Introduction
We’ve entered the era of Artificial Intelligence in manufacturing. AI is now used in factories as an ‘assistant’ by following instructions through prompts and helping machines learn from data, recognize patterns, predict problems, and work with humans in decision-making.
How did we end up here? It all started with the first industrial revolution, when machines replaced hand tools and the world shifted from handmade to machine-made goods. Assembly lines were introduced soon after, and mass production became possible. Fast-forward a few decades, and computers entered the picture, making manufacturing processes more efficient.
As technology continues to advance, AI will move beyond just assisting to executing tasks with minimal human assistance. Some years from now, it won’t be surprising if your colleague is a robot!
AI has enabled factories to become smarter, faster, and safer, making its relevance in the manufacturing industry undeniable, prompting more manufacturers to integrate AI technology into their business models.
Overview of AI in Manufacturing
The shift to Industry 4.0
The manufacturing industry is undergoing a fourth industrial revolution (Industry 4.0) marked by the integration of AI in manufacturing processes. Before this, the industry witnessed other major shifts starting with engine power (water and steam), followed by electric power (assembly lines), and then computers, bringing us to today’s ‘smart’ factories.
Smart manufacturing is a term used when factory equipment is incorporated with AI systems, allowing equipment to monitor, analyse, and predict information, in turn making the manufacturing processes highly efficient. It goes beyond just basic automation and can support real-time decision making.
This huge leap forward is made possible by the Internet of Things (IoT). Think of sensors attached to machines; these sensors, when attached to manufacturing equipment, feed data from the equipment into AI systems over the Internet, so that various parameters such as temperature, speed, pressure, etc. can be monitored in real time.
Ultimately, smart manufacturing is not merely an upgrade but a strategic shift that is going to make manufacturing companies agile, competitive, and relevant going forward.
Let’s look at some AI technologies that have made these changes possible.
AI Technologies in Manufacturing
Machine Learning (ML) and Natural Language Processing (NLP) are considered the ‘brains’ behind AI technologies, making it onto the factory floors. Here’s a closer look at how AI technologies are being used in manufacturing processes.
Generative Design
Generative Design uses machine learning and can run through tons of designs and blueprints in minutes. This, in turn, allows product design teams to choose from various smart designs, helps in mass customization, creates faster design timelines, and accelerates the product development process.
Computer Vision Systems
These are super-fast AI-powered cameras that scan raw material for defects and are always on the lookout for anything that is ‘off’. These cameras can spot scratches, misalignments, and assembly errors in real time. They can even identify micro defects invisible to the human eye. Computer vision has redefined inspections and quality control workflows, especially in the food and beverage, automotive, and electronics industries, enabling inspection levels to reach amazingly high precision.
Digital Twins
Digital twins are virtual replicas of a factory setup or a manufacturing process that allow manufacturers to create simulations and test, analyze, and make predictions before launching the real product. By digitally mirroring a live setup, manufacturers can test changes and optimize their workflows before implementing them, reducing risks and improving efficiency.
Natural Language Processing
NLP reads and understands data from maintenance logs, quality control reports, and converts this data into insights or real-time answers that help managers and factory workers spot issues and reduce repetitive tasks.
Applications of AI in Manufacturing
Let’s take a closer look at the application of these AI technologies in manufacturing. A typical manufacturing process has basic steps such as Input ➡ ️Processing ➡ ️ Assembly ➡ ️ Finishing/ Testing ➡ ️Packaging. AI is gradually making its way into each of these steps, the most explored roles being machine failure detection and testing/ quality control.
Here are some of the main applications of AI in manufacturing and production.
AI in Supply Chain and Inventory Management
AI is used in forecasting demand, analysing seasonal trends, and maintaining optimal stock levels to avoid overproduction. It also plays a role in streamlining logistics by finding the most efficient routes and in ESG (Environmental, Social, and Governance) compliance to identify suppliers that meet the required regulations.
AI in Predictive Maintenance
One of the most impactful applications of AI has been predictive maintenance. Maintenance of equipment typically requires large amounts of downtime and huge costs. With manufacturers working with AI systems, they can predict failures before they occur, significantly reducing downtime and maintenance costs. This has been quite impactful in industries like automotive, aerospace, and energy manufacturing, where equipment reliability is crucial and has also helped extend equipment life.
AI in Quality Control
AI systems, with the help of computer vision, analyze products as they are manufactured and identify inconsistencies, scratches, and other flaws or defects with much greater accuracy than humans, resulting in less waste and rework. This has helped manufactured products meet stringent quality standards seamlessly.
AI in Industrial Robots
It’s hard to picture smart factories without industrial robots. Industrial robots are used in heavy-duty jobs like lifting and welding, and excel at repetitive tasks. Cobots (collaborative robots) work with humans on tasks to improve the efficiency and accuracy in manufacturing processes, bridging the gap between humans and machines.
Examples of AI in Manufacturing
AI in Automotive Manufacturing
- Tesla and BMW use robotics in assembly lines to automate the entire production process. These robots inspect and fit various parts together as they move through the assembly line.
- AI systems have helped automotive manufacturing benefit by reducing emissions from vehicles, improving safety, and reducing breakdowns due to predictive maintenance.
AI in Aerospace Manufacturing
- AI is used to design lightweight flying material and in predictive maintenance systems to analyze engine data for early warning signs.
- GE Aerospace uses machine learning for automated inspections and predictive maintenance.
- Airbus uses AI in product development to design lighter aircraft components.
AI in Energy Manufacturing
- In oil and gas production, even tiny leaks can be dangerous and costly, making the role of AI in leak detection of grave importance.
- Kinder Morgan has integrated AI-driven leak detection across its pipelines to detect anomalies and send out alerts to maintenance crews, helping to reduce costs associated with downtime and improving safety conditions.
AI in Food Manufacturing
- Food manufacturers have greatly benefited from using computer vision to detect freshness levels and contamination to reduce wastage and improve food quality.
- Nestle has incorporated AI into its KitKat factory to expand production capacity and minimise food and packaging waste.
- Kellanova (Pringles) uses AI to refine recipe formulations and improve food compliance.
AI in Electronics & Semiconductors Manufacturing
- Manufacturing semiconductors involves high-precision work. AI detection systems have helped catch microscopic flaws and improve production processes.
- At LG Innotek, robotic arms have been used to peel off protective films from panels, a manual process otherwise prone to scratches.
AI in Steel Manufacturing
- Steel mills run 24/7, and AI has helped optimize operations, reduce energy consumption, and improve safety conditions.
- Tata Steel has built over 550 AI models for enhancing yield and productivity.
AI in Heavy Machinery Manufacturing
- Building heavy machinery involves lifting heavy parts and repetitive tasks; cobots have played a big role in revolutionising this kind of manufacturing.
- Mahindra & Mahindra uses cobot-based applications that have increased production capacity.
As we look at the impact of AI across manufacturing industries, we can’t help but notice that the changes are going beyond just processes and equipment, to also changing the nature of the workforce.
Future of jobs in manufacturing
As the manufacturing industry incorporates all these advancements, it is natural to wonder, ‘What happens to our jobs?’ As with any major industrial shift, these changes not only bring changes in technology but also a shift in job roles, making it essential for the workforce to adapt and upskill.
Roles that may become redundant
- Repetitive and manual tasks, like product inspection, loading, unloading, and working on assembly lines, may require fewer staff than before. Robots and computer vision systems can handle these tasks faster, with much greater accuracy, and with no breaks.
- Routine data entry tasks and reporting, which involve typing maintenance logs or creating reports, will be redundant as AI tools can generate reports and flag anomalies much faster.
New opportunities that may emerge
- The flip side is that we can expect to see demand for AI maintenance and supervision growing, with new opportunities opening up for
- AI Engineers/ AI Specialists
- Data Analysts/ Insights Engineers
- Cobot & Robotic Coordinators
- As companies like Tata Steel continue to expand AI usage, they have also established facilities to train and upskill their employees.
Just as we began with the idea of a robot as a colleague, the future leans towards humans and robots working together, each working according to their strengths. It is important to note that AI may not exactly replace people, just like how machines did not replace the workforce during the first industrial revolution, but rather gave rise to a whole lot of new opportunities.
Conclusion
Seeing the manufacturing industry move from steam engines, assembly lines, and computers to now AI-powered factories, it’s clear that each phase has redefined how we look at companies, economies, and careers.
With AI’s role in driving change through predictive maintenance, automated inspections, and collaborative robots, the manufacturing industry is shifting its future outlook and leading the way with smarter, faster, and safer factories, showcasing the potential for more manufacturers to ‘shift’ and integrate AI into their business models.
If you found this article insightful, do check out our other articles on our AI Blog.