In the history of industrial revolutions, the Industry 4.0 corresponds to the digitalization of manufacturing. AI combined with connected devices (the IoT), robotics and data analysis allowed companies to automate processes further.
The integration of AI is transforming the manufacturing techniques by automating repetitive, time-consuming tasks or tasks that are impossible for a human brain to perform. This increased production efficiency and accuracy for those willing to take the next step towards a smart factory with ‘cyber-physical production systems’.
With the up-and-coming fifth revolution (Industry 5.0), the collaboration between humans, AI and robots will reach a new stage. Human intelligence and creativity will be working with cognitive computing to switch from mass production to mass personalization. Thus, allowing production to respond immediately to changes in consumer preferences while keeping cost down.
AI is an excellent tool to improve the energetic performance of manufacturing processes.
By analyzing data on electricity usage, AI can help identify ways to reduce energy consumption. This could involve making changes to the production line or altering the way machines are used.
The savings can be huge. For example, a phosphate production plant in Belgium saves €500,000 in energy per year as a result of its use of PEPITe’s predictive machine learning technology.
Moreover, AI may be used to monitor and predict changes in energy prices. This information can then be used to negotiate better energy contracts or find new sources of energy when needed.
AI can also be used to streamline inventory management. By tracking production data and customer demand, AI will predict when stocks are running low and automatically order new supplies. This ensures that production never has to stop due to a lack of raw materials, and that customers always have access to the products they need.
In comparison to traditional human approaches, AI’s ability to grasp real-time inventory control dynamics, which affect inventory stock levels, makes it revolutionary. AI can anticipate scenarios, suggest solutions, and even carry them out for you.
Predictive maintenance is another promising application of AI in manufacturing. By analyzing data from sensors and other sources, AI helps predict when machines are likely to break down.
This allows manufacturers to carry out preventive maintenance before problems occur, resulting in reduced downtime and increased productivity amongst other things. According to a study by PwC, predictive maintenance improves uptime by 9%, cost reduction by 12%, reduction of health, safety and environmental risks by 14% and extends duration of aging assets by 20%.
A 2020 MIT suvey divulged that around six out of ten manufacturers and pharma companies are using AI to improve product quality. This is due to AI making a big impact on quality control efficiency.
By using machine learning algorithms, manufacturers are able to constantly monitor product quality and identify issues early on, tracking root-causes in real-time. This helps to avoid potential costly recalls and improves customer satisfaction.
An AI system paired with predictive analytics models allow companies to be more prepared for the unexpected.
This combination helps pinpoint potential risks and opportunities in the market, as well as provide insight into future trends. This could be the future shortage of some raw materials due to a natural disaster or political event up to a new sought-after product.
By using this information, manufacturers can make changes to their production process to avoid potential problems and take advantage of new opportunities.
Companies are also turning to AI to optimize their delivery routes and transport costs.
By analyzing data on traffic patterns, weather conditions, fuel cost and customer locations, AI can help identify the most efficient route for each delivery. This not only saves time and money, but also reduces emissions from vehicles and ensures on time deliveries.
In addition, AI is able to track and analyze past traffic congestion, roadblocks or any other unforeseen road delay. This way, a route optimization software combined with AI can gather insights and provide solutions when these events occur.
Aside from the capacity to do regular jobs, robots combined with AI now adapt to changes in the input provided by people and the environment.
For example, a robotic arm in a factory may be designed to weld two parts together. But if the welding torch is moved to a different position by the operator, the robot will learn the new position and adjust its own movements accordingly. This type of adaptation is possible thanks to the robot’s ability to learn from experience. By constantly adjusting its algorithms, the robot becomes more efficient over time.
In addition, AI-powered robots may carry out tasks that are too dangerous or difficult for humans. For example, robots can be used to operate in hazardous environments such as nuclear power plants or chemical factories.
By considering a set of constraints, such as weight, strength, and cost, generative design AI driven software produces thousands of potential solutions. The designer then chooses the best option or uses it as a starting point for further refinement.
In generating multiple design options for one product, manufacturers can also test different designs before committing to expensive production runs. This technique is often used in product design, but it can also be used to design manufacturing processes. For instance, generative design could be used to create a new layout for a factory floor that is more efficient than the existing one.
One of the most exciting applications of AI in manufacturing is its potential to create entirely new products. By analyzing data and trends, AI identifies opportunities for innovation that humans may not be able to anticipate.
AI is also being used to develop new products by customizing them for specific markets. By understanding customer preferences, AI can create products that are much more likely to be successful. This is often done through personalization, which is something that AI excels at.
According to a study by Capgemini, Kellogg’s has introduced an AI technology that assists consumers in deciding which recipe to use to create their desired product.
The use of augmented reality (AR) combined with Artificial Intelligence capabilities is growing in the manufacturing industry as it provides several benefits.
One of them is that AR can be used to display information about a product or process, which helps workers to understand it better. Another usage would be to provide instructions on how to carry out a task, effectively reducing the need for training.
Finally, AR may be employed to improve safety in the workplace by displaying warning signs or identifying hazards.
