Since ChatGPT thrust artificial intelligence (AI) into the public domain and greater awareness a few months ago, there have been many discussions about the practical applications of AI and the impact on jobs and businesses. I mean it’s fun to throw questions at ChatGPT and marvel at the quality of the answers, but playing with a new toy and getting real measurable and practical benefits from a tool in our daily professional lives are two very different things. So while people have shown how to use ChatGPT and GPT-4 in their current form to write letters, marketing materials, posts etc, practical industrial applications for product development, manufacturing etc have so far not been explored or demonstrated in great detail, and certainly not implemented and used.
So what are some potential practical applications of AI specifically in product development and manufacturing? In time AI certainly has a wide range of practical industrial applications in these areas. By leveraging the power of AI, businesses can optimize processes, reduce costs, and increase efficiency. Some possible applications include:
- Design optimization: AI can analyze vast amounts of data to suggest design improvements, identify potential flaws, and optimize the product for manufacturing, reducing material waste and costs. Generative design is one such application where AI algorithms generate numerous design variations based on set parameters and constraints.
- Predictive maintenance: AI-driven predictive maintenance systems can analyze sensor data, historical information, and environmental factors to predict when equipment is likely to fail. This enables businesses to schedule maintenance only when needed, reducing downtime and maintenance costs.
- Quality control: AI-powered computer vision and machine learning can detect defects and inconsistencies in products, improving overall quality. These systems can learn from data to become more accurate over time, surpassing human capabilities in many cases.
- Supply chain optimization: AI can help manage and optimize supply chains by analyzing data from various sources such as demand forecasts, inventory levels, and production schedules. This can lead to better demand planning, reduced stockouts, and improved supplier management.
- Production planning and scheduling: AI-driven algorithms can optimize production planning and scheduling by considering factors like machine capacity, workforce availability, and order deadlines. This leads to higher throughput and reduced lead times.
- Robotics and automation: AI can improve the performance of industrial robots, allowing them to perform complex tasks and adapt to new situations. This can result in higher productivity and reduced dependence on manual labor.
- Energy efficiency: AI can monitor and optimize energy consumption in manufacturing plants, identifying opportunities for energy savings and reducing overall operational costs.
- Process optimization: AI can analyze manufacturing processes to identify inefficiencies and suggest improvements, resulting in reduced cycle times and increased output.
- Customization and personalization: AI can help manufacturers develop customized products based on customer preferences and requirements, leading to increased customer satisfaction and loyalty.
- R&D and innovation: AI can augment human creativity in research and development, assisting with materials discovery, new product ideas, and innovative solutions to engineering problems.
These applications demonstrate the immense potential of AI in product development and manufacturing, offering businesses the opportunity to stay competitive and grow in an increasingly complex and dynamic market.
However, we are not there yet. While GPT-4 and ChatGPT have been trained using a huge amount of publicly available information, the information needed to do the tasks listed above require access to individual company’s intellectual property that they certainly wouldn’t want to be shared with the public. This means in order to make AI truly usable for companies, it would have to have all the private information available and be trained on it in addition to generally available best practices, and it would have to be ensured that the AI does not share this information with anyone outside of the respective company.
So while the technology is there, I think the primary challenge for individual companies in leveraging AI in product development and manufacturing is the availability of the information required to make the AI smart enough to be useful in those areas. I think there most companies still have some homework to do.