Top 5 Applications of AI in Manufacturing
Research shows that 60% of manufacturing companies are using AI technologies [1]
Manufacturers often face various operational challenges such as unexpected machinery failure or defective product delivery.
Leveraging AI and machine learning, manufacturers can improve operational efficiency, inform new product launches, and customise product designs. As a result, they experience improved product quality, greater speed and visibility across the supply chain, and optimised inventory management.
Implementing AI in manufacturing is becoming increasingly popular. Hundreds of variables impact the production process and while these are very hard to analyse for humans, AI and machine learning models can easily predict the impact of individual variables in such complex circumstances. Here are the top 5 applications of AI in manufacturing:
Predictive Maintenance
Manufacturers can leverage AI technologies to predict potential downtime and incidents.
IoT sensors embedded into machinery help manufacturers identify faults in a production line, which are then fed into ML-powered predictive tools to forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. A single fault in equipment can significantly disrupt the entire production line, increasing downtime, and overall costs. Therefore, proper and timely maintenance of the machinery is crucial.
Quality Assessments
Quality assurance is the maintenance of a desired level of quality in a product.
Throughout the manufacturing process it may be difficult to detect its internal issues caused by factors such as faulty equipment. By merely observing the functionality of a product, experts often fail to identify the root causes of the problem leading to major flaws in the production process.
Assembly lines are data-driven, interconnected and autonomous networks. These lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI and ML technologies can easily identify areas for improvement, giving designers the option to address faults and enhance the product. Further down the process, AI systems can trigger an alert to users if an end-product is lower quality than expected so that they can react to make adjustments. This helps in improving the overall product quality and performance.
Inventory Management
Inefficiency in inventory management can result in significant overheads for manufacturing companies.
AI helps companies to predict consumer demand, manage supplier backorders and to optimise inventory stock levels. The technology consolidates, standardises and enriches data such as costs, time frames, and market-wide trends, providing the foundation for AI analytics to present data-driven recommendations that managers can either chose to accept, reject or revise. AI-powered demand forecasting tools are more accurate than traditional demand forecasting methods, enabling businesses to monitor and manage inventory levels based on supply and demand.
Generative Design
The traditional design process in manufacturing is linear.
A high amount of technical expertise is required through the use of sophisticated software and complex domain-specific tools, to produce a design. This can create a negative feedback loop if any step is overlooked during the validation or manufacturing phase, causing product recalls, redesign efforts, and a significant waste of resources. The creativity of designers is also limited to how fast they can generate designs, especially with tight schedules.
Generative design methods mimic an engineer’s approach to design. The software provides a multitude of possible outcomes given a set of parameters, such as materials, size, weight, budget, manufacturing methods, CO2 emissions. With this method, manufacturers can quickly generate thousands of design options; negative feedback loops are cut short by lowering the barriers to design. As an example, Under Armour leveraged generative design algorithms to create a shoe inspired by tree roots for optimal flexibility and stability [2].
Robotics
One of the most popular applications of AI and ML for manufacturers are AI-powered robots.
Industrial robots automate repetitive tasks, reduce human error, and allow human workers to focus on more creative and productive tasks. AI robots have the ability to monitor its own accuracy and performance and train itself to do better. Some are also equipped with machine vision technologies to achieve precise mobility when working alongside human teams on tasks that cannot be completely automated. Some applications of AI robots include assembly, product inspection, welding, drilling and grinding. For example, the Tesla Gigafactory is allegedly one of the most advanced factories ever built [3]. Robots are used there to self-navigate Autonomous Indoor Vehicles (AIVs) freely without beacons or magnets guiding them. Their main responsibility is to efficiently shift goods between workstations.
Artificial intelligence is the future of the manufacturing industry
Beyond the examples given above, there are numerous other applications of AI and ML in manufacturing many of which are designed to increase the supply chain efficiency and overall sustainability of a production line.
The role of AI in this industry will no doubt continue to grow as our environmental impact becomes more noticeable, computational resources become less costly and domain knowledge proliferates.