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Driving successful manufacturing with data-powered AI

by Staff GBAF Publications Ltd
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Driving successful manufacturing with data-powered AI


Kirsty Biddiscombe, UK Head for AI, ML & Analytics at NetApp

I like to make a comparison of the importance of data to being that of water due to its continuous flow, indispensable nature, and the need for constant treatment. Just as dirty water is rendered unusable, neglected, and discarded, untreated data leads to inefficiency and hampers progress. 

However, when data is handled with care, ensuring it is clean and accessible, it becomes a valuable resource that can be utilized, reused, and allocated effectively in various aspects of everyday life.

Significance of clean and consistent data is particularly crucial for the success of AI projects. Without access to secure and reliable data, firms cannot sustain their AI initiatives. Similar to how water is essential for survival, clean data acts as the lifeblood for successful and healthy AI endeavors.

Unveiling the present reality

At the moment, the landscape of AI is witnessing the emergence of ground-breaking models, leading to a surge in the popularity of AI language models. This development has raised concerns among internet users, prompting speculation about the possibility of an impending android apocalypse. However, the true potential of AI lies in its ability to streamline processes, and simplify our workflows, regardless of the project’s size or scale.

McKinsey’s 2022 State of AI report serves as proof of AI’s growing integration. The report reveals that AI adoption has more than doubled since 2017, with 50% of organizations incorporating AI into at least one area of their business. This adoption has revolutionized industries such as retail and utilities, enabling innovative solutions to meet organizational and customer needs. However, it’s become clear that the manufacturing sector has experienced the most significant impact from AI.

AI-based tools like Machine Learning (ML) and Deep Learning (DL) have facilitated the development of smart factories capable of optimizing complex, multi-stage processes. Automation has empowered manufacturers in a plethora of ways, enhancing the likes of sustainability, efficiency, and cost-effectiveness. Nonetheless, businesses seeking to integrate AI into their manufacturing operations face important questions: where should they begin, and how can they ensure clean data lies at the core of the process?

Data security is the bedrock

Much like you should keep your bank information to yourself, businesses should be careful with keeping their data safe. 

Data security serves as the foundation for high-performing AI solutions in general, but also more specifically in manufacturing – an industry that increasingly relies on accurate datasets. Legacy systems have traditionally placed data storage as a secondary priority, but the digitalization of the manufacturing industry has driven a manual shift in mindset. 

Manufacturing CIOs now recognize the importance of not only collecting and inputting data but also storing it safely and cleanly. This is particularly crucial when safeguarding industry secrets or personal data, which are often under threat of compromise.  Thus, by driving the belief that effective manufacturing applications start with clean and secure data, businesses can successfully plan and execute AI-powered projects.

The Real Enablers: Data & AI

In the manufacturing industry, leaders understand that data management plays a pivotal role in the success or failure of a project. Presently, data scientists spend approximately 80% of their working hours collecting, cleaning, and identifying flawed data, which hampers the creation of actionable insights. 

Just as clean water is vital for sustenance, clean data is also essential for training AI algorithms to make accurate predictions regarding impending plant breakdowns or machine downtime. Training AI algorithms with unclean data can be detrimental, but maintaining better data hygiene enables businesses to seamlessly integrate information into existing software programs. 

Subsequently, organizations can leverage AI to automate processes, leading to enhanced efficiency and productivity. Ultimately, the quality and quantity of data processed determine the success of AI projects in manufacturing—better data yields superior results.

When integrating any technology tools into their tech stack, businesses should seek strategic value and enhanced day-to-day operations. The same principle applies to AI integration. In this instance, they must cover all fronts, starting by considering the costs, challenges, and limitations associated with AI implementation. 

However, with the right approach and the appropriate partner, AI integration can be an optimised way to yield immediate, cost-effective, and sophisticated outcomes. Across manufacturing, its integration may involve introducing intelligent machine maintenance, improving quality control efficiency, enhancing agility in supply chain management, or increasing AI-powered automation for streamlined processes. Through computer vision, for instance, AI can also further improve site safety and risk mitigation. 

Amid hype over artificial intelligence, staying on top of the curve is essential to remain competitive in one’s respective market. This is why leaders must guide their employees in effectively utilising automation as a tool, driving optimal manufacturing activities with data-fuelled AI.