For years, the giants of manufacturing have been on their toes, eagerly embracing every technology wave that came their way. Despite their roots stretching back centuries and dominated by baby boomers, these stalwarts haven’t missed a beat in evolving with the times. And now, with the entry of Generative AI (GenAI), they’re at it again, rolling up their sleeves to incorporate this breakthrough technology into their product life cycle. And it is everywhere, turning every function into an innovator and transforming manufacturing from concept to retirement of the product.

Imagine every bit of data from every function of the manufacturing enterprise being tossed into a massive pot. These functions don’t even have to dig through this pot for insights; instead, they cast a “magic spell”—a prompt—to GenAI and receive exactly the information they need, synthesized from the entire organization’s data. Every department gets insights from the pooled knowledge – from customer service to product development, operations, maintenance, and beyond.

GenAI can effectively be used as a powerful blender of data, breaking down silos across product life cycle stages and enhancing productivity by up to 20%1. Here’s how it works: insights from operations and maintenance become inputs for product improvement and innovation. Engineering can design new product features based on customer service data, and marketing can analyze maintenance logs to understand product short comings and longevity.

This cross-pollination of information ensures that decisions are informed by a comprehensive understanding of the entire product lifecycle, from concept to customer feedback and back to the drawing board again.

After more than a year of diligent experimentation, research, and development, we share insights on how GenAI fits into and dramatically adds value to every stage of the product manufacturing lifecycle.

Starting with a Spark: The product development phase

Manufacturers must put innovation2 and digital transformation at the heart of everything. So far, traditional AI has been our go-to guide in product design based on past successes and current demands. But the very idea of looking into history goes against the principles of innovation.

GenAI addresses this challenge. With the ability to churn through mountains of design standards, specifications, Operations, and daily maintenance data, GenAI doesn’t just look back; it looks around and ahead. It simulates countless design scenarios3 and pushes us beyond the familiar to designs that are novel and deeply resonant with future customers and industry shifts.

Making It Real: The production planning and manufacturing phase

When it’s time to turn those ideas into tangible products, GenAI’s ability to generate new ideas and solve complex problems on the fly makes it a game-changer for efficiency and customization. It helps plan your production line, foresee hiccups before they happen, and ensure quality is top-notch. It gives you a heads-up to dodge pitfalls and streamline your processes. It can generate and recommend bespoke manufacturing plans and approaches for new or customized products. It can assist manufacturing shop floor stake holders with appropriate engineering & manufacturing specifications and generate automated deviation resolution with documentation.

Unleashing Precision: The Quality Control and Assurance

Manufacturing organizations who are early adopters of digitalization have been using AI for defect detection and quality assurance, employing image recognition and machine learning to identify issues that might escape the traditional inspection processes. It’s been beneficial, but we could only predict known issues so far and have failed to flag any new categories of defects and anomalies. GenAI fills this gap. It simulates various manufacturing conditions and design variations to identify new defects. And not just through textual data. It can train on a vast array of synthetic defect out images to recognize subtle defects that could have been overlooked before. This means that not only can we catch more issues, but we’re doing it way faster and with greater efficiency.

More importantly, this is not just a one-time setup. As we introduce new products or processes, GenAI adapts and learns, making the whole quality assurance more thorough and future-proof.

Elevating Efficiency: Maintenance and Troubleshooting

Manufacturers endure, planned and unplanned downtime annually4, leading to substantial revenue losses, inflated labor costs, and strained business relationships. GenAI takes predictive maintenance to a whole new level5. It knows when a machine will fail, suggests a range of solutions, learns from each repair, and improves the maintenance schedule dynamically. By analyzing vast arrays of data from different sources, we can extract more nuanced troubleshooting and maintenance strategies, enhancing efficiency and reducing downtime.

Clearly, beyond troubleshooting, the perks of blending Gen AI and LLMs (Large Language Models) into product development, operations, and maintenance are nothing short of game changing. Taking these use cases even further, GenAI can also make an impact in testing and certification. We can generate test documents and streamline the certification process, ensuring products meet all necessary standards and regulations before reaching the market.

A net-positive impact on sustainability

While GenAI optimizes and enhances every step in the manufacturing lifecycle with its nature and features, there’s a broader vision at play. The push towards sustainable practices has been on the agenda for most of the executives, but not many have been able to act on it. GenAI can accelerate this vision.

It is important to note that, GenAI does contribute to carbon emissions due to its computational demands. However, when we use it strategically to enhance sustainability practices, the overall environmental impact is net positive. Here are some use cases:

  • Energy Efficiency: GenAI goes through energy consumption data to find patterns and inefficiencies, suggesting operational tweaks for machines.
  • Greener Materials: On the materials front, GenAI can pinpoint eco-friendly materials and enhance recycling processes.
  • Waste Reduction: Fixing machines before they fail, decreasing the likelihood of producing defective goods that end up as waste, preventing overproduction, and addressing the issue of excess inventory that could become waste can be achieved with GenAI’s predictive capabilities.
  • Lifecycle Management: We can design products that are durable, repairable, and recyclable, championing the principles of a circular economy where waste is minimized, and resources are reused.

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Is this as good as it sounds?

Of course, it’s not all smooth sailing. Lack of high-quality data for accurate outputs, integrating these advanced technologies seamlessly into existing systems and workflows, and overcoming hallucinations can all be significant challenges. However, consistent model training, fine-tuning, and rigorous testing make it more reliable. The key takeaway, however, is that GenAI is a force multiplier and here to stay with gamechanger possibilities. It ensures that all functions do not just remain a cog in the machine but turns into an active contributor to the organization’s innovation and growth.

It’s a thrilling time, with the potential to redefine manufacturing as we know it and far beyond what we imagined possible with traditional methods. Let’s embrace this journey and see where this exciting partnership with GenAI takes us!

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies