Published
by Bipin Jayaraj, Vice President and Chief Information Officer
Corporate
Artificial Intelligence (AI) has been a buzzword for quite some time now, and it has been transforming industries across the board. This is especially true for Generative AI, a subset of the broader AI umbrella that hit mainstream headlines in 2022 with the launch of ChatGPT — a chatbot capable of very context-sensitive human-like interactions.
As a Technology leader, I am often asked about the topic, its hype, and its realities. It is a standing topic at any event at the Board level, conferences, and earnings calls. The world is embracing it, and there are already many game-changing applications from FinTech to social governance. This is an exciting breakthrough, but some initial wrinkles need to be ironed out, like any other new technology. The failure stories are making headlines, too — the recent Google Gemini fiasco being one of them.
I am going to take a narrower view of our manufacturing industry and talk about some of the benefits that can be reaped from AI and its applications in our world. This is all but a toe dip into the vast ocean to get you thinking of the "art of the possible."
AI has already been used to optimize production processes, reducing costs and improving the quality of the end product.
One of the most significant use cases of generative AI in the manufacturing industry is predictive maintenance. Predictive maintenance is the practice of using data analytics, machine learning, and AI to predict when equipment is likely to fail. By predicting when equipment is likely to fail, manufacturers can schedule maintenance before the equipment breaks down, reducing downtime and increasing productivity. Generative AI can help manufacturers optimize operations by interpreting telemetry from equipment and machines to reduce unplanned downtime, leading to efficiencies. As a next step, if a problem is identified, generative AI can also recommend potential solutions and a service plan to help maintenance teams rectify the issue. This is the real advantage — providing proactive solutions to a potential failure to help us get ready.
Another use case of generative AI in the manufacturing industry is product design and development. Generative AI can help manufacturers create new designs and optimize existing ones. Manufacturers can create designs optimized for specific requirements, such as weight, strength, and durability. Generative AI can also help manufacturers optimize designs for cost and manufacturability. It also can reduce time-to-market cycles by reducing the test cycle timeframes and potentially testing real-life scenarios that were not originally thought of, thereby enhancing product life.
Generative AI can also improve quality control in the manufacturing industry. Generative AI can identify defects and other quality issues by analyzing data from sensors and other sources in real-time. This allows manufacturers to take corrective action before the product is shipped, reducing the number of defective products and improving customer satisfaction. This is a big cost-saver as any manufacturer can vouch for the recall costs and loss of reputation.
Gartner, one of the premier technology research organizations, has made these bold predictions:
As the technology evolves, we can expect to see even more use cases emerge. Integrating AI into the workplace brings many benefits when used properly to enhance value and improve efficiencies. As organizations navigate the evolving business landscape, leveraging AI can be a key differentiator, enabling them to stay agile, innovative, and competitive in the global marketplace. I encourage all who are curious about AI to learn about it and consider how it can improve your daily work and overall organizational performance.
Published on Mar 14, 2024
