Important points
Integrating artificial intelligence into the manufacturing sector is no longer a futuristic concept, but a current reality, driving significant operational improvements.
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According to Grand View Research, the global AI in manufacturing market is expected to reach $47.88 billion by 2030, up from $5.32 billion in 2024. [1].
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Companies are using AI in manufacturing for predictive maintenance, quality control, and supply chain management.
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Jobs that use AI in manufacturing include factory managers, quality control inspectors, and process engineers.
Discover the uses of AI in manufacturing, common jobs that use AI in this field, and the pros and cons of AI in manufacturing. If you’re ready to start a career in AI, enroll in IBM’s AI Foundations forEveryone specialization. Learn machine learning software, application deployment, prompted engineering, data science, and more in as little as 4 weeks.
How is AI used in manufacturing?
Manufacturing companies use several types of artificial intelligence technologies during production to facilitate tasks such as predictive maintenance, quality control, supply chain management, and human-machine collaboration. See how different aspects of AI can assist at different stages of the manufacturing process.
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Predictive maintenance (PdM): Utilize sensors to collect data about machines and equipment. AI and machine learning (ML) technologies then analyze the collected information to build up-to-date representations of machines and equipment and identify potential defects and future maintenance needs.
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Quality control: Automated quality management systems leverage computer vision, AI, the Internet of Things (IoT), and robotics to quickly and accurately identify product defects and determine the quality of an organization’s products without the need for human intervention.
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Supply chain management: It involves the use of ML, predictive analytics, computer vision, generative AI (GenAI), and AI agents to develop more durable and efficient supply chains that can predict problems, automate tasks, and reduce operational expenses.
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Human-machine collaboration: Collaborative robots, or “cobots,” work in tandem with employees to improve efficiency, reduce errors, and supplement employee productivity. Cobots operate using a combination of sensor data, control software, and ML algorithms and can be found operating in various sections of the manufacturing process, including assembly, machine care, material handling, quality inspection, and welding.
read more: 10 machine learning algorithms you need to know
What kind of jobs will utilize AI in the manufacturing industry?
Across the manufacturing industry, you may see AI used in a variety of roles, including factory managers, process engineers, quality control inspectors, and IoT specialists. Explore these roles and how they use AI in more detail to see how AI can impact your work.
factory manager
This management role may use AI for predictive maintenance, quality control, production optimization, and inventory management. Additionally, AI can be employed to automate repetitive tasks and use data-driven insights to improve plant performance.
process engineer
As a process engineer, your goal is to make manufacturing processes more efficient. AI algorithms can be used to review datasets and analyze variables such as temperature, pressure, flow rate, and chemical concentration. Using this information, AI models can discover opportunities for improvement and adjust settings in real-time to optimize the entire manufacturing process.
quality control inspector
As a quality control inspector, you may work in an automated environment while using data and AI analysis to check product quality. You can also work with engineers and AI system integrators to solve core problems, improve AI model accuracy through continuous training, and ensure your organization adheres to compliance rules.
Industrial IoT (IIoT) Specialist
As an IIoT specialist, you can manage an ML-enabled IIoT network that learns from previous events and adapts based on new information. Using ML technology, these systems can also learn from operational failures, identify patterns unrecognizable to human workers, and perform historical data analysis to make autonomous decisions in real time. You can also implement AI-powered predictive maintenance to monitor equipment health and minimize the risk of downtime.
What is a digital twin and how does it benefit manufacturing?
In the manufacturing industry, a digital twin is a virtual representation of a product, process, or system. For example, if you are hiring new operators to work at your manufacturing facility, you can use a digital twin of your workspace to train your employees and help them avoid physical hazards while avoiding disruption to the production process. Benefits of using digital twins in manufacturing include optimizing production schedules, predicting machine problems, and increasing efficiency at every stage of the manufacturing process.
Real-world AI use cases in manufacturing: Examples
Major companies like Siemens, General Electric (GE), PepsiCo, and Amazon are already working with AI to enhance their manufacturing processes in some way. Let’s take a closer look at how these companies are leveraging AI to benefit their manufacturing processes.
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Siemens: Global technology company Siemens provides the Andretti Global Racing organization with a digital twin tool that allows them to build virtual models of race cars. Designers can simulate vehicle schematics, test components, and virtually predict race car performance before starting the manufacturing process. [3].
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General Electric: To monitor equipment functionality and prevent costly downtime in manufacturing facilities, GE is focusing on AI-powered predictive maintenance. AI analyzes data from sensors to predict possible failures and recommend maintenance schedules. [4].
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PepsiCo: The organization is moving to a planning strategy based on digital tools, leveraging AI agents and physics-compliant digital twins as co-designers. This allows you to simulate, approve and optimize the layout of your production plant before starting the construction process. [5].
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Amazon: The company uses generative AI to make workspaces more ergonomic for employees, develop smarter warehouse robots, better predict where to store new inventory, and optimize delivery schedules. [6].
What are the advantages and disadvantages of artificial intelligence in manufacturing?
Introducing AI in manufacturing processes offers several benefits such as accuracy, financial savings, and energy efficiency, but it may also face some challenges. Identify the advantages and disadvantages of incorporating AI into manufacturing.
Strong Points
AI in manufacturing has several benefits, including improved accuracy, reduced costs, strengthened supply chains, and increased energy efficiency. Let’s take a closer look at these advantages.
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Improved quality and accuracy: AI-based systems can be used for quality control, and these systems analyze the product and compare the results to fixed standards. By leveraging AI for quality control, product defects can be removed before mass production.
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Improving supply chain and inventory management: Leveraging AI in manufacturing can provide up-to-date insights into your supply chain and inventory. By doing so, you can match supply and demand and reduce financial losses caused by having too much or too little product in stock.
Cons
When using AI in manufacturing, you may also need to consider some disadvantages, such as reduced data quality, cybersecurity threats, and workforce skill gaps. We will detail some of the disadvantages you may face.
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Data quality: Manufacturing facilities typically operate using legacy systems, which can generate insufficient or inconsistent data, and AI requires good data to be effective. Before deploying AI, audit data accuracy and properly calibrate data collection equipment.
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Cybersecurity threats: Incorporating AI tools into technologically interconnected systems adds new entry points and increases an organization’s attack surface. IoT sensors and home transducers collect real-time data about manufacturing operations and forward that information to interconnected AI tools, so these connectivity vectors must be protected from malicious actors.
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Employee skills gap: If you don’t have enough people with AI expertise, your predictive models may not be optimized. When integrating AI into your manufacturing processes, consider hiring someone who understands how machine learning and data science work.
What does the future hold for industrial AI in manufacturing?
The use of industrial AI in manufacturing is most likely to continue to grow. According to Grand View Research, the global AI in manufacturing market size was $5.32 billion in 2024, and the research organization expects its value to reach $47.88 billion by 2030. [1]. Additionally, regarding the importance of AI in this industry, Deloitte surveyed 600 manufacturing executives and found that 85% “believe their company’s smart manufacturing efforts will transform the way products are made, increase agility, and attract new manufacturing talent.” [2].
Additionally, more personalized product recommendations appear to be a future aspect of AI in manufacturing, as this technology allows you to design the exact product you want and customize it to your specific needs.
How can you start a career leveraging AI in manufacturing?
If you’re planning a role in manufacturing that uses AI, you’ll need to develop several skills, including AI literacy, data analysis, and basic coding. Consider some options for increasing your competency in the following areas:
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AI: Several institutions, such as the University of Maryland and Stanford University, offer online certificates focused on developing AI skill sets. You can also explore AI boot camps at universities like Massachusetts Institute of Technology (MIT), Northwestern University, Arizona State University, and Columbia University. Earning a bachelor’s degree is also an option, and potential majors for future AI employees include computer engineering, computer science, and electrical engineering.
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Data analysis: Several universities offer data analytics bootcamps. Institutions offering these include Arizona State University, Columbia University, Case Western University, and Michigan State University. If a bachelor’s degree suits your plans better, consider majoring in business intelligence. You can also pursue a degree in computer science, statistics, or information systems.
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coding: Programming boot camps are another possibility to improve your skill set in this field. Colorado State University, Johns Hopkins University, and Carnegie Mellon University offer coding bootcamps. As for my degree, I would like to major in subjects such as computer programming, computer science, or software engineering.
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