This is particularly true in the Manufacturing industry, where companies are increasingly embracing AI to make targeted investments with the objective of improving supply chain performance.
Gen AI is more than just a tech fad – it is the culmination of over 50 years of AI research, development and use cases. The current wave of AI capabilities has transformative potential in the manufacturing industry – although it is not without risks.
At its core, GenAI is about creating content – be it text, images, audio, or synthetic data – by learning patterns from existing datasets and generating new, original content. What’s brought this technology into mainstream, and generated the wave of excitement we’re experiencing, is the advent of user-friendly interfaces like OpenAI's ChatGPT and Google's Gemini. These advances have democratised content generation, making it accessible and efficient for users across different domains.
Gen AI and manufacturing
The application of AI innovations in manufacturing (including EdgeAI and Computer Vision) has been growing at a rapid pace, with significant advances in technology and adoption in recent years. The availability of vast amounts of data from Internet-of-Things (IoT) sensors, cheaper computing power via the cloud, as well as advanced deep learning algorithms have led to applications of these AI innovations in a number of use cases in the industry, such as predictive maintenance, quality control and process optimisation, among others. Alongside these other innovations, GenAI has now emerged as part of the AI portfolio for manufacturing. It offers the potential for improvement via further optimisation and automation of work processes where there is need for text content generation.
On the factory floor, GenAI has the potential to more seamlessly integrate essential tasks, ranging from predictive maintenance to quality control and process optimisation. For example, in supply chain management and plant maintenance, GenAI-based co-pilots are now capable of enhancing the workflow and problem-solving capabilities for reliability engineers by producing automatic equipment reporting and root-cause analysis through voice command.
Beyond the factory floor, GenAI can also streamline functional areas such as HR operations. This is particularly crucial in the manufacturing industry, where high turnover rates and a contractor workforce necessitate swift and efficient onboarding procedures to maintain continuity and productivity. By automating routine tasks and refining onboarding processes, it eases the reliance on HR personnel while ensuring that new hires are seamlessly integrated into the workforce.
Preparing for GenAI
As innovations in GenAI continue to advance, the race is on for manufacturing businesses to be ready to leverage the technology or risk losing potential competitive advantage. However, incorporating it into manufacturing processes requires careful consideration.
Firstly, businesses need to understand how GenAI fits into their broader AI strategy, identifying competitive opportunities and threats. Businesses must also establish the Value Case – that is, evaluate the impact of GenAI investment on various metrics, from delivery time to employee productivity, ensuring scalability and demonstrating tangible value through rapid prototyping. Then, choosing the right technology infrastructure and vendors is crucial. Whether it's cloud services from Microsoft, AWS, and Google, or scalable models from innovative startups, it is important to keep abreast of the rapid pace of development among vendors.
Develop a core team of GenAI experts and encourage knowledge building and training not only on technical aspects but also on data security, privacy, and GenAI-specific considerations such as hallucinations (false or misleading information generated), model adoption and prompt injection. Businesses should consider setting up a sandbox environment that allows safe experimentation for both technical and business teams.
Most importantly, the outcome of any Gen-AI-assisted process will only be as good as the quality of the input data and the accuracy of the prompts. It is therefore crucial to maintain data integrity to ensure the accuracy and quality of Gen-AI-generated outputs. Failure to do so risks leaving your business open to potential information processing issues which could create complications in the value chain.
The Future is now
GenAI represents a rapidly advancing technology with the potential to unlock substantial value within manufacturing. By thoughtfully harnessing its capabilities, businesses can enhance human capabilities, drive growth, and pave the way for a truly transformative industry landscape – the initial steps of which are already underway.
John Woods, Alvarez & Marsal
Promoted content: Does social media work for engineers – and how can you make it work for you?
So in addition to doing their own job, engineers are expected to do the marketing department´s work for them as well? Sorry, wait a minute, I know the...