AI-Powered Manufacturing: 7 Use Cases of AI in Manufacturing Industry

How is AI Digitally Transforming the Manufacturing Industry?

Author: Inza Khan

Manufacturing companies have started to adopt artificial intelligence tools to address common challenges and gain a competitive edge. This also aligns with the widespread adoption of intelligent automation across different economic sectors. However, the impact of AI in manufacturing extends beyond efficiency improvements.

This blog explores additional use cases that highlight the practical applications of AI in addressing manufacturing industry challenges, providing insights into its versatile role.

Applications of AI in the Manufacturing Industry

1- Cobots

Collaborative robots, or cobots work closely with human workers. This teamwork is changing how we approach manufacturing tasks, offering a look into the future of collaborative manufacturing. Cobots are adaptable with learning to handle various tasks and navigating around obstacles. In practical terms, they assist with heavy lifting in automotive factories, work precisely on assembly lines, and even help locate and retrieve items in large warehouses. This collaboration boosts efficiency by allowing human workers to focus on more complex tasks, and it enhances safety by detecting and avoiding obstacles.

Key Use Cases:

• Heavy Lifting in Automotive Factories
• Precision on Assembly Lines
• Warehouse Automation
• Obstacle Detection and Avoidance

2- Digital Twin

A digital twin is like a virtual copy of a physical object, connected to it through smart sensors. These sensors send real-time data to the digital twin, providing a detailed view of how the object performs over time. This virtual copy allows AI to analyze data, find anomalies, and predict failures, letting manufacturers fix issues before they cause downtime. Digital twins use smart sensors, AI, and lifecycle monitoring to work. In the aviation industry, for instance, airplane engines have sensors that send data to their digital twins every time the plane flies. This helps airlines and manufacturers understand how the engine is doing, anticipate potential issues and plan maintenance. Digital twins offer benefits like predicting when maintenance is needed, running simulations, and optimizing performance.

Key Use Cases:

• Predictive Maintenance
• Lifecycle Monitoring
• Real-Time Data Exchange
• Proactive Issue Identification
• Remote Monitoring

3- Assembly Line Optimization

AI is transforming manufacturing by improving quality control, workflow efficiency, and resource management on assembly lines. Computer vision systems powered by AI scrutinize product details in real time, ensuring high standards and customer satisfaction. Machine learning algorithms analyze data to optimize workflows and predict maintenance needs, reducing downtime. AI-driven automation adapts production parameters to demand, minimizing waste and aligning with sustainability. Volkswagen’s use of AI for analyzing sensor data, predicting maintenance, and streamlining operations is a practical example of how AI enhances manufacturing efficiency and quality.

Key Use Cases:

• Quality Assurance through Computer Vision
• Predictive Maintenance for Downtime Reduction
• Resource Utilization with Intelligent Automation

4- Quality Assurance

By using AI-powered computer vision, companies can thoroughly analyze product images and videos with greater precision than humans. An example of this shift is seen in Foxconn, a major electronics manufacturer, which has incorporated AI and computer vision into its production lines. This allows for swift identification of flaws in electronic components, ensuring strict adherence to quality standards and improving overall production efficiency. The application of AI in manufacturing is not just about immediate control but also involves predicting potential quality issues. Machine Learning algorithms analyze historical and real-time sensor data to identify patterns, helping manufacturers address defects before affecting final product quality.

Key Use Cases:

• Computer Vision for Defect Detection
• Predictive Quality Assurance with Machine Learning
• Proactive Defect Addressing

5- Demand Forecasting

AI helps companies analyze sales data, market trends, and external factors more accurately than before by using advanced algorithms and machine learning. This real-time analysis lets manufacturers adjust production based on current market needs. AI in demand forecasting also means flexible pricing so that companies can adapt prices in real time for better profits and competitiveness. This doesn’t just affect production; it also helps with customer service. Accurate demand forecasts let companies plan better, ensuring products are available and orders are fulfilled efficiently. Amazon is a good example of AI in action, using algorithms to forecast demand, manage inventory, and improve delivery reliability. AI also helps with strategic inventory management, reducing risks of overstocking or running out of stock.

Key Use Cases:

• Flexible Pricing Management
• Enhanced Customer Service
• Strategic Inventory Management
• Market-Responsive Operations

6- Product Development

AR (Augmented Reality) and VR (Virtual Reality) can be used for simulations before production, reducing costs, and speeding up time-to-market. It helps predict and prevent problems, making maintenance and debugging efficient. This predictive ability improves product quality and minimizes issues after launch. AI also streamlines post-production processes, enhancing overall product reliability. It allows manufacturers to create advanced products and stay competitive. AI continuously gets feedback that helps to improve products over time. Tesla’s use of AI to simulate scenarios and optimize vehicle designs is a real-world example, reducing the need for extensive physical testing and enabling rapid innovation in electric vehicle production.

Key Use Cases:

• Faster Time-to-Market
• Predictive Problem-Solving
• Streamlined Maintenance and Debugging
• Continuous Improvement with Feedback Loops

7- Streamlining Paperwork through RPA

AI and ML simplify paperwork through Robotic Process Automation (RPA). In the conventional setup, dealing with purchase orders, invoices, and quality control reports often leads to delays and errors due to manual processes. To address these challenges, companies like Whirlpool are using Conversational AI to employ intelligent bots with AI capabilities. These bots automate paperwork tasks by extracting and organizing data from documents and inputting it into relevant systems. Whirlpool applies RPA in assembly lines and material handling tasks, ensuring accuracy and efficiency. This automation extends to quality control inspections, enhancing consistency and precision in evaluating finished products.

Key Use Cases:

• Paperwork Automation
• Quality Control Automation
• Standardization of Procedures

How is AI Changing the Manufacturing Industry?

The manufacturing sector is smartly adopting AI and machine learning. With 2.7 million industrial robots worldwide in 2020, a 12% increase from 2019 shows a commitment to using automation for better efficiency. Manufacturers are focusing on investing in AI and machine learning to cut production costs and improve time-to-market.

The aftermath of the global pandemic has increased the industry’s interest in resilience, making AI adoption essential for automating tasks, predicting disruptions, and ensuring control over operations. Examples like predictive maintenance, supply chain optimization, quality assurance, and demand forecasting demonstrate how AI is transforming manufacturing.