Building on ambition: Enabling the future of manufacturing with Gen AI

Gen AI is certainly one of today’s hottest boardroom topics. Organizations of every shape and size are looking at the technology and asking questions of how it can help optimize the core tasks, processes, and workflows that underpin everything they do.

Equally, and as covered in previous blogs, the application of Gen AI is almost boundless – from inspiring large RISE with SAP transformations to reimagining the software development life cycle.

Yet while big-picture thinking on the full potential of Gen AI is a critical, ongoing endeavor, it’s also vital to follow up such analysis of what’s possible with practical use cases.

Knowing what Gen AI can do in creating new content, simplifying the analysis of complex data streams, and streamlining information access is important – but so is understanding how this capability can be applied to day-to-day realities. So, with that in mind, let’s look at opportunities for applying Gen AI within a specific industry sector – in this instance manufacturing.

Getting productive

Speak to any manufacturer about their operational challenges, and common issues soon emerge relating to the seemingly endless task of improving productivity. A task made excessively complex by the myriad variables involved, ranging from material availability to unexpected machine breakdowns.

What’s more, surrounding any manufacturing process is a wealth of structured and unstructured data – typically residing in SAP and non-SAP systems – that if available in a timely manner can provide both advance warning of upcoming problems and options for immediate resolution.

In effect, efforts to overcome the “productivity barrier” are ultimately focused on turning this raw data into actionable insight. To this end, many technologies, from manufacturing execution systems (MES) to advanced analytics, are already employed. Indeed, much of the required insight can be made available and embedded into established workflows.

But with its ability to be trained to follow precise rules, analyze vast data sets, detect discrepancies, and provide tailored responses, Gen AI can truly “democratize” the flow of insight across manufacturing operations. This is a capability that in turn lowers the barrier for people to discover (or receive) timely intelligence on which to base their decisions.

Advancing the journey to Industry 4.0 (and beyond)

The Capgemini Research Institute’s report, Harnessing the value of generative AI: 2nd edition – Top use cases across sectors, highlights how organizations are leveraging generative AI to enhance operational efficiency, foster innovation, and unlock new revenue streams across industries, including manufacturing.

Here are a few examples of manufacturing capabilities currently being developed by Capgemini:

Asset availability – with Gen AI models able to create real-time forecasts of capacity, predict potential machine stoppages, and maintain a more dynamic form of production scheduling that can react instantly to any stoppages or lack of available resources/labor.

Product quality – where quality checklists incorporate voice controls and real-time updates based on product-specific quality intelligence, to fast-track the process both at the assembly line and within the warehouse operation.

Workforce self-service – with Gen AI used to diagnose the root cause of high priority issues, suggest possible resolutions to users, and propose long-term solutions to prevent recurrence – as well as the process and time needed to implement them.

Supply chain optimization – where Gen AI combines historical data with real-time forecasts to create highly detailed bills of materials (BOMs) and matching this to existing stock levels and known supplier availability to cover predicted shortfalls.

Time to insight

With all these use cases, the value of Gen AI comes in its ability to provide a simplified interface between users and a bewildering array of complex data. Answers can be found without using the technology, but often in a way that requires too much time and effort to make the process viable – as well as a basic skill level for performing such analysis. Which is why, in the past, key insights that could transform both user productivity and customer relationships were often left hidden in the detail.

An example of Gen AI turning vast data sources into meaningful insight can be found in a current project with a semiconductor manufacturer using SAP solutions. As with any Gen AI initiative, the work has originated from a clear operational problem:

• The client has a portfolio spanning thousands of products and components – each accompanied by a mass of documentation.
• Responding to customer queries means people having to navigate through these design and specification documents to find answers – an exhaustively inefficient process.
• As a result, customers may not always be presented with the ideal product recommendations, and receive a slow response to any urgent sales inquiry.

Where Gen AI can help is in bringing together the structured and unstructured documentation surrounding each product. This data can then be queried via a conversational chat window, with responses increasingly tailored to the expectations and personalities of individual users. As a result, sales and technical employees can now ask questions – such as “what’s the best components mix to meet a customer’s precise specification?” and “what’s the fastest, most economical and sustainable way to supply these products from our global operation?” – and receive answers in seconds.

Final thoughts

Gen AI opens a window into an organization’s collective knowledge and intellectual property. It is a trained intelligence able to interpret a user’s intent and produce the most relevant data possible. It’s about radically shortening time to query and providing a layer of contextual understanding that helps advance the collective ambition for Industry 4.0 and beyond.

Challenges exist in introducing an industrialized Gen AI solution that can be trusted to consistently deliver authentic answers, from architecting the right solution to implementing the correct policies and controls. But as Capgemini is routinely demonstrating, these issues can be quickly solved with a robust implementation process and in-depth industry expertise. The hardest part remains the conceptualization of different use cases and imagining how and where Gen AI can complement existing processes – while inspiring new ways of tackling old problems.

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