Machine learning stands as the predominant technology in the AI manufacturing landscape, capturing more than 67% of the market share. Its dominance is attributed to its inherent capability to glean insights from data and make predictive assessments—a fundamental requirement in various manufacturing applications, including predictive maintenance, quality control, and supply chain optimization. Following closely is Natural Language Processing (NLP), securing the second position with a market share exceeding 15%. NLP empowers machines to comprehend and interact with human language, facilitating applications like customer service chatbots and automated product documentation generation. Occupying the third spot is Computer Vision, contributing with a market share surpassing 10%. This technology specializes in image and video analysis, enabling applications such as defect detection and product inspection. Additionally, emerging technologies like robotics, augmented reality, and virtual reality are in the early stages of their development and hold transformative potential for the manufacturing sector.

By Offering

  • The services is the fastest growing segment, growing at CAGR of 29.13% during the forecast period.

By Offering, the global Artificial Intelligence in Manufacturing Market is divided into Hardware, Software, Services.
Software takes the lead in the AI in manufacturing market due to its scalability, adaptability, and capacity to automate critical manufacturing processes, making it a fundamental choice for enhancing operational efficiency and data analytics. With the flexibility to cater to various manufacturing needs, software solutions are favored for their cost-effectiveness and data-driven decision-making capabilities.

Conversely, AI services emerge as the fastest-growing segment, offering specialized expertise that manufacturers may lack in-house. These services facilitate the rapid and tailored implementation of AI solutions, ensuring the seamless integration of AI into manufacturing processes. They play a pivotal role in overcoming implementation challenges, addressing unique production needs, and providing ongoing support to prevent disruptions, making them a crucial component for the successful adoption of AI in manufacturing.

By Application

  • The predictive maintenance and machinery inspection dominated
  • The supply chain optimization is the fastest growing segment, growing at a CAGR of 20.43% during the forecast period

By Application, the global Artificial Intelligence in Manufacturing Market is divided into Predictive Maintenance And Machinery Inspection, Supply Chain Optimization, Quality Control, Production Planning And Optimization, And Others.

Predictive maintenance and machinery inspection currently dominate the AI applications in manufacturing landscape, holding a significant market share of over 32.3%. Maintenance serves as a crucial tool for mitigating downtime and elevating operational efficiency by preemptively identifying potential equipment failures. This proactive approach minimizes costly repairs and disruptions to the production process. The manufacturing industry is witnessing a surge in the collection of sensor data, offering valuable insights into equipment performance. AI plays a pivotal role in deciphering these data patterns, allowing for the early detection of potential malfunctions. Also, ongoing development efforts are yielding sophisticated AI algorithms tailored for predictive maintenance, enhancing accuracy and effectiveness.

However, supply chain optimization is poised to emerge as the fastest-growing AI application within manufacturing in the coming years due to globalization and the demand for just-in-time manufacturing have intensified the intricacies of supply chains. This complexity necessitates AI-driven optimization, as manual efforts struggle to keep pace. Supply chain optimization offers substantial cost-saving potential by eliminating inefficiencies and waste. The development of specialized AI algorithms designed for supply chain optimization is unleashing its full potential, making it more effective than ever before. This evolution underscores its rapid growth in the manufacturing landscape.

By End-user Industries

  • The Automotive Segment Dominated The Artificial Intelligence In Manufacturing Market In 2022.
  • Healthcare Is The Fastest Growing Segment, Growing At A CAGR Of 26.45% During The Forecast Period.

By End-Use Industries the Artificial Intelligence in Manufacturing Market is divided by automotive, electronics and semiconductors, aerospace and defense, healthcare, food and beverage, and others.

The automotive industry has taken the lead in the AI in manufacturing market, primarily due to its vast scale, global reach, and heavy reliance on automation. AI technologies have been instrumental in automating production processes, improving quality control, and enhancing efficiency. Additionally, the automotive sector benefits from AI's support in supply chain management, customizing vehicles to individual preferences, and driving innovation in connected and autonomous vehicles.

Conversely, the healthcare sector is the fastest-growing segment in AI manufacturing, thanks to its abundance of data generated from electronic health records, medical images, and clinical notes. AI's role in personalized medicine, early disease diagnosis, and treatment personalization is revolutionizing patient care. The healthcare industry leverages AI for drug discovery, remote monitoring, and predictive analytics, with an emphasis on enhancing operational efficiency and extending accessible healthcare services. Regulatory support and increased investment in AI further fuel its growth in the healthcare sector, addressing critical health challenges and evolving patient needs.

By Region

  • The North America Region Dominated The Artificial Intelligence In Manufacturing Market In 2022.
  • The APAC Region Is The Fastest Growing Segment, Growing At A CAGR Of 21.6% During The Forecast Period.

By region, the global Artificial Intelligence in Manufacturing Market is divided into North America, Europe, APAC and Others. Others is further divided into Middle East, Africa and South America.
The North American region is currently the leading market for AI in manufacturing, with a market share of over 32.65% due to its renowned tech innovation hubs, such as Silicon Valley, and a mature ecosystem of startups and research institutions actively collaborating to drive AI advancements. The region benefits from substantial investments, a wealth of venture capital, and a skilled workforce graduating from top universities. The North American manufacturing sector has been quick to adopt AI technologies, enhancing processes and supply chains, which contributes to its leadership in this field.

In contrast, the Asia-Pacific (APAC) region is the fastest-growing market for AI in manufacturing. APAC's success can be attributed to its substantial population and economic growth, fostering an increased demand for manufacturing. Government support and initiatives in countries like China and India propel AI development, while a burgeoning startup ecosystem tailors AI solutions to local manufacturing challenges. APAC's adaptive approach to adopting AI, coupled with diverse manufacturing sectors, drives the rapid growth and integration of AI technologies into manufacturing processes, making it a dynamic force in the global AI landscape.

Competitive Landscape

The global Artificial Intelligence in Manufacturing Market is consolidated with the presence of few major players contributing to the market revenue. This dominance of these major players is driven by their technological expertise, extensive resources, and established brand recognition. These companies typically offered comprehensive and diversified solutions to end use industries.

  • Siemen

Siemens AG, commonly known as Siemens, is a global conglomerate headquartered in Munich, Germany. Founded in 1847 by Werner von Siemens, it has grown to become one of the world's largest and most diversified technology companies.

  • IBM

International Business Machines Corporation, commonly known as IBM, is a multinational technology and consulting company with a rich history dating back to its founding in 1911. IBM has played a pivotal role in the development of computer technology and is known for its innovations in hardware, software, and services.

  • Intel

International Business Machines Corporation, commonly known as IBM, is a multinational technology and consulting company with a rich history dating back to its founding in 1911. IBM has played a pivotal role in the development of computer technology and is known for its innovations in hardware, software, and services.

  • NVIDIA
  • General Electric
  • Oracle
  • SAP
  • Bosch
  • Rockwell Automation
  • ABB
  • Honeywell
  • Schneider Electric
  • Emerson Electric
  • PTC
  • GE Digital
  • Dassault Système
  • Autodesk
  • Ansys
  • Cognite
  • Microsoft

Recent Developments

  • In June 2023, Google and Siemens unveiled their collaboration, aiming to advance AI-driven manufacturing solutions. This partnership focuses on creating innovative AI algorithms and applications that enhance manufacturing processes, minimize operational downtime, and refine quality control standards.
  • In May 2023, IBM and GE joined forces to pioneer AI-based predictive maintenance solutions. This strategic partnership involves the development of cutting-edge AI algorithms designed to forecast equipment failure, enabling proactive maintenance and preventing costly operational interruptions.
  • April 2023 witnessed Microsoft and Rockwell Automation's collaboration, where they are dedicated to the development of AI-powered manufacturing solutions. Together, they are crafting state-of-the-art AI algorithms and applications to optimize manufacturing processes, reduce expenses, and elevate quality control measures.
  • In March 2023, NVIDIA and Bosch announced a groundbreaking collaboration, focusing on AI-powered manufacturing solutions. Through this partnership, they are committed to delivering advanced AI algorithms and applications that enhance manufacturing processes, elevate quality control, and drive cost reduction efforts.

Artificial Intelligence in Manufacturing Market Scope

Report Components Details
Base Year

2022

Forecast Period

2023 – 2030

Quantitative Units

Revenue in US $

Drivers

Increasing adoption of Industry 4.0 technologies

Growing need for data-driven decision-making

Development of new AI technologies

Challenges

Lack of skilled workforce

Data security and privacy concerns

High initial investment

Trends

Development of new AI applications

Expansion into new markets

Improvement in product quality

 

Segments Covered

By Offering (Hardware, Software, And Services), By Technology (Machine Learning, Natural Language Processing, Computer Vision, And Others), By Application (Predictive Maintenance And Machinery Inspection, Supply Chain Optimization, Quality Control, Production Planning And Optimization, And Others), By End-Use Industry (Automotive, Electronics And Semiconductors, Aerospace And Defense, Healthcare, Food And Beverage, And Others),

 

Countries Covered

U.S. and Canada in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the APAC, Others include Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA), Brazil, Argentina, Mexico, and Rest of South America as part of South America

 

Market Players Covered

Siemens, IBM, Intel, NVIDIA, General Electric, Oracle, SAP, Bosch, Rockwell Automation, ABB, Honeywell, Schneider Electric, Emerson Electric, PTC, GE Digital, Dassault Systèmes, Autodesk, Ansys, Cognite, Microsoft

 

Table of Contents

1 INTRODUCTION OF GLOBAL ARTIFICIAL INTELLIGENCE IN MANUFACTURING MARKET

1.1 Overview of the Market

1.2 Scope of Report

1.3 Assumptions

 

2 EXECUTIVE SUMMARY

 

3 RESEARCH METHODOLOGY

3.1 Data Mining

3.2 Validation

3.3 Primary Interviews

3.4 List of Data Sources

 

4 GLOBAL ARTIFICIAL INTELLIGENCE IN MANUFACTURING MARKET OUTLOOK

4.1 Overview

4.2 Market Dynamics

4.2.1 Drivers

4.2.2 Restraints

4.2.3 Opportunities

4.3 Porters Five Force Model

4.3.1. Bargaining Power of Suppliers

4.3.2. Threat of New Entrants

4.3.3. Threat of Substitutes

4.3.4. Competitive Rivalry

4.3.5. Bargaining Power among Buyers

4.4 Value Chain Analysis

 

5 GLOBAL ARTIFICIAL INTELLIGENCE IN MANUFACTURING MARKET, BY OFFERING

5.1 Overview

5.2 Hardware

5.3 Software

5.4 Services

6 GLOBAL ARTIFICIAL INTELLIGENCE IN MANUFACTURING MARKET, BY TECHNOLOGY

6.1 Overview

6.2 machine learning

6.3 Natural language processing

6.4 Computer vision

6.5 Others

7 GLOBAL ARTIFICIAL INTELLIGENCE IN MANUFACTURING MARKET, BY APPLICATION

7.1 Overview

7.2 Predictive maintenance and machinery inspection

7.3 Supply chain optimization

7.4 Quality control

7.5 Production planning and optimization

7.6 others

8 GLOBAL ARTIFICIAL INTELLIGENCE IN MANUFACTURING MARKET, BY END-USE INDUSTRIES

8.1 Automotive

8.2 Electronics and semiconductors

8.3 Aerospace and defense

8.4 Healthcare

8.5 Food and beverage

8.6 Others.

9 GLOBAL ARTIFICIAL INTELLIGENCE IN MANUFACTURING MARKET, By REGION

9.1 North America

9.1.1 U.S.

9.1.2 Canada

9.2 Europe

9.2.1 Germany

9.2.3 U.K.

9.2.4 France

9.2.5 Rest of Europe

9.3 Asia Pacific

9.3.1 China

9.3.2 Japan

9.3.3 India

9.3.4 South Korea

9.3.5 Singapore

9.3.6 Malaysia

9.3.7 Australia

9.3.8 Thailand

9.3.9 Indonesia

9.3.10 Philippines

9.3.11 Rest of Asia Pacific

9.4 Others

9.4.1 Saudi Arabia

9.4.2 U.A.E.

9.4.3 South Africa

9.4.4 Egypt

9.4.5 Israel

9.4.6 Rest of Middle East and Africa (MEA)

9.4.7 Brazil

9.4.8 Argentina

9.4.9 Mexico

9.4.10 Rest of South America

10 COMPANY PROFILES

10.1 Siemens

10.1.1. Company Overview

10.1.2. Key Executives

10.1.3. Operating Business Segments

10.1.4. Product Portfolio

10.1.5. Financial Performance (As per availability)

10.1.6 Key News

 

10.2 IBM

10.2.1. Company Overview

10.2.2. Key Executives

10.2.3. Operating Business Segments

10.2.4. Product Portfolio

10.2.5. Financial Performance (As per availability)

10.2.6. Key News

 

10.3 Intel

10.3.1. Company Overview

10.3.2. Key Executives

10.3.3. Operating Business Segments

10.3.4. Product Portfolio

10.3.5. Financial Performance (As per availability)

10.3.6. Key News

 

10.4  NVIDIA

10.4.1. Company Overview

10.4.2. Key Executives

10.4.3. Operating Business Segments

10.4.4. Product Portfolio

10.4.5. Financial Performance (As per availability)

10.4.6. Key News

 

10.5 General Electric

10.5.1. Company Overview

10.5.2. Key Executives

10.5.3. Operating Business Segments

10.5.4. Product Portfolio

10.5.5. Financial Performance (As per availability)

10.5.6. Key News

 

10.6 Oracle

10.6.1. Company Overview

10.6.2. Key Executives

10.6.3. Operating Business Segments

10.6.4. Product Portfolio

10.6.5. Financial Performance (As per availability)

10.6.6. Key News

 

10.7 SAP

10.7.1. Company Overview

10.7.2. Key Executives

10.7.3. Operating Business Segments

10.7.4. Product Portfolio

10.7.5. Financial Performance (As per availability)

10.7.6. Key News

 

10.8 Bosch

10.8.1. Company Overview

10.8.2. Key Executives

10.8.3. Operating Business Segments

10.8.4. Product Portfolio

10.8.5. Financial Performance (As per availability)

10.8.6. Key News

 

10.9 Rockwell Automation

10.9.1. Company Overview

10.9.2. Key Executives

10.9.3. Operating Business Segments

10.9.4. Product Portfolio

10.9.5. Financial Performance (As per availability)

10.9.6. Key News

 

10.10 ABB

10.10.1. Company Overview

10.10.2. Key Executives

10.10.3. Operating Business Segments

10.10.4. Product Portfolio

10.10.5. Financial Performance (As per availability)

10.10.6. Key News

 

10.11 Honeywell

10.11.1. Company Overview

10.11.2. Key Executives

10.11.3. Operating Business Segments

10.11.4. Product Portfolio

10.11.5. Financial Performance (As per availability)

10.11.6. Key News

 

10.12 Schneider Electric

10.12.1. Company Overview

10.12.2. Key Executives

10.12.3. Operating Business Segments

10.12.4. Product Portfolio

10.12.5. Financial Performance (As per availability)

10.12.6. Key News

 

10.13 Emerson Electric

10.13.1. Company Overview

10.13.2. Key Executives

10.13.3. Operating Business Segments

10.13.4. Product Portfolio

10.13.5. Financial Performance (As per availability)

10.13.6. Key News

 

10.14  PTC

10.14.1. Company Overview

10.14.2. Key Executives

10.14.3. Operating Business Segments

10.14.4. Product Portfolio

10.14.5. Financial Performance (As per availability)

10.14.6. Key News

 

10.15 GE Digital

10.15.1. Company Overview

10.15.2. Key Executives

10.15.3. Operating Business Segments

10.15.4. Product Portfolio

10.15.5. Financial Performance (As per availability)

10.15.6. Key News

 

10.16 Dassault Systèmes

10.16.1. Company Overview

10.16.2. Key Executives

10.16.3. Operating Business Segments

10.16.4. Product Portfolio

10.16.5. Financial Performance (As per availability)

10.16.6. Key News

 

10.17 Autodesk

10.17.1. Company Overview

10.17.2. Key Executives

10.17.3. Operating Business Segments

10.17.4. Product Portfolio

10.17.5. Financial Performance (As per availability)

10.17.6. Key News

 

10.18 Ansys

10.18.1. Company Overview

10.18.2. Key Executives

10.18.3. Operating Business Segments

10.18.4. Product Portfolio

10.18.5. Financial Performance (As per availability)

10.18.6. Key News

 

10.19 Cognite

10.19.1. Company Overview

10.19.2. Key Executives

10.19.3. Operating Business Segments

10.19.4. Product Portfolio

10.19.5. Financial Performance (As per availability)

10.19.6. Key News

 

10.20 Microsoft

10.20.1. Company Overview

10.20.2. Key Executives

10.20.3. Operating Business Segments

10.20.4. Product Portfolio

10.20.5. Financial Performance (As per availability)

10.20.6. Key News

Global Artificial Intelligence in Manufacturing Market Segmentation

Artificial Intelligence in Manufacturing by Technology: Market Size & Forecast 2023-2030

  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Others

Artificial Intelligence in Manufacturing by Offering: Market Size & Forecast 2023-2030

  • Hardware
  • Software
  • Services

Artificial Intelligence in Manufacturing by Application: Market Size & Forecast 2023-2030

  • Predictive Maintenance And Machinery Inspection
  • Supply Chain Optimization
  • Quality Control
  • Production Planning And Optimization
  • Others

Artificial Intelligence in Manufacturing by End Use Industries: Market Size & Forecast 2023-2030

  • Automotive
  • Electronics And Semiconductors
  • Aerospace And Defense
  • Healthcare
  • Food And Beverage
  • Others

Artificial Intelligence in Manufacturing by Geography: Market Size & Forecast 2023-2030

  • North America (USA, Canada, Mexico)
  • Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
  • Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
  • South America (Brazil, Argentina, Columbia, Rest of South America)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

By Major Players: Market Size & Forecast 2023-2030

  • Siemen
  • IBM
  • Intel
  • NVIDIA
  • General Electric
  • Oracle
  • SAP
  • Bosch
  • Rockwell Automation
  • ABB
  • Honeywell
  • Schneider Electric
  • Emerson Electric
  • PTC
  • GE Digital
  • Dassault Système
  • Autodesk
  • Ansys
  • Cognite
  • Microsoft

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