Quality control: This is a one of the most promising areas where GenAI can make significant strides. GenAI can be trained on vast datasets where it learns to identify product defects and anomalies along with potential issues in real time. This proactive approach to quality control minimizes defects, reduces waste, and ensures that only high-quality products reach the market.
Troubleshooting: Manufacturing processes often encounter unexpected issues that can disrupt production. Factory workers on the plant floor may be experts in their roles on the production line and understand the workings of key equipment, yet struggle to embrace technology. GenAI allows them to simply speak their request into a model and instantly receive feedback in the form of voice or text, whichever they prefer. This can expedite the troubleshooting process, reduce downtime, and minimize the impact on production schedules.
In 2024, manufacturers will increasingly apply it to their businesses and add it as a critical element in their digital toolboxes and as a powerful driver to their quality protocols. In fact, Gartner predicts that by 2026, more than 80% of enterprises will have used generative AI models and/or deployed them in production environments, up from less than 5% in early 2023.
GenAI: The gateway AI tool
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AI, in the traditional sense (if such a nascent technology can be called traditional), has involved intelligent ’bots roaming the warehouse and performing routine tasks. It also has involved predictive analytics, which leverages data to forecast future events, or machine learning systems that automatically detect product defects on the assembly line. Although these types of AI are being deployed by a minority of manufacturers, they’re still in their infancy and require massive disruption of how manufacturers operate.
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GenAI is a form of artificial intelligence that “self-learns” via large amounts of publicly available data to generate new content in the form of text, images, video, and other types of information. The speed at which it responds to natural language prompts to create highly relevant content is unprecedented.
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How Trailblazing Manufacturers Are Using Generative AI in 2024
From design to inspection to supply chain management, AI is transforming manufacturing
Production: GenAI can be used for better production planning, optimizing schedules, resource allocation, and workflow. By considering factors such as machine capacity, raw material availability, and workforce, it can assist with the development of optimal production plans that minimize downtime and maximize output.
GenAI is transforming manufacturing by streamlining design, optimizing production, improving quality, and enabling safer and more responsive manufacturing environments. As the new year continues to unfold, we can expect to see more manufacturers embracing it and building it into their tech stacks, enabling a whole new level of intelligence to support human ingenuity.
Because AI is trained on massive datasets using what are known as machine-learning algorithms to predict future outcomes, it can help manufacturers make better, faster, and more data-driven business decisions, such as anticipating supplier disruptions, optimizing production lines, reducing waste, or acting on customer feedback with product quality improvements.
Generative AI took the world by storm in 2023, from the classroom to the film studio, and the writer’s bench to the White House. Enterprises and creative industries worked to figure out how to leverage it in their operations, while classrooms and government entities struggled to govern its use.
Product design: One significant application of GenAI in manufacturing is product design. Designing complex products often involves numerous iterations and prototyping. GenAI can analyze vast amounts of data, taking into account constraints and objectives to create optimal designs. This not only accelerates the design process but also results in more efficient and realistic products, saving materials and reducing costs.
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Foundation models from GenAI systems can automate processes, aid humans or machines in their daily tasks, and autonomously execute business and IT processes. They’re being used by businesses to generate marketing content and articles, develop customer communication, and identify future trends. In software development, they can be used to document software code, predict code sequences, and automate other software development tasks.
However, the ubiquity and easy access to open source (and free) GenAI models in 2024, such as ChatGPT or Google Bard, will enable manufacturers to become more familiar and comfortable with AI without a significant financial investment or massive disruption. GenAI will become the conduit to AI-driven manufacturing operations, helping manufacturers to imagine the possibilities that machine intelligence can provide. But it all starts with an understanding of what exactly GenAI is.
Understanding GenAI
Onboarding/training: GenAI can be used to train staff on standard operating procedures and the safe use of equipment. It can produce documentation and training materials, answer employee questions, and provide guidance in real time.
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Consider the following ways GenAI will be used by manufacturers.
While GenAI will play a major role in helping companies leverage the vast amounts of data available internally and across the world, as a nascent technology, it has obstacles to overcome. There’s currently no way to confirm the accuracy of artifacts that GenAI produces, nor a way to verify that it’s presenting unbiased data. Additionally, there are fears that GenAI’s ability to create simulated imagery or mass communication could enable those with sinister intent to spread false information, or worse.
Despite the challenges, Forrester predicts that GenAI “will be the fulcrum that businesses rely on to enhance, empower, and engage employees and customers.”
Supplier management: The supply chain can benefit from GenAI by identifying trends in historical data to optimize inventory levels and logistics. This results in reduced lead times, lower transportation costs, and a more responsive and resilient supply chain.
Generative AI (GenAI), built upon large language models, has democratized artificial intelligence and made it accessible to manufacturers of all types and sizes. Yet it’s important to understand how it differs from the types of AI that have been discussed and deployed by only the largest of manufacturers.
Software development: As enterprisewide systems become more complex, GenAI can assist with software coding, helping to integrate critical applications or build new applications. A McKinsey study found that software developers can complete coding tasks up to twice as fast with GenAI.