In this blog, we explore the transformative potential of generative AI in advanced manufacturing technologies, highlighting its applications and successful implementations. It addresses current limitations and challenges in adoption and offers a call to action for manufacturers to consider these innovative solutions.
Generative AI “manufactures” words and images all the time, but can it help build actual, physical products? The short answer is yes. The more complete answer is, “Yes, once you understand how to use it.” That being said, GenAI and manufacturing can be excellent partners, especially for producers who need to increase efficiency and decrease costs.
Here, we dig into what manufacturers currently use AI to do, how GenAI can play a significant role in your operations and solutions, and some challenges to look out for as you plan your GenAI implementation.
The Current State of Advanced Manufacturing Technologies
Generative AI in manufacturing stands on the shoulders of several advanced manufacturing technologies — many of which have improved speed and quality for years.
Predictive Maintenance
Predictive maintenance involves using data to predict when and how to maintain machinery. Since AI algorithms do such a good job of identifying trends, they are used in predicting:
- When to service components and then set up automated service schedules
- When certain machines are most likely to reach end-of-life status, which makes it easier to decide whether to purchase new units or repair existing equipment
- The future costs of maintenance, which is useful for budget projections, financing and capital restructuring
Robotic Process Automation
Robotic process automation (RPA) is currently used in manufacturing to automate repetitive processes humans normally take on. For example, you can use an RPA system to gather inventory data and automatically choose the best supply chain to source crucial components.
Sounds a lot like AI, right? The difference between RPA and AI is that an AI system can learn independently and then use data to make decisions. RPA, on the other hand, needs to be programmed and told what to do based on predefined conditions.
Image Recognition
Image recognition is a form of AI that enables a computer to identify components, people, animals, and objects and then feed that data to another system for decision-making. In manufacturing, companies use image recognition to inspect quality and detect defects.
The system can recognize anomalies indicating a problem or weakness and then send that data to an integrated system. The system can then report the necessary information or adjust machine settings to address the issue.
Limitations of Current Systems
Even advanced manufacturing technologies can’t create things on their own. For example, an RPA can automatically follow a program that enables it to print different product labels based on product IDs. But it can’t create a label from scratch.
Also, a lot of automation in manufacturing requires extensive human input and maintenance to function properly. Depending on the solution, this may consume many precious hours a week, eroding the efficiency of the system.
This is why more innovation is key, and generative AI is poised to play a leading role in helping manufacturers do more in less time while creating superior products.
Generative AI in Manufacturing: A Game-Changer
Artificial intelligence can be very effective for automating elements of manufacturing processes. But GenAI is different. You can use it to create things from scratch. Here are some of the concepts and use cases driving the adoption of generative AI in manufacturing.
Creative Design
Generative AI initiatives typically aim to reduce the time humans spend ideating and creating so they can move on to testing ideas, refining their designs, and gathering feedback. For manufacturers, GenAI can play a particularly vital role, especially if you use it to:
- Quickly sketch up prototypes based on your input
- Create product descriptions you can include in catalogs and presentations
- Build videos and other collateral to showcase your concepts to team members, investors, or other external stakeholders
- Produce detailed plans for prototypes, rendering production-ready designs you can feed to a 3D printer or CAD-driven machinery
Keep in mind that these creative design functions aren’t replacements for human designers. Instead, they’re multipliers that expand human potential.
Engineering Components
GenAI can tell you how to build components in a way that aligns with the goals of each project.
To illustrate, suppose you’re building car parts and need to reduce the weight of your vehicles to optimize fuel efficiency. Each material you’re considering using to build components has various factors you need to account for, such as weight, tensile strength, cost, and where you have to source it from.
You can feed all of those factors into a GenAI system and ask it to tell you which materials to use based on:
- How their weight contributes to fuel efficiency
- The cost of materials
- The strength of the final product
- The availability of each component according to your current — and even future — supply chain structure
It’s crucial to use AI responsibly. For instance, when engineering components it’s important for human experts to check and verify any recommendations that an AI system makes. An organization can expose itself to serious safety, cost, and compliance risks if they blindly follow what an AI says.
Designing More Efficient Workflows
Armed with GenAI, a manager or executive can design better and faster ways of producing goods without having to crunch numbers and analyze data themselves.
For example, let’s say you have data regarding each station on an assembly line, their production statistics, and the overhead costs associated with those running each machine. You want to ramp up production in time for a busy season but don’t know exactly how.
You can feed your data to a generative AI solution, and it can tell you:
- Who to run each machine
- The underlying expense to produce each component
- The amount of time it will take to reach your production goals
- The least expensive way to meet your goals and the time or quality-of-work sacrifices that may be involved
GenAI can do this because it not only constructs ideas but can also perform data analysis and help leaders make decisions accordingly.
Generative AI in Manufacturing: Use Cases
Airbus Uses GenAI to Create the Plane of the Future
Passenger plane manufacturer Airbus combined GenAI and biomimetics to design a more efficient plane, dubbed “the jetliner of 2050.”
Biomimetics involves analyzing design structures in nature and then imitating them as you engineer new concepts or improve old ones. In the case of Airbus, the company used GenAI to create a more efficient partition for their plane of the future.
The company’s team also used AI to build an algorithm for a plane partition based on the structure of mammalian bone growth. Their densities vary at different points, providing necessary strength without adding excess weight. This resulted in a partition that was 45 percent lighter than Airbus’s traditional option.
BMW Uses GenAI to Create a Car Companion
BMW has introduced its BMW Intelligent Personal Assistant. This is a GenAI model powered by Alexa that serves as a passenger’s persona vehicle expert. The system can answer driver questions quickly to minimize distraction.
The Personal Assistant also can control some of the vehicle’s functions. This control prevents drivers from having to take their hands off the steering wheel or eyes off the road.
BMW’s Personal Assistant use case highlights the potential of using GenAI to enhance manufacturers’ end products. By weaving a GenAI-powered, customer-facing solution into your offering, you bridge the gap between the solutions you build and those that use them.
Overcoming Challenges and Maximizing Benefits
One of the most common obstacles in adopting generative AI in manufacturing is figuring out how to design and train the system without breaking the bank. Remember that much of the legwork can be outsourced to AI experts, especially regarding the underlying data powering your GenAI.
Once you let the pros handle the programming, you just have to follow their lead. In many cases, they’ll tell you to choose data that’ll be used to create a knowledge base or training materials. Often, all of the data you need is already at your fingertips via machine output data, maintenance information, and performance data.
Another challenge is infusing a GenAI-positive culture into your company. Some are used to doing things the “old-fashioned way” because it’s worked for so long. But here are some best practices for a successful GenAI integration:
- Encourage employees to use GenAI when performing a range of tasks, such as writing simple emails and planning work trips. This helps weave GenAI into your organization’s fabric — even before it hits the design studio or manufacturing floor.
- For operational implementations, build a list of all creative and analytical processes that GenAI could benefit. This will help you focus your efforts on specific use cases that empower your people.
- For customer-facing implementations, touch base with customer service reps and customers regarding pain points that GenAI could help with.
- Choose qualified AI experts early in your consideration process. Their guidance can save you a lot of time, especially because they understand what’s possible and how GenAI can get you there.
- Create and adhere to a responsible AI governance system. This can build confidence and trust in your AI initiatives, especially if it includes policies that emphasize the ethical use of AI and transparency around AI engineering.
The Future Outlook for GenAI in Manufacturing
AI agents will power the future of GenAI in the manufacturing sector. While traditional GenAI can answer questions, an AI agent can use reasoning to make well-researched, data-backed decisions. They can even build out entire plans of action and provide analysis that explains the advantages and drawbacks of each plan they create.
For example, an AI agent could help you more effectively manage your supply chain system. It can inform key decisions you make such as which parts to order, how many, when, and from whom. It could also place orders, track them, and update your inventory — all without the step-by-step, human prompting a traditional GenAI solution needs.
Start Building Your GenAI Manufacturing Solutions Now
GenAI can speed up your creation process, including prototyping and testing. You can also use generative AI to make decisions that enhance productivity. For some manufacturers, GenAI can also be a powerful tool for interfacing with customers and answering their questions — effectively using computers to create more human experiences.
Now is the time to start exploring the potential of GenAI for your organization so you can plot a path for adopting these technologies.
