By now we are all more or less familiar with artificial intelligence (AI), the ability of a machine to exhibit human capabilities such as reasoning, learning, planning and creativity. These characteristics, once attributed exclusively to humans, are now driving technologies which are fundamentally changing many industries, out of which manufacturing notably stands out.
In particular, the adoption of artificial intelligence has launched a new era, where innovation, efficiency and sustainability are the pillars driving the transformation of both factories and manufacturing processes.
Why is artificial intelligence so important in the manufacturing sector?
In this particular sector, the adoption of artificial intelligence has become vital. In an increasingly competitive economic and industrial environment, AI is enabling companies to optimise every aspect of manufacturing, including efficiency, quality, cost reduction and improving day-to-day operations.
Over the past two decades, the development and application of artificial intelligence in manufacturing has been a true “golden era.” The combination of ICT (information and communication technology) and AI has created the foundation for unprecedented innovation in the industry, enabling companies to remain competitive even in an ever-changing global environment.
“All possible futures for modern manufacturing in the United States involve AI. This technology is a game changer and will continue to prove an essential partner in the manufacturing process,” says Kathryn Wengel Executive Vice President and Chief Technical Operations & Risk Officer at Johnson & Johnson and Chair of the Board of the National Association of Manufacturers. With AI, manufacturers not only optimise processes but can make a major qualitative leap, improving productivity and significantly reducing costs.
Practical applications of Artificial intelligence in the manufacturing sector
The concrete applications of AI in the manufacturing sector are many, ranging from predictive maintenance to supply chain optimisation, process automation and new product development.
With its ability to analyse huge amounts of data in real-time, AI enables companies to make more informed decisions, reduce costs and improve sustainability. In addition, the integration of AI technology with other innovations, such as computer vision and digital twins, is opening new frontiers in smart manufacturing.
Here are some of the main applications of AI in manufacturing.
Predictive maintenance and quality control
Artificial intelligence can predict when a machine will need maintenance, minimising downtime. By analysing data collected through sensors and cameras, it can continuously monitor machines and detect anomalies before they become problematic.
In this way, AI helps companies plan maintenance work, optimising costs and extending equipment life. In addition, the introduction of computer vision into quality processes has transformed the inspection of defects in materials by detecting imperfections that are difficult for the human eye to detect. This has greatly improved product quality and sped up inspection processes.
Predictive maintenance technologies, combined with the use of digital twins, or virtual replicas of physical objects or systems that simulate their behaviour in real-time, allow machine behaviours to be simulated in real-time, optimising intervention time and preventing failures before they happen.
Supply chain optimisation
Machine learning models analyse vast amounts of data from the supply chain, improving demand forecasting, inventory management and reducing disruption risks. AI can also optimise logistics, reduce warehouse downtime and improve resource management, with the goal of smoother-running and lower-cost production.
At the same time, the use of predictive AI solutions in the supply chain supports increasingly efficient management of outages.
Using AI to analyse global patterns and market trends enables companies to quickly adapt to unexpected situations, minimising downtime and improving the resilience of the entire process. AI, integrated with digital twins, allows the entire supply chain to be simulated and optimised, improving demand forecasting and resource management in real-time.
Production process optimisation
Analysis of real-time data collected by sensors in factories enables the identification of bottlenecks and inefficiencies in manufacturing processes. AI suggests solutions to improve equipment layout and resource allocation, reducing waste and energy costs, which also has a positive impact on environmental sustainability. For example, AI can predict what changes in production parameters can optimise resource use, improving product quality while reducing energy consumption.
In addition, Factory in a Box technology is emerging as an innovative solution, offering modular, autonomous production units that can be quickly adapted to local production needs through AI-based automation.
Automating repetitive tasks
Automation of repetitive tasks through AI enables workers to focus on more strategic and high-value-added activities. Advanced robotics, combined with AI, can perform manual and administrative tasks with greater speed and accuracy, increasing productivity and reducing human error.
Cobots, collaborative robots, are designed to work side by side with humans, improving efficiency and safety, and are already used in various industries, such as electronics, to assemble components with great precision.
Product development and customisation
AI can quickly analyse different design applications and predict product behaviour under different conditions, reducing development time and the risk of errors in development. In addition, companies are using AI models to customise products to specific customer needs, speeding up the design of new solutions and reducing the costs associated with customisation.
With generative design tools, a wide range of design solutions can be explored, optimising parts and materials based on manufacturing parameters and costs.
AI and machine vision for greater efficiency and security
Artificial intelligence, combined with computer vision, has revolutionized manufacturing processes in several industries. Machines can now see their surroundings and analyse visual data to draw conclusions. For example, a logistics company can use computer vision to sort packages and make autonomous decisions based on constantly changing situations.
In industry, AI and machine vision are used to improve quality by quickly examining materials and parts for defects that are difficult for the human eye to detect. This application has made processes faster and more reliable, improving product quality and overall safety. Cobots and automated safety solutions help reduce the risks associated with hazardous tasks and improve workplace safety.
Work safety
Artificial intelligence is also being used to improve safety in the workplace. An automotive manufacturer, for example, can leverage AI and machine vision to monitor intersections in production lines, alerting workers to the presence of vehicles or forklifts that may be out of their line of sight.
In addition, AI technologies aimed at ergonomic assistance can use AI-powered robotic exoskeletons to monitor workers' movements and reduce physical stress, preventing injuries and improving safety and working conditions. By automating repetitive and hazardous tasks, workers can focus on more complex and strategic tasks, increasing overall safety and efficiency.
Workforce development and training
Artificial intelligence is also fostering employee upskilling through specific training programs to equip workers with the digital skills needed to interact with AI-based systems.
Indeed, the adoption of AI makes it possible both to reduce repetitive tasks and physical activities, improving work efficiency and safety; and to optimise staff planning and identify future skills needed to meet new challenges. Technologies such as generative product search can improve the interaction between operators and systems by facilitating the retrieval of information about components and parts in an intuitive way.
In sum, artificial intelligence is transforming manufacturing in increasingly sophisticated and interconnected ways, improving efficiency, safety, quality, and sustainability, while at the same time reducing costs and optimising resource management.
Benefits of artificial intelligence in the manufacturing sector
In short, the adoption of artificial intelligence in industrial production brings with it several key benefits:
- Improved product design
- Better decision
- Smart manufacturing
- Cost reduction
- Environmental sustainability
- Innovation and competitive
Challenges and concerns in implementing AI
Despite the undeniable advantages, many companies are still hesitant about adopting AI, mainly due to the size of the initial infrastructure investment and the difficulties in acquiring the necessary skills and training of the personnel involved.
Other major barriers to entry regarding AI adoption are concerns about data privacy and security, the management of legacy systems, and the quality and availability of structured, specific, quality data for AI to process and analyse.
The absence of source data with which to gain reliable insights is typical of several business areas today, such as quality control, where the availability of incomplete defect data can compromise the accuracy of processed models.
Internationally, privacy and data protection concerns are also at the centre of discussions in many companies. For instance, the United States is working on federal laws to avoid fragmentation in privacy regulations, circumventing possible conflicts between state and international laws.
Moreover, the integration of AI, by necessarily increasing digital connectivity, creates more potential points for cyber-attacks and necessitates the adoption of advanced cybersecurity measures to protect sensitive systems.
Among the main obstacles are undoubtedly the necessary changes in existing skills, processes and infrastructure. The implementation of AI requires significant initial investment in highly specialised professionals, technology and infrastructure, coupled with the need for ongoing training and upskilling programmes for staff to ensure a smooth transition to advanced and innovative industrial production.
At the same time, policymakers are called upon to support policies that foster innovation in the field of AI, avoiding the imposition of regulations that could hinder the growth of the sector. Specific regulation for each use case, differentiating regulations according to the risks arising from the implementation of AI, is therefore now really necessary. Companies, which already have internal governance structures in place, should be supported in reducing compliance burdens to create an environment conducive to the adoption of innovative technologies that improve productivity, security and operational efficiency.
A bright future for AI in manufacturing
In summary, artificial intelligence has the potential to revolutionise the manufacturing sector, leading to greater efficiency, higher product quality and more sustainable production.
With technologies such as predictive maintenance, process automation and supply chain optimisation, AI currently represents an unmissable opportunity for companies wishing to remain competitive.
The future of the manufacturing sector, if supported appropriately by institutions at a national and international level, is therefore increasingly linked to these innovations, and the early adoption of AI can undoubtedly be a determining factor in the success of companies in the near future.
