GFT & Google Cloud: Dismantling Data Silos To Unlock AI
Brandon Speweik is Head of Industry Sales & Strategy at GFT, where he helps manufacturers transform operations through technology. He enjoys learning from the diverse people he encounters, considering this the most rewarding aspect of his role.
“I’m a proponent of leading by example and understanding the work of each team member, whether in software engineering, design, architecture, product management, marketing or sales,” he explains.
“This broad understanding helps me position the team for success, but it’s equally important to be highly collaborative and empower others to shine, removing obstacles or making decisions when necessary.”
Fabien Duboeuf, an Industry Manager at Google Cloud, focuses on cloud, AI and data analytics initiatives in manufacturing. Raised in France in a multi-generational family with ties to manufacturing, Fabien began his career as a research scientist. As he studied bacteria and enzymes across Europe and the US, he developed a strategic approach to problem-solving.
After fifteen years driving digital transformation at Autodesk, Elsevier and Siemens, Fabien now does the same for manufacturers at Google Cloud.
“I find great fulfillment in guiding businesses through transformations that could disrupt industries,” he says. “I encourage our teams to understand customers' needs at all levels, making us trusted advisors who can collaborate to reach successful outcomes.”
Both Brandon and Fabien are united in their strong belief in AI’s potential to transform manufacturing — a belief backed up by data.
AI: Unrealised potential due to data silos
Google Cloud’sROI of Gen AI for Manufacturing & Auto report reveals that nearly two-thirds of manufacturers are deploying generative AI, highlighting the industry’s ongoing surge in AI adoption.
“It signifies a powerful shift in the industry, driven by a belief in AI’s ability to transform operations,” Fabien says. “AI can improve safety, enhance employee and customer experiences, develop new business models and unlock efficiency and growth.”
Brandon believes AI’s impact on manufacturing will be profound, arguably more so than other sectors. Despite this, acknowledges the adoption process has been slow due to insufficient digital foundations.
“The manufacturing ecosystem is diverse and siloed, with different OT and IT providers, making it challenging to aggregate data,” he explains. “But thanks to innovations like those we’ve developed with Google Cloud, we are beginning to realise AI’s full potential.”
Fabien concurs, noting that AI adoption in manufacturing lags behind other sectors like financial services, where AI has been used for fraud detection for over a decade. However, as AI initiatives gain traction, manufacturers are rapidly seeing benefits including greater efficiency, quality and safety.
“Now that AI is taking hold in manufacturing, its value is evident in improved product quality, more efficient production, and fewer resources required,” Brandon says. “The real excitement comes from connecting the entire manufacturing process, from raw materials to the final product.”
Brandon sees AI as a tool to connect manufacturing workflows from end to end, enabling real-time data access and analysis to optimise processes.
“We’ve implemented point solutions, such as computer vision AI for visual inspection, to detect defects in production,” he says. “This can then be extended to root cause analysis by adding traditional manufacturing data. Once we identify root causes, we can create alerts or automation to prevent defects.”
This level of automation, Brandon suggests, will transform the industry in the near future.
“These solutions don’t replace humans, they augment their roles by providing new upskilling and enabling data-driven decisions,” he adds. “This is already happening, and the industry is starting to see the benefits.”
Despite the promise of AI, the adoption process has been slow due to a myriad of factors- including of course, the dreaded problem of data siloing.
“Manufacturing generates vast amounts of data that’s crucial for process automation, but connecting and ‘right-sizing’ that data for AI use cases has always been challenging,” Brandon explains.
Fabien adds: “Factories rely on a mix of old and new systems that produce data in different formats, preventing a clear view of operations. This leads to inefficiencies and hampers productivity.”
To overcome these challenges, Fabian argues that integrating AI with existing systems, such as CRMs, ERPs, PLMs and MES is critical. AI can enhance these systems by providing more flexibility and speed, enabling manufacturers to make better decisions and operate more effectively.
Since 2019, GFT and Google Cloud have partnered to do exactly this, addressing the issue of data siloing and freeing manufacturers up to holistically embrace AI.
The Manufacturing Data Engine
“We’ve worked together to provide manufacturers with the digital infrastructure to centralise their data, making it accessible for AI applications across the production lifecycle,” Fabian says.
“One example is the Manufacturing Data Engine (MDE), a cloud platform that stores and analyses data from factory machines, enabling faster and more efficient operations.”
MDE aggregates data from various factory sources, processes it and makes it accessible for analysis and AI applications like predictive maintenance and quality control. It's a centralised hub that integrates seamlessly with Google Cloud’s AI and machine learning tools to optimise production and drive innovation.
“Manufacturers can now visualise data trends, identify bottlenecks, and make informed decisions more quickly,” he continues. “MDE allows companies to use AI models for predictive maintenance, quality control, and other applications to optimise production.”
The partnership between GFT and Google has been key in implementing MDE for clients like Ford Motor Co. GFT’s role has been to help design the architecture and deploy tools for predictive maintenance, visual inspection, and robotics on top of the data platform.
“Our work with Google Cloud provides manufacturers with the infrastructure to house their data and make it actionable,” says Brandon. “We’ve built tools that help manufacturers improve efficiency and product quality using this data.”
The effectiveness of MDE has been demonstrated at the MXD Experience Centre, showcasing how manufacturers can improve production, equipment management and quality control through AI-driven insights.
In addition to MDE, GFT has worked with manufacturers like Fehrer to deliver AI-powered visual inspection tools.
“AI helps scale visual inspection by automating the process of detecting defects in assembly lines, where manual inspection can be time-consuming and prone to error,” Brandon says.
“With Google Cloud’s help, we implemented a solution for Fehrer in just three months, improving production quality and efficiency.”
AI adoption despite solutions such as MDE still remains daunting for many manufacturers. It will be less daunting however if you heed Brandon and Fabian’s advice.
Advice on AI
“Don’t rush implementation,” Brandon suggests. “Start small, take an incremental approach and showcase value step by step. It’s an evolutionary process, not a revolution.”
Fabien agrees, saying: “Introduce AI gradually to minimise disruption. Allow employees time to adapt and provide support to help them optimise new tools.”
Flexibility is crucial during AI adoption, according to Fabian: “Be ready to pivot based on feedback, and don’t try to change everything at once. Start with technologies that integrate well with existing systems.”
Brandon emphasises in this vein that AI should be seen as a tool to augment human roles, not replace them: “AI should create a safer work environment and enhance workflows,” he says.
Ultimately both leaders encourage manufacturers to embrace AI’s potential, focusing on its potential rather than its limitations.
“Be bold, experiment, and know that the only limit to AI is your imagination,” Brandon concludes.
To read the full article in the magazine, click HERE.
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