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Artificial Intelligence and manufacturing

How AI is being used in manufacturing: Examples, use cases, and benefits


This article delves into how artificial intelligence is transforming manufacturing. We’ll explore the top applications of AI and showcase real-life examples from industry leaders, giving you a glimpse into the future of smarter, more efficient production. By now, you’re probably 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 on our everyday lives.


But beyond these everyday innovations lies a revolution that is 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 of global manufacturers, 89% of companies plan to implement AI in their production networks soon, and 68% have already started implementing AI solutions. However, only 16% achieved their goals, mainly due to a lack of digital skills and scaling capabilities.  These statistics show that the industry recognizes 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 successful large-scale AI integrations remains a challenge.

In this article, we will explain these statistics by diving deeper into how AI is used in manufacturing, providing insights into the main applications of AI, such as predictive maintenance and quality control. We will also highlight some use cases by leading manufacturing companies around the world, and finally, we will go over the benefits that AI brings to manufacturing and its limitations, mainly associated with implementation.

What is AI?

Let's start with the basics: what is artificial intelligence (AI)? If you're not familiar 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 abilities. AI assists in tasks that enhance human intelligence, such as decision-making or problem-solving. However, AI can also completely take over tasks that require substantial human intervention, such as driving self-driving cars.


AI systems use algorithms and large data sets to mimic human intelligence. They collect and process data, identify patterns, and use models such as neural networks to complete tasks. AI constantly learns and adapts to improve its performance over time.


However, it's important to note that the term artificial intelligence encompasses many technologies that power AI, such as 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 we often see in AI solutions for manufacturing include:

Machine LearningDeep LearningNatural Language Processing (NLP)Computer VisionRobotics

Machine Learning

This is a subset of AI that gives machines the ability to learn from data. It automates the creation of analytical models, allowing systems to learn from data, identify patterns, and make decisions.

Deep Learning

Deep learning is a subset of machine learning. It sets basic parameters on data and trains the computer to learn independently by recognizing patterns using many layers of processing. It is 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 conversion.

Computer Vision

Computer vision enables machines to interpret and understand visual information from images or videos, for tasks such as image classification or facial recognition.

Robotics

Robotics combines artificial intelligence with mechanical engineering to create machines (robots) capable of performing 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 know the underlying technologies, let's look at how AI solutions are implemented and how they improve efficiency and innovation in the manufacturing sector.

How is AI used in manufacturing?

AI is transforming factories into more efficient, profitable, and innovative environments. The top five applications of AI in manufacturing are:

  1. Predictive maintenance
  2. AI-enhanced quality control
  3. Supply chain optimization
  4. Intelligent automation
  5. AI-based training and support

Predictive maintenance is undoubtedly one of the most game-changing AI use cases. It's no surprise, considering that AI-based predictive maintenance can significantly improve the manufacturing process. By analyzing data collected from sensors, equipment telemetry, and other sources, machine learning algorithms can predict when equipment failures are likely to occur. This AI solution enables manufacturers to proactively schedule maintenance, 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 equipment sensor data, AI for manufacturing can analyze patterns, identify anomalies, and predict potential failures. This information provides maintenance teams with predictive insights to proactively schedule maintenance interventions before equipment failures occur.


Generative AI

Generative AI is actually a subset of deep learning and learns from existing data sets to generate new content such as text, images, and code. Generative AI can generate synthetic data that simulates possible failure scenarios. This synthetic data can be used to train predictive maintenance models.

A mistake during production jeopardizes the quality and safety of the final product. Machine 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 data sets to recognize patterns associated with defects, allowing defects to be classified and sorted in an automated way.

Imagine a crystal ball that predicts equipment failures and forecasts consumption trends, delivery times, or transportation delays. This is how AI transforms supply chain management: by predicting fluctuations in demand, optimizing inventory, and identifying potential disruptions.


Demand forecasting

AI is increasingly being applied in demand forecasting to improve accuracy and reliability. AI algorithms can identify patterns and trends that you might otherwise miss by analyzing large volumes of data, including sales data, customer behavior, economic indicators, and external factors (e.g., weather patterns).


Inventory management

AI systems enable manufacturers to maintain optimal inventory levels by taking into account multiple factors such as lead time, carrying costs, ordering costs, and service level requirements. By tracking stock levels, order status, and expected delivery times in real time, manufacturers can balance on-hand inventory and improve inventory visibility across the supply chain. This enables manufacturers to anticipate changes in demand with greater accuracy, optimize inventory levels, and make informed decisions about production, sourcing, and resource allocation.

Intelligent automation is the combination of intelligent software and robotic equipment. Take advantage of the benefits of AI to automate tasks that go beyond repetition by combining AI, industrial robots, or robotic process automation.


Automated Robots

Industrial robots have long been a staple in the manufacturing industry. However, the integration of AI into automated robots represents a significant advancement in manufacturing technology. Unlike traditional industrial robots programmed with fixed instructions, AI 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 exhibit significantly lower error rates, a feature that allows manufacturers to expand 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 cannot be fully automated (and performed by an automated robot). This collaborative approach to automation improves the efficiency, flexibility, and ergonomics of 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 using computers. It uses software robots to mimic human actions such as data entry, copying files, and filling out forms. Invoices, orders, reports, checklists—paperwork is present in every aspect of manufacturing. If digitizing paperwork is the first step toward efficiency, incorporating AI-based RPA is the ultimate goal. RPA is an assistant that takes care of repetitive paperwork tasks. Thanks to AI, it can provide analytical and decision-making capabilities for an optimal automation strategy.

In the industrial sector, clear and precise work instructions are the backbone of efficient production processes. Traditionally, these instructions were compiled manually, which was a time-consuming and error-prone process. In recent years, digital work instructions have revolutionised operational efficiency and productivity in factories. However, adding a layer of AI-powered digital tools could change the way work instructions are created. For example, with speech-to-text capabilities, factory workers can dictate instructions and automatically convert them into structured, written steps. Another application is automatic video segmentation, where instructions recorded in video format are analysed and broken down into discrete, easy-to-follow steps. This is made possible by advanced speech recognition and AI-based content analysis.