Global Artificial Intelligence in Manufacturing Market Size, Share & Industry Trends Analysis Report By Offering (Software, Hardware, and Services), By Application, By Technology, By Industry, By Regional Outlook and Forecast, 2022 – 2028

Global Artificial Intelligence in Manufacturing Market Size, Share & Industry Trends Analysis Report By Offering (Software, Hardware, and Services), By Application, By Technology, By Industry, By Regional Outlook and Forecast, 2022 – 2028

The Global Artificial Intelligence in Manufacturing Market size is expected to reach $21.3 billion by 2028, rising at a market growth of 43.4% CAGR during the forecast period.

In recent years, artificial intelligence has become one of the fastest-growing technology. AI is linked to human intellect and shares similar traits like language comprehension, thinking, learning, problem solving, and so on. In the development and revision of such a technology, manufacturers in the market face huge underlying intellectual obstacles. AI is at the heart of the market's next-generation software technologies.

The implementation of artificial intelligence in the manufacturing sector is growing due to increased automation in the manufacturing industry and increased demand for big data integration. Moreover, the artificial intelligence in manufacturing market is being driven by the increased use of machine vision cameras in industrial applications such as machinery inspection, material movement, field service, and quality control. Furthermore, leading market players are employing a variety of methods, such as product launch and product innovation, to increase their current product portfolio and maintain competition in the quickly expanding AI industry. Oracle, for example, released new artificial intelligence-based apps for supply chain, manufacturing, and other professionals in October 2017. IBM released Watson Assistant, an AI-powered business assistant, in 2018. This product is an artificial intelligence-enabled smart enterprise assistant.

Artificial intelligence (AI) in manufacturing supply chains can forecast demand trends for items across time, socioeconomic segments, and geographical marketplaces. Prominent corporations are incorporating AI into their systems in order to boost client happiness. For example, GE launched the Brilliant Manufacturing Suite to enable customers to create their Brilliant Factory concept. Moreover, the use of automation and big data in the manufacturing business reduces risks throughout manufacturing processes and allows customers to receive rapid responses, improving the customer experience. However, some human vocations are projected to be replaced by AI-based technological systems in the near future.

COVID-19 Impact

To stop the spread of the COVID-19 pandemic, unprecedented lockdowns were enacted around the world, and many manufacturing units were shut down. The pandemic wreaked havoc on the global population and the virus killed millions of people around the world. It also had a drastically negative impact on the manufacturing sector. The disposable income of many people dropped. This resulted in lower demand for industrial items and, as a result, slowed global economic activity. Many countries are in the process of recovering from the ill effects of the pandemic. Improvements in manufacturing plant operating efficiency, rising application of AI in intelligent business operations, and increasing deployment of automation technologies to mitigate the consequences of COVID-19 are all prospects for AI in the manufacturing industry.

Market Growth Factors

Developing Industrial IoT and Automation Technologies

The Industrial Internet of Things (IIoT) enables an architecture that offers real-time information about operational and business systems, making industrial operations more efficient, productive, and inventive. The data collected by IoT devices must be transformed into instructions that tell machines how to execute specific tasks. These instructions were created by an AI system that used deep learning, context awareness, and natural language processing to learn human behavior (NLP). AI-based systems function faster and are less likely to make mistakes. As a result, manufacturing efficiency improves, assisting in business expansion.

Demand for AI in Manufacturing Being Driven by Increasing Volume of Complex Datasets

Companies in the manufacturing industry have access to a wealth of data collection and tracking resources. Big data, also known as sensor data, production data, IoT-driven systems, and manufacturing software, is exceedingly massive and difficult for humans to understand. Since vast volumes of structured and unstructured data from a variety of sources can be examined rapidly, AI and big data analytics have emerged as viable solutions for important manufacturing concerns. Organizations can interpret data and detect anomalies using AI and machine learning algorithms, minimize maintenance costs, improve customer service, increase predictive and preventative maintenance, and use raw data to support decision-making.

Market Restraining Factors

Hesitancy Among Manufacturers to Adopt AI-based Technologies

Artificial intelligence (AI) provides firms with tools to improve their predictive maintenance and machinery inspection processes. But, manufacturers, on the other hand, are hesitant to incorporate new technologies, particularly AI-based solutions, into their expensive machines and equipment. Any errors in management could increase the costs. Furthermore, many manufacturers are skeptical of AI-based systems' ability to accurately perform maintenance and inspection tasks. Given these considerations, persuading manufacturers and persuading them that AI-based solutions are cost-effective, effective, and safe is a little more difficult. However, some manufacturers are increasingly accepting of the potential benefits of AI-based solutions and the range of applications they can support.

Offering Outlook

Based on Offering, the market is segmented into Software, Hardware, and Services. The hardware segment procured a substantial revenue share in the artificial intelligence in manufacturing market in 2021. The AI program is executed on hardware that includes processors and logic circuits, among other things. AI requires its processing units, which is anticipated to increase hardware demand. In addition, according to PWC, AI hardware vendors are expanding and consolidating their market share.

Application Outlook

Based on Application, the market is segmented into Predictive maintenance & Machinery Inspection, Inventory Optimization, Quality Control, Cybersecurity, Industrial robots, Field Services, Production Planning, and Others. The predictive maintenance and machinery inspection segment acquired the largest revenue share in the artificial intelligence in manufacturing market. This is due to these technologies (vibration analysis, infrared thermography, motor circuit analysis, and so on) detecting faults that were missed by prior inspection methods while the machine is in use. Condition monitoring, also known as predictive maintenance (PdM), is the early detection and eradication of equipment faults that could result in unplanned downtime or wasteful expenditures using condition-based monitoring technologies, statistical process control, or equipment performance.

Technology Outlook

Based on Technology, the market is segmented into Machine Learning, Computer Vision, Natural Language Processing, and Context-aware Computing. The computer vision segment procured a substantial revenue share in the artificial intelligence in manufacturing market in 2021. This is due to artificial intelligence, along with computer vision techniques, aiding in the faster completion of jobs. The robots can better understand and navigate in the factory environment and around humans with the use of computer vision. The use of AI-based computer vision in smart factories aids in the detection of flaws and problems in the final product. The technology improves the factory's workflow even further. Micron Technology Inc., for example, manufactures memory technology on silicon wafers. This is a highly precise and sophisticated process with a significant likelihood of flaws that are not visible to the naked eye.

Industry Outlook

Based on Industry, the market is segmented into Automotive, Food & Beverage, Pharmaceutical, Heavy Metals & Machine Manufacturing, Semiconductor & Electronics, and Others. The semiconductor and electronics segment procured a substantial revenue share in the artificial intelligence in manufacturing market in 2021. This is due to artificial intelligence (AI) being employed in the semiconductor and electronics sector for production planning, quality control, and material mobility. AI-based solutions are projected to aid manufacturers in lowering production costs, implementing new technologies, and integrating components. Electronics equipment production is a complicated process that necessitates real-time manufacturing data.

Regional Outlook

Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. The Asia Pacific region acquired the largest revenue share in the artificial intelligence in manufacturing market in 2021. To study innovative technologies, Japan has developed the Industrial Value Chain Initiative. Among the leading corporations that cater to big industries in the electric, IT, machinery, and automobile fields are Mitsubishi Electric, Fujitsu, Nissan Motor, and Panasonic. For a long time, connected machines and transport robots have been employed on the shop floor of corporations like Toyota. Furthermore, the government has promoted IT initiatives around the country. Information and communication technology, manufacturing processes, the automotive and industrial sectors, automation and robot technologies, and electronic parts are all strong areas for Japan.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Apple, Inc. and Google, Inc. are the forerunners in the Artificial Intelligence in Manufacturing Market. Companies such as IBM Corporation, Intel Corporation, and NVIDIA are some of the key innovators in the Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include NVIDIA Corporation, Cisco Systems, Inc., Microsoft Corporation, IBM Corporation, Intel Corporation, Oracle Corporation, Google LLC, Micron Technology, Inc., Siemens AG, and General Electric (GE) Co.

Recent Strategies Deployed in Artificial Intelligence in Manufacturing Market

Partnerships, Collaborations, and Agreements

Mar-2022: Cisco formed a partnership with Cogniac Corporation, a California-based provider of artificial intelligence (AI) image and video analysis. The partnership aimed to quicken the delivery of AI-powered computer vision applications at the edge of the network. The partnership enabled enterprise customers to build smarter, more automated spaces that improve employee productivity, enhance customer experiences, and automate operations without the need for new infrastructure. Enterprises could utilize video intelligence to create customized experiences for safer environments and smarter spaces with a Cisco Meraki cloud-managed smart camera solution. Customers are enabled to solve a wide variety of use cases that require analytics.

Dec-2021: NVIDIA collaborated with Sight Machine, a company that offers an analytics platform that helps solve critical challenges in quality and productivity. The collaboration aimed to apply machine learning to turn the chaos of factory data into insights for enhancing production. The collaboration joined Sight Machine's manufacturing data foundation with NVIDIA's AI platform to remove the last hurdle in the digital transformation of manufacturing - getting raw factory data ready for analysis. NVIDIA machine learning software running on NVIDIA GPU hardware would be guided by Sight Machine's manufacturing intelligence to process two or more orders of magnitude more data at the beginning of digital transformation projects.

Nov-2021: IBM joined hands with NeuReality, an Israeli AI systems and semiconductor company. Under the collaboration, both companies aimed to develop high-performance AI inference platforms designed to offer cost and power consumption enhancements for deep learning. The platforms would be built for verticals like finance, insurance, healthcare, manufacturing, and smart cities to adopt computer vision, natural language processing, recommendation systems, and other AI use cases.

Sep-2021: Google entered into a partnership with C3 AI, a leading enterprise AI software provider. It was a unique partnership aimed to assist organizations across multiple industries accelerates their application of artificial intelligence (AI) solutions. Under the partnership, both companies' global sales teams co-sold C3 AI's enterprise AI applications, running on Google Cloud. The entire product line of C3 AI's Enterprise AI applications, including industry-specific AI Applications, C3 AI Suite®, C3 AI CRM, and C3 AI Ex Machina, was made available on Google Cloud's global, secure, and low-latency infrastructure, enabling customers to run C3 AI on the industry's cleanest cloud.

Aug-2021: Google entered into a partnership with Tata Consultancy Services (TCS), an Indian information technology services and consulting company. The partnership aimed at creating experience centers for customers to analyze cloud solutions, apart from co-developing new solutions that offer digital consumer experiences in retail, manufacturing, and financial services. The partnership and the new Google Garages at TCS Pace Ports helped the companies enable enterprises with the vital capabilities required to adopt the cloud for purpose-led, sustainable growth.

Jun-2021: Nvidia entered into a partnership with Google Cloud, a company under Google offering cloud computing solutions. Under the partnership, Nvidia aimed to establish an AI-on-5G Innovation Lab, allowing network infrastructure players and AI software partners to develop, test, and deploy solutions that will help quicken the development of smart cities, smart factories, and other advanced 5G and AI applications. The lab would offer enterprises access to Google Cloud’s Anthos platform and NVIDIA accelerated computing hardware and software platforms that let them utilize data and AI to propel business performance, enhance operational efficiency, and optimize safety and dependability.

Jun-2021: Intel joined hands with PathPartner Technology, a dominant product R&D organization. Under the collaboration, PathPartner Technology provided an Artificial Intelligence (AI) technology-based arc welding defect detection solution to the manufacturing industry. PathPartner utilized its expertise in building state-of-the-art AI and Machine Vision software on Intel edge processors to aid manufacturers in fruitfully adopting the defect detection technology and expanding it to address wider Industry 4.0 use-cases. Intel developed its AI technology to address an expensive, age-old problem of manual defect detection in the robotic welding process. The AI uses a deep neural network-based inferencing engine to identify defects that are not possible for a human eye.

Jun-2021: Intel collaborated with EXOR International, JMA Wireless, and Telecom Italia. Intel aimed to build a 5G smart factory “from the ground up” to show the advantages of Industry 4.0 digitalization. Industry 4.0, also referred to as the fourth industrial revolution, integrates modern technologies like 5G, AI, IoT, AR/VR, and cloud and edge computing, to bring the digital transformation of manufacturing/production and related industries and value-creation processes.

Jun-2021: Microsoft signed an agreement with Mars, an American multinational manufacturer of confectionery, pet food, and other food products. Under the agreement, the companies built upon a longstanding technology relationship that would influence every touchpoint across the consumer experience. The latest agreement increased the reach of intelligent manufacturing supply chains. The companies cooperated to integrate data across a digital infrastructure that offered Mars business insights to accelerate growth and develop trust with customers and consumers through transparent experiences.

Apr-2021: Siemens teamed up with Google Cloud, a company under Google offering cloud computing solutions. The collaboration aimed to optimize factory processes and enhance productivity on the shop floor. Siemens integrated Google Cloud’s leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to assist manufacturers in innovating for the future.

Apr-2021: Siemens joined hands with Toyota, a Japanese multinational automotive manufacturer. The collaboration aimed to develop artificial intelligence (AI) that can forecast product abnormalities in aluminum die casting, an important stage in automotive air conditioning compressor production. The system was one of the world's first to utilize defect prediction AI for die casting. It enhanced quality and productivity by using the AI application in Industrial Edge, the Siemens edge computing platform for the industry.

Dec-2020: Microsoft entered into a partnership with SAP, a German multinational software corporation. The partnership empowered customers to design and manage intelligent digital supply chains and Industry 4.0 solutions in the cloud and at the edge. As a consequence of this partnership, organizations were able to use a comprehensive set of SAP Digital Supply Chain solutions on Microsoft Azure, including SAP solutions for digital manufacturing, SAP Intelligent Asset Management solutions, SAP Integrated Business Planning, and SAP Logistics Business Network.

Jul-2020: Google formed a partnership with Groupe Renault, a French multinational automobile manufacturer. The partnership is aimed at accelerating the digitization of Groupe Renault’s industrial system and Industry 4.0 transformation. Google Cloud's solutions and expertise in artificial intelligence (AI), machine learning (ML), and smart analytics, empowered Groupe Renault to enhance its supply chain and manufacturing efficiency, its production quality, and reduce its environmental impact through energy savings.

Mar-2020: Siemens teamed up with NEC, a company delivering communications and IT infrastructure. Under the collaboration, both companies aimed to offer a tracking and analysis solution for manufacturing that connects MindSphere®, the cloud-based, open Internet of Things (IoT) operating system from Siemens, and NEC's System Invariant Analysis Technology (SIAT). As per the agreement, NEC joined the MindSphere Partner Program, which offered NEC access to specialized technical training and helps from Siemens as well as numerous joint go-to-market capabilities.

Product Launches and Product Expansions

Mar-2022: Nvidia unveiled the fourth-generation NVIDIA DGX system, the world's foremost AI platform to be made with new NVIDIA H100 Tensor Core GPUs. These systems offer the scale required to meet the huge compute requirements of large language models, recommender systems, healthcare research and climate science. Each DGX H100 is packs 8 NVIDIA H100 GPUs, tethered as one by NVIDIA NVLink, and offers 2 petaflops of AI performance at new FP8 precision

Feb-2022: Intel released updates to its OpenVINO artificial intelligence (AI) developer toolkit, which enabled developers to utilize it to bring a wider range of intelligent applications to the edge. A manufacturer could utilize the toolkit to create a defect spotting system and a way to hear a machine’s motor for signs of failure with broader model support.

Feb-2022: Siemens launched NX™ software, its industry-leading software, and a part of the Xcelerator portfolio of software and services. This newest version of NX software utilizes advanced technologies like artificial intelligence (AI) and advanced simulation capabilities, whilst enhancing productivity and capability to help its community of designers, engineers, and manufacturers to innovate more quickly. The novel NX Topology Optimizer helps to make parts based purely on functional and design space needs, resulting in completely editable convergent bodies that would be almost impossible to design and engineer manually.

Acquisition and Mergers

Apr-2021: IBM completed the acquisition of myInvenio, a process mining software company based in Reggio Emilia, Italy. The acquisition provided organizations with data-driven software that helped them recognize the most impactful business processes to automate using AI – including sales, procurement, production, and accounting. It also advanced IBM's hybrid cloud and AI strategy by delivering a comprehensive suite of AI-powered automation capabilities for business automation to customers.

Scope of the Study

Market Segments covered in the Report:

By Offering

• Software
• Hardware
• Services

By Application

• Predictive maintenance & Machinery Inspection
• Inventory Optimization
• Quality Control
• Cybersecurity
• Industrial robots
• Field Services
• Production Planning
• Others

By Technology

• Machine Learning
• Computer Vision
• Natural Language Processing
• Context-aware Computing

By Industry

• Automotive
• Food & Beverage
• Pharmaceutical
• Heavy Metals & Machine Manufacturing
• Semiconductor & Electronics
• Others

By Geography

• North America
• US
• Canada
• Mexico
• Rest of North America
• Europe
• Germany
• UK
• France
• Russia
• Spain
• Italy
• Rest of Europe
• Asia Pacific
• China
• Japan
• India
• South Korea
• Singapore
• Malaysia
• Rest of Asia Pacific
• LAMEA
• Brazil
• Argentina
• UAE
• Saudi Arabia
• South Africa
• Nigeria
• Rest of LAMEA

Companies Profiled

• NVIDIA Corporation
• Cisco Systems, Inc.
• Microsoft Corporation
• IBM Corporation
• Intel Corporation
• Oracle Corporation
• Google LLC
• Micron Technology, Inc.
• Siemens AG
• General Electric (GE) Co.

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