Shaping the Future of Manufacturing with LLMs and Generative AI

Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) represent two of the most recent developments in the realm of artificial intelligence. They enable the generation of human-like text, images and other multimedia outputs (e.g., videos) based on AI models that have been trained with vast datasets. These technologies are nowadays exceptional in both understanding and generating natural language, which enables them to perform a wide range of intelligent tasks from conversation emulation and content creation (e.g., blog writing), to analysis and reasoning over rich collections of facts and figures. 

Even though LLMs have been around for nearly a decade, their popularity skyrocketed following the release of ChatGPT, which is currently the application with the fastest growing user base in the history of the internet. This release back in November 2022 marked a significant milestone in the practical applications of LLMs, as it gave rise to the creation of a vibrant ecosystem of LLM-based apps that are prompted by human users to generate information and automate many different tasks in virtually every sector of the economy. Many of these applications leverage advanced LLM models and GenAI capabilities to produce designs, blueprints, software code, and solutions that mimic human creativity and analytical skills. To this end, they leverage and enhance large-scale foundational GenAI models (e.g., GPT3.5, GPT-4, Claude, LLaMa3), which have been trained with tremendous amounts of data and come with a wide range of context specific and energy efficient capabilities. Most importantly, these models are constantly improving in terms of knowledge, robustness and reasoning capabilities.  

The transformational power of LLMs and GenAI lie in their ability to lower the barriers for access to intelligence. Specifically, they enable a shift from AI systems that are built by experts (e.g., machine learning engineers) to GenAI systems that can be built and commanded by virtually any domain expert. In the near future, organizations will be able to increasingly use AI systems simply by prompting large scale LLM models rather than having to build conventional custom, domain specific, machine learning models (e.g., deep neural networks). This transformational impact will affect organizations in different sectors, including manufacturing enterprises and their supply chain partners.

LLMs and GenAI Use Cases in Manufacturing

GenAI is already transforming manufacturing operations based on the following use cases:

• Product Design Generation: The application of LLMs in generating innovative product designs is revolutionizing the way manufacturers conceptualize new products. Based on generative models, manufacturers can explore a wider array of design possibilities, which significantly reduces the time and cost associated with product development. Imagine for example an automotive manufacturer wishing to design its new SUV (Sports Utility Vehicle) model. Its product design engineers can command an LLM model to rapidly generate 100s of relevant configurations, including many variations of different aspects and parts of each design. This process that would require weeks or months can be nowadays completed within a few hours. 

• Generating Optimized Production Configurations: As a next step to producing product designs, LLMs can be also used to rapidly generate production configurations and recipes for specific products. In this direction, LLMs can be used to analyse production data and to simulate different configurations in terms of their CO2 emissions, environmental performance, and production costs. The simulation process can include experimentation with different materials and production pipelines. In these ways, LLMs can boost the fast and effective identification of optimized configurations that balance cost efficiency with production quality and environmental considerations.

• Personalizing and Increasing the Efficiency of Industrial Training:  LLMs can tailor training materials and simulations to the learning pace and style of individual employees. Hence, they can enhance the efficiency and effectiveness of industrial training programs. This personalized approach not only accelerates competency development but also addresses the unique needs of each trainee. LLMs can therefore become the technology cornerstone for the implementation of the human-centred industrial applications of the Industry 5.0 era.

• Troubleshooting Support Through Effective Access to Documents and Knowledge Bases: Based on the integration of LLMs with existing knowledge bases, manufacturers can provide real-time, context-sensitive support for troubleshooting and problem-solving. These applications will significantly enhance the responsiveness and accuracy of support services, which will be offered based on Natural Language Processing (NLP) interactions to manufacturing workers. Furthermore, LLMs can essentially augment the diagnostics functionalities of field service engineers, through reducing the time needed to access, browse, and process documents (e.g., technical manuals).

• Improved Streamlining of Production Operations: GenAI can improve the streamlining of production operations by replacing complex data-driven pipelines involving machine learning with user-friendly LLM prompts. For instance, workers could prompt an LLM to identify potential supply chain risks (e.g., bottlenecks) for available orders. The LLM can then trigger queries to multiple systems and databases towards answering the query, in ways that reduce interaction times. Moreover, GenAI/LLM systems enable the development of powerful digital twins that incorporate effective LLM reasoning strategies (e.g., Chain of Thought (CoT), Tree of Though (ToT)) instead of the more time consuming and tedious processes of building custom AI-based reasoning capabilities. 

Overall, while GenAI in manufacturing is in its early stages, there is already evidence that LLMs will fundamentally change the way human workers will interact with systems (e.g., machinery) and documents (e.g., analytics reports). Moreover, GenAI will boost the extraction of hidden insights from unstructured data in support of many different use cases in automation, customer service, and business intelligence for manufacturing enterprises.

As already outlined, LLMs are likely to revolutionize manufacturers’ access to intelligence, through enabling domain experts to prompt LLMs rather than relying on AI engineers to develop the intelligence needed. Thus, LLMs could soon be used in a variety of legacy AI-based use cases in manufacturing such as predictive maintenance and supply chain optimization. For instance, we will see enterprise maintenance engineers deploying and using LLM-based systems that forecast and anticipate equipment failures towards scheduling service and repair operations at optimal points in time. 

Steps to Harness the Power of LLMs in Manufacturing

Manufacturers cannot afford to ignore the LLM revolution. Rather they had better consider the capabilities of GenAI and integrate them in their AI strategies. In this direction, they can undertake the following steps and activities: 

• Collect and Structure their manufacturing data: Access to LLM models like GPT4, Claude and Gemini are likely to become commoditized. Hence, for most manufacturers, innovation will stem from their own data and knowledge rather than from the use of the LLM technology. As a result, a foundational step in leveraging LLMs involves the collection and structuring of manufacturing data. This data will feed the initial training of customer and enhanced LLM models, while also supporting ongoing learning and adaptation.

• Development of Custom (Vector) Databases: Manufacturers must seek ways to enhance and customized their LLMs infrastructures towards supporting their use cases. In this direction, they must implement vector databases using new sets of embeddings that will enhance the efficiency of querying and retrieving information for various manufacturing use cases. The latter will boost the performance and accuracy of LLMs in manufacturing scenarios.

• Employee Training on LLMs: It is very important to equip employees with the knowledge and skills they need to utilize LLM technologies effectively. This training includes understanding the capabilities, limitations, and best practices associated with these tools. It is a key prerequisite for elevating employee productivity and maximize the benefits of LLMs and GenAI.

• Establish Custom Private LLM Infrastructures: The design and implementation of customized LLM infrastructures can significantly enhance the customer experience, offering tailored interactions and solutions. At the same time, private LLM infrastructures can protect corporate data and become engineered to reduce the CO2 of their operations. In the medium and long term, no manufacturing enterprise would like to rely on third-party infrastructures for access to intelligence. They will be using their own, customized infrastructures.

Overall, the integration of LLMs and GenAI into manufacturing processes signals a new era of efficiency, innovation, and sustainability for industrial organizations. The ability to generate novel product designs, optimize production configurations, and tailor training programs are among the main benefits that these technologies offer. In this context, the adoption of LLM and GenAI technologies is not merely advantageous but essential for maintaining a competitive edge. Manufacturers are therefore urged to proactively explore and implement LLM strategies towards harnessing the transformative potential of these technologies in ways that redefine manufacturing excellence.