By now, you’re surely familiar with self-driving cars, generative AI assistants like ChatGPT, or interactive chatbots. These are just a few tangible examples of AI that have already made their mark in our daily lives.
But beyond these everyday innovations lies a revolution, one that’s reshaping the manufacturing landscape. Artificial intelligence in manufacturing is here, and it’s here to stay.
AI in The Manufacturing Industry
According to a survey conducted among international manufacturers, 89% of companies plan to implement AI in their production networks soon, and 68% have already started implementing AI solutions. However, only 16% reached their goals, mainly due to a lack of digital skills and scaling capabilities.
These statistics show that the industry acknowledges the importance and benefits of artificial intelligence for manufacturing, and companies are already making an effort to adopt AI in their operations. However, the gap between pilot projects and fully scaled, successful AI integrations remains challenging.
In this article, we’ll explain these statistics by delving deeper into how AI is used in manufacturing, providing insight into the primary AI applications, such as predictive maintenance and quality control. We’ll also highlight some use cases by leading worldwide manufacturing companies, and lastly, go over the benefits AI brings to manufacturing and its limitations, primarily associated with the implementation.
What Is AI?
Let’s start with the basics: what is artificial intelligence (AI)? If you’re unfamiliar with the concept and its underlying technology, we’ll get you up to speed.
Artificial intelligence is the technology that enables computers and machines to simulate human intelligence and problem-solving capabilities. AI assists with tasks that enhance human intelligence, such as decision-making or problem-solving. Still, AI can also completely take over tasks that require substantial human intervention, such as driving autonomous cars.
AI systems work by using algorithms and large datasets to mimic human intelligence. They collect and process data, identify patterns, and use models like neural networks to complete tasks. AI is constantly learning and adapting to improve its performance over time.
However, it’s important to note that the term artificial intelligence covers many technologies that power AI, including machine learning, deep learning, and natural language processing.
Types of AI Technologies in Manufacturing
Artificial intelligence is often used as an umbrella term for tools and techniques that enable machines to simulate human-like intelligence and behavior. Some of the key technologies that we see often in AI solutions for manufacturing include:
Machine Learning
This is a subset of AI that gives machines the ability to learn from data. It automates analytical model building by enabling systems to learn from data, identify patterns, and make decisions.
Deep Learning
Deep learning is a subset of machine learning. It sets up basic parameters about the data and trains the computer to learn independently by recognizing patterns using many processing layers. It’s useful for image and speech recognition.
Natural Language Processing (NLP)
NLP enables machines to understand, interpret, and generate human language. It is used for language translation, sentiment analysis, and speech-to-text.
Computer Vision
Computer vision allows machines to interpret and understand visual information from images or videos, for tasks such as image classification or facial recognition.
Robotics
Robotics combine AI with mechanical engineering to create machines (robots) that can perform tasks autonomously or with minimal human intervention. This includes industrial robots used in manufacturing, as well as social robots designed for human interaction.
Now that you’re familiar with the underlying technologies, let’s look at how AI solutions are implemented and how they enhance efficiency and innovation in the manufacturing sector.
How Is AI Used in Manufacturing?
AI is transforming factories into more efficient, cost-effective, and innovative environments. The five main AI applications in manufacturing include:
- Predictive Maintenance
- AI-Enhanced Quality Control
- Supply Chain optimization
- Intelligent Automation
- AI-driven Training And Assistance
1. Predictive Maintenance
Predictive maintenance is undoubtedly one of AI’s most trending and game-changing use cases. It’s no wonder, considering AI-based predictive maintenance can significantly improve the manufacturing process.
By analyzing data collected from sensors, equipment telemetry, and other sources, the machine learning algorithms can forecast when equipment failures are likely to occur. This AI solution allows manufacturers to schedule maintenance proactively, minimizing downtime and reducing maintenance costs.
Digital Twins
A digital twin is a virtual replica of a physical asset that captures real-time data and simulates its behavior in a virtual environment. By connecting the digital twin with sensor data from the equipment, AI for the manufacturing industry can analyze patterns, identify anomalies, and predict potential failures.
This information gives maintenance teams predictive insights to schedule maintenance interventions proactively before equipment failure occurs.
Generative AI
Generative AI is actually a subset of deep learning and learns from existing data sets to generate new content, such as text, image, and code. Generative AI can generate synthetic data that simulates potential failure scenarios. This synthetic data can then be used to train predictive maintenance models.
2. Quality Control
A mistake during production jeopardizes the final product’s quality and safety. AI-powered computer vision systems can mitigate these risks by analyzing images or sensor data to detect defects or anomalies in products. Machine learning algorithms are trained on labeled datasets to recognize patterns associated with defects, allowing for automated defect classification and sorting.
3. Supply Chain Optimization
Imagine a crystal ball that predicts equipment failures and forecasts consumer trends, lead times, or transportation delays. That is how AI transforms supply chain management: by predicting demand fluctuations, optimizing inventory, and identifying potential disruptions.
Demand forecasting
AI is increasingly implemented in demand forecasting to improve accuracy and reliability. AI algorithms can identify patterns and trends that you may overlook by analyzing large volumes of data, including sales data, customer behavior, economic indicators, and external factors (e.g., weather patterns).
Managing inventory
AI systems enable manufacturers to maintain optimal inventory levels considering multiple factors like lead time, holding costs, ordering costs, and service level requirements. Thanks to real-time tracking of stock levels, order status, and anticipated delivery times, manufacturers can balance the stock inventory and enhance inventory visibility across the entire supply chain.
This enables manufacturers to anticipate changes in demand more accurately, optimize inventory levels, and make informed decisions about production, procurement, and resource allocation.
4. Intelligent Automation
Intelligent automation is the combination of intelligent software and robotic equipment. It uses AI’s advantages to automate tasks that go beyond repetition by combining AI, industrial robots, or robotic process automation.
Automated robots
Industrial robots have been a staple in the manufacturing industry for a while. However, integrating AI into automated robots represents a significant advancement in manufacturing technology. Unlike traditional industrial robots programmed with fixed instructions, AI-powered robots can learn from their environment, adapt to changing conditions, and make decisions autonomously.
AI robots, unlike human workers, can operate continuously without the need for breaks. They also demonstrate significantly lower error rates, a feature that allows manufacturers to scale their production capacity with confidence.
Collaborative robots (cobots)
Collaborative robots, also called cobots or co-robots, are robots that work alongside workers in a factory to complete a task that can’t be fully automated (and performed by an automated robot).
This collaborative approach to automation improves efficiency, flexibility, and ergonomics in manufacturing operations while allowing workers to focus on more complex tasks that require human intelligence.
Robotic Process Automation
Robotic Process Automation (RPA) automates repetitive, rule-based tasks that workers typically perform on computers. It uses software bots to mimic human actions like data entry, copying files, and filling out forms.
Invoices, orders, reports, checklists – paperwork is in every aspect of manufacturing. If digitizing paperwork is the first step towards efficiency, bringing in an AI-based RPA is the ultimate goal. RPA is an assistant that takes care of repetitive paperwork tasks. Using AI, it can bring decision-making and analytical capabilities to the table, for an optimal automation strategy.
5. Training And Assistance
In the industrial sector, clear and accurate work instructions are the backbone of efficient production processes. Traditionally, these instructions were compiled manually, which resulted in a time-consuming and error-prone process. In recent years, digital work instructions have revolutionized factories’ operational efficiency and productivity. However, adding a layer of AI-powered digital tools could change how work instructions are created.
For example, with speech-to-text capabilities, factory workers can now dictate instructions and automatically convert them into structured, written steps. Another application is automatic video segmentation, where instructions recorded in video format are analyzed and divided into discrete, easy-to-follow steps. This is made possible by advanced speech recognition and AI-driven content analysis.
AI in Manufacturing Examples
AI’s integration into production operations has transitioned from a futuristic vision to a practical reality, as the following companies using ai in manufacturing prove. For example, how leading car manufacturers are putting AI in the driver’s seat, Ford putting cobots next to assembly line workers, and BMW is customizing quality control with its AI platform.
BMW Group – Custom-Developed Quality Control
As the following example from BMW Group shows, AI is becoming increasingly prevalent in car manufacturing. The German manufacturer custom-developed its AI platform AIQX, which stands for Artificial Intelligence Quality Next.
The platform uses cameras, sensor technology, and AI to automate quality processes in the conveyor belt. Algorithms and AI analyze the data recorded by these in real-time and send immediate feedback to employees on the production line via smart devices.
Ford – Robots in The Assembly Line
Over a century ago, Henry Ford revolutionized the automotive industry with his groundbreaking assembly line. Since then, the car manufacturer has continued to innovate in vehicle production.
More recently, Ford integrated AI into their assembly lines by installing robot arms that grab metal converters and assemble them into place. While performing this task, the AI system learns the most efficient way to put the parts together.
Rolls Royce – Digital Twins for Predictive Maintenance
For optimal performance and maintenance of aviation engines, the aircraft manufacturer Rolls-Royce developed a digital twin platform to consolidate data from all produced engines.
Rolls-Royce can monitor engine performance, predict potential issues, and optimize maintenance schedules by collecting and analyzing historical and real-time data from these engines. This integration of digital twins and AI improves operational efficiency and enhances aviation safety and reliability.
General Electric – Data for Sustainability
Beginning in 2024, General Electric (GE) released the Proficy for Sustainability Insights, a new software solution that helps manufacturers reach their sustainability goals, while maximizing productivity and profitability. By integrating operational and sustainability data, AI-based software allows manufacturers to use resources more efficiently and effectively across a plant or entire operations, and keep track of climate metrics required for regulatory compliance.
Benefits of AI in Manufacturing
AI could be considered the mastermind behind manufacturing in Industry 4.0. It analyzes data to optimize processes, boosting efficiency, lowering costs, and enhancing quality with eagle-eyed AI quality control.
Cost Savings
AI reduces operational costs through optimized processes, decreased downtime, and efficient resource allocation.
Data-Driven Decision-Making
AI analyzes vast amounts of data to identify trends and patterns, providing valuable insights for optimizing production processes, improving product design, and making data-driven business decisions.
Supply Chain Optimization
AI optimizes supply chain logistics, inventory management, and procurement processes, improving efficiency, reducing costs, and enhancing overall supply chain resilience.
Improved Safety
AI improves workplace safety by combining automation, real-time monitoring, and predictive analytics. This multi-pronged approach shields workers and keeps the manufacturing environment safe.
Enhanced Product Quality
AI-powered vision systems can inspect products with far greater accuracy and speed than human inspectors, who are more prone to making errors (and overlooking them).
Increased Efficiency
AI can take over repetitive tasks, freeing up workers to concentrate on more complex processes. The systems optimize processes by analyzing data to identify bottlenecks, detect issues before they occur, ensure top-notch quality control, and suggest improvements. This speeds up production and minimizes errors and wasted materials, leading to a smoother, more productive manufacturing environment.
Limitations of AI in Manufacturing
Even though the benefits of AI in manufacturing are promising, the limitations are as important to consider. The challenges and concerns associated with AI are mostly related to the implementation process and workforce.
Data Infrastructure
AI thrives on data. However, traditional manufacturing may need more data infrastructure to collect, store, and analyze the vast data required for practical AI training. Data quality is also critical, and ensuring clean and accurate data sets can be challenging.
By providing a central hub for collecting and monitoring production data in real-time, it can break down data silos, ensure data quality, and streamline the flow of information.
Data Protection and Regulations
As AI systems rely heavily on data, including sensitive information related to manufacturing processes, product designs, and customer data, ensuring data privacy and security becomes paramount.
Besides securing data, manufacturing companies must comply with various data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in the United States. Failure to adhere to these regulations can result in fines and reputational damage.
Standardization
Scaling an AI solution might require standardizing processes or data formats to ensure the AI functions consistently. While it ensures clean data and simplifies AI integration, it can also limit AI’s ability to learn and adapt to unique situations.
The key is finding a balance: standardize data for smooth AI integration but maintain some flexibility within processes to allow AI to discover and suggest improvements, ultimately creating a smarter and more adaptable manufacturing environment.
Skill Gap
Implementing complex AI systems requires specialists in data science, AI engineering, and manufacturing. This, coupled with the varying levels of digital maturity among manufacturers, creates a challenge. Even if AI is implemented, a gap in understanding AI outputs can persist.
Workers with a data science background need help understanding how data science and predictive modeling work and need more confidence in the abstract algorithms behind AI technology.
Training existing employees, developing clear digital work instructions for AI tools, and partnering with AI experts. By addressing the skill gap and promoting digital fluency, manufacturers can unlock the true potential of AI and transform their operations. Developing clear, digital work instructions can also play a crucial role. These instructions should explain how to utilize AI-powered tools and effectively interpret their outputs.
AI for Manufacturing Companies in a Nutshell
Since the manufacturing industry is at the forefront of digital transformation, integrating AI becomes more a must-have than a nice-to-have. AI applications in manufacturing such as predictive maintenance, quality control, and intelligent automation boost efficiency, safety, and decision-making.
However, the road to fully integrated AI in manufacturing is not without challenges. Issues such as data infrastructure, standardization, and the digital skill gap should be addressed to realize AI’s full benefits. The future of manufacturing is undoubtedly one where AI has its place, and manufacturers who embrace its potential will lead the charge in innovation, efficiency, and competitiveness.
