Artificial Intelligence is revolutionizing every industry on the planet; from healthcare applications, medicine, and geology to cybersecurity and autonomous vehicles to everyday use cases for individuals, AI is now the cornerstone of survival. With all the businesses adopting digital transformation in full swing, AI is expanding rapidly in manufacturing industries as well.
Simply put, AI in manufacturing industry is the simulation of human intelligence processes learned by highly advanced computer systems. Benefits include increasing productivity and decreasing expenses while enhancing quality and reducing downtime.
According to a report published by Accenture, businesses applying AI in factories could increase profit margins by an average of 38 percent by 2035. This increase could lead to an economic boost of up to US$14 trillion across 16 industries in 12 economies.
What is AI in Manufacturing?
AI in manufacturing is the use of Machine Learning (ML) solutions and Deep Learning (DL) neural networks to automate complex tasks and unveil previously unknown patterns to optimize manufacturing processes with improved data analysis and decision-making.
Every company is doing data-based transactions and ultimately becoming a data company. With so much data being produced by industrial companies from industrial IoT devices, smart sensors, and people, AI applications in manufacturing are empowering unprecedented levels of efficiency, customization and productivity. Embedding AI for manufacturing can be classified under the term 4IR, which stands for Fourth Industrial Revolution (Industry 4.0).
According to McKinsey, a top management consulting company, the 4IR technologies are expected to bring up to US$ 3.7 trillion in value by 2025. AI alone has the potential to generate $1.2 - $2 trillion in value for manufacturing and supply chain management.
How is AI Used in the Manufacturing Industry?
AI for manufacturing refers to using the ample data collected by multiple sources and coupling it with machine learning and deep learning algorithms. This synergy helps automate tasks and make manufacturing processes faster, more effective and more precise.
Technically speaking, the steps include collecting the pre-processing manufacturing data, developing AI models and testing them before using them in production. These algorithms are then plugged into various industry applications to improve efficiency, productivity and accuracy across multiple processes.
The AI4M (Artificial Intelligence for Manufacturing) is driving a paradigm shift by unleashing the industry towards unprecedented innovations and efficiencies. Following are the top use cases of AI in manufacturing:
| Use Case | Description |
|---|---|
| Supply Chain Mgmt. | AI is enabling predictive analytics, optimizing inventory management, enhancing demand forecast and streamlining logistics. |
| Co-bots | Co-bots are collaborative bots that work in AI-driven manufacturing with human operators doing intricate tasks. |
| Warehouse Mgmt. | Large-scale warehouses are using AI-powered solutions to improve efficiency and accuracy, leading to cost savings. |
| Predictive Maintenance | AI-based predictive maintenance (PdM) anticipates servicing needs while improving safety and lowering costs automatically. |
| New Product Development | AI brings innovative approaches that revolutionize the way companies create new products, reducing the time to land in the market. |
| Quality Assurance | Manufacturers can employ computer vision algorithms to analyze images or videos of the products with ultra-realistic precision to find any defects, anomalies and deviations from quality standards. |
| Lights-Out Factories | Lights-out factories are fully automated factories that operate entirely on a robotic workforce without the on-site presence or involvement of the human workforce. |
The Role of Generative AI in Manufacturing
In the context of manufacturing, generative AI can aid in improving production processes by creating new solutions and designs on top of existing ones. Gen-AI systems exhibit the capability of analyzing vast amounts of data accumulated over time, recognizing patterns amongst data, and receiving user feedback to enhance the target system’s performance massively. This iterative process of human feedback allows for continuous improvement rather than traditional manual system-fed updates. This makes generative AI an advantageous model as an ultimate working assistant, adept at interpreting extensive inputs. As a general rule of thumb, the quality of output is directly proportional to the data and feedback provided by the system user.
Manufacturing Industry Data
Industrial data collected from every touch point acts as a nervous system of any intelligent factory. AI heavily relies on vast amounts of data and input in order to analyze and translate the information into actionable insights as output. Hence, data plays a pivotal role in digitally transforming a traditional factory into an AI-driven industry. Below are some industry data collection points:
- Real-Time Monitoring Sensors: With the widely available industrial IoT sensors and sophisticated analytics, manufacturers can monitor every aspect of the production in ultra-real time, adjusting immediately as needed.
- Supply Chain Data: Gathering data from supplier performance, inventory levels, sales and demand forecasts enables responsive and flexible supply chain management.
- Environmental Data: Data accumulated from energy consumption, waste management, and greenhouse gas emissions allows manufacturers to fine-tune their industries for more eco-friendliness and sustainability.
Facts and Statistics on AI4M
Following are some astonishing facts and figures about AI in factories:
- A report from MarketsAndMarkets share staggering numbers and growth rates for AI and manufacturing. The report mentions that AI4M is valued at US$ 2.3 billion in 2023, and it is poised to grow to US$20.8 billion by 2028 at a CAGR rate of 45.6%.
- A survey conducted by rootstock (a renowned manufacturing ERP company) shares the state of AI for manufacturing revealing that over 70% of industries already have tapped into some form of AI in their operations. The top areas are production, employee training and customer service.
- PwC shares insights about the use of AI in manufacturing industry. Their report states that AI-enabled predictive maintenance can substantially reduce the maintenance cost by up to 30% and unplanned downtime by 45%. It also supports the fact that 94% of organizations believe that AI will help create more opportunities rather than be a threat to their industry.
- A report published by a well-known firm, McKinsey, states that AI factories are well on track to automate tasks that absorb 60% to 70% of employee’s time today. Generative AI’s impact on productivity is expected to add trillions of dollars in value to the global economy.
- The World Economic Forum shared a white paper about unlocking value from Artificial Intelligence in manufacturing industry. The paper states that AI implementation in manufacturing processes can lead to cost reductions of up to 30%.
Future of AI in Manufacturing
The future of AI4M is promising and expected to drive significant growth and increased efficiency in all industrial sectors. The data from various research organizations and facts depict that it is the right time for businesses to transform themselves digitally with AI-driven solutions. AI systems can operate autonomously and intelligently manage manufacturing processes in response to external events without human intervention. These systems will enable more precise manufacturing process designs, problem diagnoses and applying resolution all on its own.
Integrating AI is more of a must-have rather than a nice-to-have option because the manufacturing industry is at the cornerstone of digital transformation. The benefits of employing AI are far greater than ignoring it.
Following are key numbers that highlight the future of AI in the manufacturing industry:
- 15 - 30% increase in labor productivity
- 30 - 50% decrease in machine downtime
- 10 - 30% yield in throughput
- 10 - 20% decline in quality-related costs
AI in Manufacturing Examples
BMW Group to Leverage NVIDIA’s Omniverse for Virtual AI Factory (Example from future)
NVIDIA is breaking the ground by opening the world's first virtual factory in NVIDIA Omniverse for BMW Group. The NVIDIA Omniverse platform will enable BMW to optimize the layouts, robotics and logistics of its planned EV plant in Debrecen, Hungary. All of this will happen before the real production even begins. This first-of-its-kind digital approach will allow BMW to pre-optimize the factory design virtually and leverage AI to eliminate costly change orders and reduce production downtime.
The alliance between NVIDIA and BMW Group showcases the power and agility of taking an AI-driven industrial manufacturing plant to an entirely new level.
General Electric (GE) uses AI Software for Sustainable Manufacturing Industry
GE unveiled a new software called GE Vernova Proficy that gathers sustainability insights to help industrial facilities meet their sustainability goals while simultaneously reducing costs. The AI software integrates operational and sustainability data to help optimize resource usage and manage climate metrics for regulatory compliance. An automotive manufacturer in Europe used Proficy to achieve 18% energy savings on factory heating systems.
Amazon employs AI-Powered Robots to Improve Delivery Timeframe and Reduce Injuries
Amazon’s new AI-powered robotics system is revolutionizing the manufacturing sector by significantly improving safety, efficiency and overall operations in its warehouses. The technology has the potential to increase product finding and storing speeds by up to 75% and order fulfillment by 25%, using robotic arms and advanced computer vision to identify inventory and streamline the delivery process. Additionally, this robotic AI-powered arm aims to eliminate mundane tasks and significantly reduce workplace injuries by having humans and robots collaborate for optimal results.
Intel’s Future Growth with AI in Manufacturing
Intel’s AI-based approach to yield analysis in semiconductor manufacturing represents a major shift toward the Industry 4.0 paradigm. This AI-based solution demonstrates the potential of AI by transforming manufacturing processes, offering increased scalability and higher efficiency. By automating the detection of gross failure areas (GFAs) and applying proactive issue resolution, AI is allowing 100% wafer analysis, multi-issue detection and sharing of knowledge across global manufacturing sites rapidly. This approach is helping Intel not only improve production yield and reduce time to market but also frees engineers to apply their time to complex projects and problem-solving.
BMW uses AI and Automation in Production Lines
BMW is a sleek fast automobile company, and now they are leveraging AI manufacturing to perform monotonous tasks in production lines to further enhance quality control, logistics and virtual layout planning. AI is aiding in automated image recognition, which detects production deviation in real-time by comparing them to a database of hundreds of other images and applying corrections before they become a significant problem. Thanks to this automation, the employees are relieved from doing repetitive tasks. The AI technology used at BMW’s plant can perform more demanding tasks and control robot systems to speed up logistics. The adoption of AI in production management is leading to more efficient and cost-saving processes, ultimately improving the overall quality of the final product.
Airbus is Tapping AI and Computer Vision for Automating Aircraft Inspection
In the highly intricate and labor-intensive process of the airplane manufacturing industry, Airbus is leveraging AI to revolutionize quality control methodologies. By constantly analyzing the video feeds, AI-powered solutions can precisely and automatically log major assembly steps, excluding the possibility of human error at full. This solution, developed in collaboration with Accenture Labs, underscores the use of AI-aided computer vision to detect manufacturing issues in the aircraft’s final assembly. It can also recognize when tasks have been completed through motion, annotating images, and video feeds to inspect the proper installation of large airplane parts. This automated process used by Airbus not only increases accuracy and efficiency but also generates enough free time for technicians to diverge their focus on more meaningful tasks, resulting in overall savings and improved quality without sacrificing safety standards.
Toyota is Embracing AI to Breakthrough in Automotive Designs
Toyota, a famous automaker from Asia, is unleashing the power of AI in its design processes. They are equipping their engineers with generative AI, VR and AR technologies to explore new design possibilities, improve performance metrics and enhance the safety of their vehicles. These technologies promote creativity among designers by incorporating engineering constraints into Gen-AI models, cutting down the iterations needed to reconcile design.
The Impact of AI in Manufacturing on Employment
The never-ending debate of AI taking human jobs is still far from reality. Some job areas have indeed been mechanized by the introduction of AI. But at the same time, AI is creating new jobs and proving to be the ultimate human assistant by boosting productivity and efficiency. The skill gap is a real issue in AI adoption, and companies need to re-train and enhance the intellectual level of their employees to better understand data and leverage AI capabilities by means of generative and prompt engineering. The best answer to end this debate is by promoting a sustainable and flexible future where humans, machines and AI will coexist for the greater good of the economy and propelling organizational growth.
Challenges of AI in Manufacturing
The rapid emergence of AI and the race to adopt AI in manufacturing is not free of challenges and obstacles. Manufacturers are exerting their maximum energies to diversify production by tapping the AI potential. However, most industries have to overcome barriers that are impeding digital transformation ambitions.
Some of the common challenges that these manufacturers face are as follows:
- Talent Gap: The lack of experienced data scientists and AI engineers is a real problem. Implementing AI factories requires a diverse team of experts, including data scientists, AI & ML engineers, software architects and IT analysts. The manufacturing industry is facing severe workforce deficiency as baby boomers are retiring and young engineers are not intrigued by the sector due to its perceived monotony and mundane nature.
- Interoperability b/w Legacy and New Technology: Industries have a wide variety of machines, tools, and production systems that use different sets of technologies and software to operate. Integrating AI into existing legacy systems is not easy in the absence of standardized protocols.
- Expensive Investment: The high initial cost of acquiring custom AI-based software, according to the nature and size of the industry, can get pricey. This mainly incurs massive upfront investment in technology, training of employees, and infrastructure. Allocating a significant budget for this investment and getting approval from all stakeholders is not always guaranteed.
- Data Quality: Access to clean and meaningful data is highly crucial for the success factor of AI systems. This can be challenging due to poor, outdated and error-prone data caused by multiple factors. Furthermore, data security, privacy and storing methodologies also need to be revised and standardized.
- Cybersecurity Concerns: Since industries are opting for digital transformation, they are more susceptible to cyber-attacks, data breaches and manipulations. It is of paramount importance to protect the integrity and confidentiality of production data, protect intellectual property and ensure the accuracy of AI algorithms and models.
Managing the Cybersecurity Risks of AI in Manufacturing
Employing AI-cybersecurity best practices in smart factories remains crucial for an unseeable future as this migration is occurring for all sizes of industries. Generative AI, on the one side, massively increases productivity and is an added valuable asset for reforming industries. In contrast, cyber attackers can manipulate AI systems to infer incorrect predictions and deny services to customers.
According to Statista, the share of cyber-attacks across worldwide industries accounts for 25.7% of the attacks in manufacturing industries. This is a stark reminder for the industrial sector to implement robust and foolproof cybersecurity while making a transition towards AI. One of the primary reasons for these attacks is that the AI model is usually hosted in the cloud outside the industrial premises, and an external network connection is needed to reach and conversate with the AI model. This reliability on the external network creates a potential vulnerability for attackers to launch their attacks since this is not usually secure.
According to Dragos research, 83% of manufacturing clients have undocumented and uncontrolled external connections to their operation technology. This vulnerability is rather a favourite option for ransomware groups to exploit the weakness and attack industries.
Some safeguarding tips for minimizing cybersecurity risk in AI manufacturing are:
- Encrypt all egressing data from industrial premises
- Restrict AI access and implement zero-trust frameworks
- Monitor AI data constantly to detect anomalies and suspicious activities
- Manufacturers should do pen-testing at least once every year in all digitized areas
- Use AI with caution; relying on it too much can be detrimental. Humans are irreplaceable.
Implementing AI-powered cybersecurity solutions to safeguard manufacturing and smart factories is a must-have.
Ways to unleash the power of AI in manufacturing
It is no surprise that manufacturers are clearly recognizing the pivotal role of AI in industry. Their journey in adopting AI4M is bolstered by creating advanced, highly efficient, smart and AI-connected factories.
Below are some ways of unleashing the power of AI and pushing it to limits to leverage it fully:
- Empowering Employees: As plain as it sounds, empowering employees by re-training them to leverage AI and understand how to interpret data is essential to boost productivity and the path forward to digitally transformed factories.
- Safe and Efficient Operations: The implementation of cobots to work alongside humans to perform various tasks such as assembling parts, operating machinery and aiding in quality inspection is a way forward to achieving overall productivity and efficacy.
- Design and Innovation: Generative AI can transform product conceptualization by analyzing market trends and customer’s needs and demands. This can help manufacturers optimize product attributes and lower development costs while increasing product performance and better compliance. Next-level AI simulation applications can help manufacturers develop, test and refine product design without the need to build actual physical prototypes.
- Proactive Predictive Maintenance: Integrating AI to analyze data gathered from various sources such as machinery, Industrial IoT devices, and smart sensors allows manufacturers to identify irregularities, predict breakdowns, and prevent equipment failure by preemptively informing system users and applying fixes.
Future Development of AI in Manufacturing
The future of AI in the manufacturing industry holds great promise with the growth of various technologies. Machine learning, with supervised and unsupervised learning, will continue to play a fundamental role in optimizing factories. Deep learning is increasingly becoming important in process industries where it will assist manufacturers in analyzing complex data sets and improve overall manufacturing processes. Natural Language Processing (NLP) is being used to process human instructions so machines can understand for better process automation and inventory management. AI-based machine vision using smart cameras will help monitor manufacturing environments, increase worker safety and reduce injuries. Additionally, AI-enabled leak detection is being widely used to identify hazardous gas and chemical leakage in real-time to foster human and environmental protection. As these technologies evolve with time, they will drive greater reliability, efficiency and innovation in the manufacturing industry.
People Also Ask
Generative AI accelerates the design and development of the product using feedback from designers and engineers. This process facilitates innovation and reduces time to market.
AI helps industries optimize energy consumption, improve waste management and reduce the emission of greenhouse gases.
Yes, AI is significantly improving worker safety in harsh manufacturing conditions by employing computer vision, intelligent cameras and cobots to reduce injuries.
AI-enabled factories rely on a constant stream of data generated by machinery and sensors. By interpreting this data in real-time, AI systems can identify potential machinery failures earlier, thus reducing downtime and driving efficiency by keeping the industry operational.
AI-driven computer vision massively helps in quality control methods by analyzing the hundreds of images stored in the database and identifying the issue almost instantly. This is also known as digital twin technology.
AI predicts inventory forecasts by analyzing the supply and demand and market trends, preventing overstock and shortages.
