Artificial intelligence in Manufacturing market size is valued at USD 2.3 billion in 2022 and is anticipated to be USD 16.3 billion by 2027; growing at a CAGR of 47.9% from 2022 to 2027. The growing demand of factors such as improving computing power of AI chipsets is expected to grow the market at an estimated rate.
Increasing adoption of AI has been observed as a new driver for semiconductor chipset manufacturers in the past few years. GPU/CPU manufacturers, such as NVIDIA, AMD, Intel, Qualcomm, Huawei, and Samsung, have significantly invested in AI hardware for the development of chipsets that are compatible with AI-based technologies and solutions. Apart from CPUs and GPUs, application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are being developed for AI applications. For instance, Google has built a new ASIC called “tensor processing unit” (TPU).
Compute-intensive chipset is among the critical parameters for processing AI algorithms; the faster the chipset, the quicker it can process data required to create an AI system. Currently, AI chipsets are mostly deployed in data centers/high-end servers as end computers are currently incapable of handling such huge workloads and do not have enough power and time frame. NVIDIA has a range of GPUs that offer GPU memory bandwidth based on application. For example, GeForce GTX Titan X offers memory bandwidth of 336.5 GB/s and is mostly deployed in desktops, while Tesla V100 16 GB offers memory bandwidth of 900 GB/s and is used in AI applications.
Application of AI for intelligent business processes
Rigid and rule-based software currently governs a majority of business processes in an organization, offering limited abilities to handle critical problems. These processes are time-consuming and require employees to work on repetitive tasks, hampering the productivity of the employees and the overall performance of the organization. Machine Learning and Natural Language Processing tools generated on AI platforms can help enterprises overcome such challenges with self-learning algorithms, which can reveal new patterns and solutions. Most organizations use enterprise software, which make the use of rule-based processing to automate business processes. This task-based automation has helped organizations in improving their productivity in a few specific processes but such rule-based software cannot self-learn and improve with experience. The integration of AI tools, such as NLP and ML, generated on AI platform for enterprise software systems, enable the software to gain mastery while solving individual processes. This software would be able to provide improved performance and productivity to enterprises over time, instead of providing a one-time boost. All these factors are said to have fueled the demand for intelligent business processes and act as opportunities for the growth of the AI in manufacturing market.
Increasing global demand for energy and power is influencing energy and power companies to adopt AI-based solutions
The increasing global demand for energy and power is influencing energy and power companies to adopt AI-based solutions that can help boost production output with minimum maintenance and reduced downtime. Maintenance and inspection are the major issues, along with material movement, in a thermal plant as the material needs to travel a long distance inside the plant. Besides, equipment used in this industry, such as turbines, conveyer belts, grids, and voltage transformers, are costly. Moreover, there are issues related to fuel mix, ambient temperature, air quality, moisture, load, weather forecast models, and market pricing in the power industry. By using AI-based technologies, these issues can be resolved and predicted in the early stages. AI-based technologies used in energy plants comprise physics insights, engineering design knowledge, and new inspection technologies, which are ideal for predictive maintenance and machinery inspections. The AI technologies work in 2 layers. First, by recognizing the pattern, and second, by learning the models. Early-stage pattern recognition notifies about impending failures.
The breakup of primaries conducted during the study is depicted below:
- By Company Type: Tier 1 – 55 %, Tier 2 – 25%, and Tier 3 –20%
- By Designation: C-Level Executives – 60%, Directors – 20%, and Others – 20%
- By Region: North America– 40%, Europe – 30%, APAC – 20%, South America – 7% and Middle East and Africa - 3%
The key players operating in the artificial intelligence in Manufacturing market include NVIDIA (US), IBM (US), Intel (US), Siemens (Germany), General Electric (US), Google (US), Microsoft Corporation (US), and Micron Technology (US).
The report segments the Artificial intelligence in Manufacturing market and forecasts its size, by value, based on region (North America, Europe, Asia Pacific, and RoW), Application ( predictive maintenance and machinery inspection, inventory optimization, production planning, field services, quality control, cybersecurity, industrial robots and reclamation), Technology (machine learning, natural language processing, context-aware computing, computer vision), Offering ( hardware, software and services) and Industry (automotive, energy & power, semiconductor & electronics, pharmaceutical, heavy metals & machine manufacturing, food & beverage and others (textile, aerospace and mining)). The report also provides a comprehensive review of market drivers, restraints, opportunities, and challenges in the head-up display market. The report also covers qualitative aspects in addition to the quantitative aspects of these markets.
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- This report includes market statistics pertaining to the offering, technology, industry, application, and region.
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