Generative AI in Manufacturing: 5 Industry-Transforming Use Cases

Generative AI is a powerful tool that can unlock peak efficiency and propel your factory towards the future. Learn from some examples of generative AI in manufacturing.
A person operates a tablet within an industrial setting, with robotic arms and machinery in the background. The tablet screen displays a dashboard with various charts and graphs, illustrating data analytics and performance metrics driven by generative AI in manufacturing.
Published on:
07 June 2024
Updated on:
13 June 2024

In the last two years, generative AI has burst into the scene. Groundbreaking tools such as OpenAI’s ChatGPT, DALL-E, and Google’s Gemini have been placed at our fingertips. These technologies not only change our daily lives, but also revolutionize entire industries. This technology shift is particularly transformative for the manufacturing industry.

The factory of the future embraces artificial intelligence as a cornerstone of its operations. But within the vast toolbox of AI, one specific technology is emerging as a game-changer: generative AI. This article will delve into the most common use cases of generative AI in manufacturing. We’ll give some insightful examples of world-leading manufacturing companies that have innovated their operations with generative AI.

What is Generative AI?

Generative AI is a subset of artificial intelligence that can generate new content or data similar to what it has been trained on. This means that, based on previously generated content, it uses algorithms and models to create text, images, video, design, audio, code, and simulations. GenAI is an advanced copycat that learns patterns and structures from data and uses that knowledge to generate synthetic data (such as images, videos, text).

So, how does generative AI differ from traditional AI? The key distinction lies in their outputs. While generative AI is capable of creating entirely new data that closely resembles its training data, traditional AI focuses on data analysis to make predictions or identify patterns based on the input data it’s trained on.

Generative AI Use Case in Manufacturing

From creating innovative concepts to optimizing delivery, generative AI is revolutionizing every stage of the manufacturing journey.

In the conceptual phase, generative AI can generate concepts and optimized designs in product design and prototyping, accelerating product development while minimizing costs.

In the production phase, intelligent sensors and advanced data analysis revolutionize equipment maintenance. These systems analyze sensor readings, maintenance logs, and historical failures to predict potential breakdowns before they occur. Besides predictive maintenance, the systems also detect quality issues.

Generative AI doesn’t just automate tasks, it empowers the workforce. By leveraging individual employee data, it offers targeted training and personalized work instructions, paving the way for a more skilled and adaptable workforce.

Throughout the whole supply chain, advanced data analysis plays a game-changer role. By crunching massive datasets, businesses can predict demand fluctuations, optimize inventory levels, and plan the most efficient delivery routes.

A horizontal infographic with five blue circles connected by dashed lines. Each circle has an icon and is labeled sequentially: Product Design & Development, Predictive Maintenance, Quality Assurance, Training & Skill Development, and Supply Chain Management, illustrating the integration of generative AI in manufacturing.

Product Design

Generative AI is revolutionizing product design in manufacturing by bringing a unique blend of creativity and efficiency. More specifically, generative text-to-image tools are helping designers bridge the gap between concepts and production-ready designs.

The product designer’s or engineer’s job is to define specific design goals, considering metrics such as sustainability goals, production costs, product criteria or compliances, and manufacturing conditions. Generative AI systems generate various design options based on these predefined parameters.

Once these designs are created, AI systems propose improvements to optimize aspects such as recyclability, material choice, and packaging, ensuring the final product is both efficient and sustainable. With promising virtual prototypes in hand, engineers and designers can evaluate and refine the suggested designs, selecting the best ones for further development.

Benefits of generative AI in product design:

  • Shortening the design cycle
  • Easily transition from initial ideas to production-ready models
  • Boosting creativity

Toyota Research Institute’s Text-To-Image Integration

The Toyota Research Institute developed a platform integrating design sketches and engineering requirements into text-to-image-based generative AI tools. This allows car designers to combine Toyota’s traditional engineering strengths with the state-of-the-art capabilities of modern generative AI.

For example, constraints such as drag (which affects fuel efficiency) and chassis dimensions such as ride height and cabin size (which affect handling, ergonomics, and safety) can now be implicitly incorporated into the generative AI process.

Predictive Maintenance

Generative AI takes predictive maintenance to a new level. It learns the expected behavior of equipment by analyzing sensor data, maintenance logs, and historical failures. It then identifies subtle anomalies that might precede a breakdown. Even the slightest change doesn’t go undetected; imagine a tiny vibration increase in a turbine engine – the AI flags it as a potential issue.

Generative AI doesn’t just predict failures; it can even simulate how they might unfold. This allows for targeted maintenance before things come to an unforeseen – and costly – halt. Manufacturers can maximize equipment lifespan and avoid downtime.

Benefits of generative AI in predictive maintenance:

  • Prevent downtime
  • Simulate equipment breakdowns
  • Optimized maintenance schedules

Siemens’ Senseye Predictive Maintenance

In February 2024, Siemens released a new generative artificial intelligence (AI) functionality into its predictive maintenance solution – Senseye Predictive Maintenance. This new feature essentially builds upon the existing AI’s strengths and makes the whole process more conversational and user-friendly.

The system leverages knowledge from similar machines and optimizes maintenance strategies across different pieces of equipment. It makes interactions between humans and machines smoother and predictive tasks more efficient, cutting down on time and resources by using chat interfaces for early and informed decision-making.

Quality Control

Generative AI goes through vast amounts of data, including images of perfect and defective products. This data can come from various sources, such as high-resolution cameras on production lines, historical inspection logs, and even customer complaints. By analyzing this massive dataset, the AI learns the intricate details of what constitutes a flawless product.

It can identify even the slightest defects in real-time, defects that might escape the human eye during a traditional inspection. The AI can analyze historical data to identify areas on products most prone to defects. This analysis can suggest a more targeted inspection approach focusing on these critical areas.

Benefits of generative AI for quality control:

  • Streamlined root cause analysis
  • Real-time defect detection
  • Consistent quality output

Bosch’s Breakthrough Image Quality Inspection

Bosch had already implemented AI image recognition for quality inspection. However, the manufacturing quality in the Bosch plants had already peaked at high levels, which meant collecting data of damages and faulty products to train the AI system became a challenge. So, to get enough image data on defect types – without intentionally producing damaged parts – Bosch switched to a generative AI-based inspection system.

Based on a relatively small number of images for each fault type, the generative AI created over 15,000 artificial images that indicate any error. This approach allowed Bosch to train their models for automated optical inspection way earlier in the production process.

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Workforce Training

When it comes to workforce training, there is no one-size-fits-all approach. This is where generative AI comes into play; it creates tailored learning for every worker.

AI analyzes employee performance data, responsibilities, experience level, and workforce skills to generate personalized training material. These generated training programs also consider the regulations and guidelines prevalent on the shop floor.

Generative AI is not just about reacting to skills gaps in the workforce. It has the ability to predict these gaps by analyzing employee performance data. This foresight allows organizations to proactively design targeted learning programs, ensuring a continuously evolving workforce that is always one step ahead.

Additionally, generative AI facilitates interactive learning experiences through chat interfaces, enabling individuals to interact with AI-powered assistants like ChatGPT. Employees can receive immediate feedback, pose inquiries, and seek clarification on areas they wish to enhance.

Besides the training modules, generative AI can generate dynamic and easy-to-follow work instructions, guiding employees through complex tasks precisely and clearly. AI can personalize instructions based on the task, worker experience, and even equipment variations.

Generative AI doesn’t just provide instructions; it offers real-time guidance. Imagine an AI-powered overlay on a worker’s tablet or smart glasses that highlights their specific step, displays relevant visuals, and even provides troubleshooting assistance through voice commands or chatbots.

Benefits of using generative AI for workforce training:

  • Personalized training on an individual level
  • Improved knowledge retention and skill development
  • Easy access to up-to-date and adaptable work instructions

Lozier’s Smart Search Across All Tools

The retail store manufacturer Lozier implemented an intranet platform with GenAI-enabled search functionality. With this smart search function that lets users crawl through external tools, ticketing systems, messaging apps, and file management systems including Office 365 and Google Drive. With the help of AI, employees get a rich and contextual AI-driven answer to a search query through conversational search and answers.

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Supply Chain

Beyond production, generative AI is revolutionizing another critical area: supply chain management. It offers a powerful toolkit for managing risks, predicting demand fluctuations, optimizing delivery routes, and ultimately boosting efficiency across the entire chain.

The first application of generative AI in the supply chain is demand forecasting, where the AI analyzes historical data and market trends to create demand forecasts. This empowers companies to optimize inventory levels, preventing stockouts and costly overstocking. They can ensure they have the right amount of product available at the right time, maximizing customer satisfaction and sales.

The generative algorithms also optimize the transportation process. The system creates the most efficient delivery routes by analyzing traffic conditions, weather forecasts, and delivery schedules. This significantly reduces transportation costs, fuel consumption, and overall delivery time.

One of the most sought-after applications of AI in supply chain management is inventory management, which showcases its adaptability. It can recommend optimal inventory levels for each product, adeptly considering factors like seasonality, demand fluctuations, and potential disruptions. This reassures companies that AI can handle various factors, helping them minimize storage costs and ensure they have the necessary stock to meet customer needs.

Lastly, generative AI models can create simulations of risk scenarios. Since disruptions and unforeseen events constantly threaten supply chains, the gen AI creates risk scenarios, including supplier disruptions, weather disasters, or political situations. This allows companies to proactively mitigate these risks by diversifying their supplier base, negotiating more favorable contracts, or identifying alternative suppliers in case of disruptions.

Benefits of using generative AI for supply chain management:

  • Increased resilience
  • Data-driven decision-making
  • Improved customer satisfaction

Microsoft Dynamics’ 365 Copilot for Supply Chains

Microsoft Dynamics 365 Copilot can be integrated into the Microsoft Supply Chain Center. It offers proactive identification of external factors such as weather, financials, and geography that could affect critical supply chain operations. Predictive insights highlight impacted orders across various aspects like materials, inventory, carriers, and distribution networks.

Users can then automatically generate emails to notify affected partners and address potential disruptions. Additionally, in-app guidance provides users with immediate, context-specific assistance directly within the application, enhancing user experience and efficiency.

How Generative AI Will Transform Manufacturing

Since generative AI has become a powerful and user-friendly tool, it has transformed industries by boosting efficiency and sparking innovation. Manufacturing is at the forefront of this revolution.

According to a recent study by Capgemini, 48% of surveyed manufacturing companies agree that generative AI will boost their industry. This indicates the recognition of the technology’s potential to transform manufacturing. Manufacturers are already experimenting with implementations to integrate generative AI in their operations; 30% of industrial manufacturing executives report their organizations are already piloting generative AI. Two applications dominate the pilot projects: tailored part design, ranking first, followed by predictive maintenance in second place.

However, the possibilities of generative AI in manufacturing lie beyond design and predictive maintenance. Generative AI is revolutionizing manufacturing by empowering quality control with superior defect detection for flawless products, optimizing supply chains through more innovative forecasting, inventory management, and efficient routes for lower costs and reliable deliveries, and personalizing workforce training with adaptable programs in various formats to boost employee performance and continuous learning, ultimately driving significant gains in efficiency, innovation, and competitiveness across the industry.

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