Using Gen AI to Shorten Product Development Cycles

Manufacturers based across the globe are taking proactive steps to brace themselves for a possible economic downturn by adopting various strategic measures aimed at boosting resilience, in addition to maintaining revenue and market relevance. 

Key priorities include diversifying product lines and expanding into new markets and regions, but, as this must happen at pace, reducing product development cycles is necessary. 

Helena Jochberger, Vice President, Corporate Services at CGI, believes generative AI (Gen AI) represents a significant building block in this endeavour, enabling companies to reduce development cycles drastically and remain competitive – even in turbulent times. 

According to CGI’s Voice of Our Clients research published earlier this year, Gen AI continues to be at the top of innovation discussion agendas, with trust and responsibility key priorities. In manufacturing, 79% of executives interviewed are investigating or conducting proof-of-concepts for Gen AI. 

Helena notes: “With the potential this technology holds to streamline design processes, reduce time-to-market and enhance product quality and innovation, this growing interest is expected.”

The pitfalls of nonconformity management

Nonconformities in research and development (R&D) refer to discrepancies between the intended design and the actual results of a production process or testing phase.

Helena points out that they not only increase the cost of production, but also the quality of the entire product – critical in sensitive industries such as aerospace, where airworthiness is vital.

“In a traditional setup, nonconformities are identified during production or the assembly phase, triggering the creation of tickets in an internal tracking system,” Helena explains. 

“Each ticket undergoes a meticulous review process, requiring input from various experts to diagnose the issue, determine the root cause, and implement a fix. 

“This process, while thorough, is inherently slow and resource-intensive. Engineers spend countless hours digging through data, cross-referencing design documents, and collaborating across departments to resolve each issue.”

Harnessing the power of Gen AI

As an example, Helena puts forward a scenario where a Gen AI model is integrated with the internal ticketing system and trained on a comprehensive knowledge database of past nonconformities, design documents and assembly data. 

Once trained, this AI system would possess the ability to carry out the following tasks:

• Ticket automation analyses: “AI automatically analyses new nonconformity tickets, compares them with historical data and identifies potential root causes within seconds,” Helena continues. “Immediate analysis can significantly accelerate the initial diagnostic phase.”
• Anticipative issue prediction: “When pattern recognition is leveraged,” Helena says, “areas of nonconformity can be predicted even before they occur, allowing anticipative measures to be taken during the design or assembly phase. This early-stage feedback loop also ensures digital continuity throughout the design phase. Including such a predictive possibility will help reduce the number of nonconformities that arise.”
• Recommend solutions: “Based on past solutions,” Helena adds, “AI can suggest potential fixes for identified nonconformities, reducing the time engineers spend formulating solutions and, as a result, increasing the accuracy of fixes.”

A transformative shift

Implementing Gen AI to manage nonconformities clearly has the potential to bring about remarkable improvements in a number of key areas. 

These include: 

• Speed of solution: Automating the initial analysis and providing probable solutions shortens the time needed to resolve each nonconformity from days or weeks to a matter of hours. This faster response time accumulates and significantly decreases the overall product development cycle.
• Optimisation of resources: By automating the initial diagnosis, engineers and experts are freed from routine tasks and can focus on more complex problem-solving and innovation. This shift maximises the use of scarce skilled professionals and boosts overall productivity.
• Enhanced quality and reliability: Quicker and more precise resolution of nonconformities leads to higher product quality and reliability, directly improving customer satisfaction and reducing issues in after sales and maintenance processes.

“Integrating Gen AI into the nonconformity management process represents a transformative shift for complex R&D environments,” Helena concludes. “By harnessing the power of AI, organisations can dramatically reduce the time and resources spent on resolving nonconformities, leading to shorter product development cycles, enhanced innovation and superior product quality.

“As we approach these new possibilities, Gen AI holds immense potential to infuse innovation within the R&D landscape. By embracing these advanced technologies responsibly, companies can streamline development processes and gain a competitive edge by delivering high-quality, reliable products to market faster than ever before.”

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