Unlocking the full value of industrial AI for all users
In today’s Digital Transformation landscape, technologies like the comprehensive Digital Twin and artificial intelligence (AI) have a valuable impact on industry, especially for end users. But industrial AI is not a one-size-fits-all tool. It creates the most value when it is optimized for each user, task and stage of the lifecycle.
Despite these complexities, by leveraging industrial AI, end users can reduce the time it takes to get the answers they need. And for companies, these tools not only save money; but also attract new users, open opportunities for new revenue streams and create a more connected feedback loop across the business.
In this Industry Forward Podcast episode, John Nixon, global vice president of process industries, and Bill Hahn, director of solutions consulting at Siemens Digital Industries Software, discuss the impact of digitalization on process industries. Their conversation focuses on industrial AI’s role in helping users find, understand and act on information throughout a product’s lifecycle.
The role of AI and simulation tools in a product’s lifecycle
Industrial AI is not a one-size-fits-all solution because each user needs different information at various points in the product lifecycle. A researcher, product developer and manufacturing team may all rely on different data, tools and technologies as they move from lab work to pilot production and, ultimately, to a full-sized commercial plant. That is why industrial AI, the comprehensive Digital Twin and simulation must be continually optimized to anticipate what users need at each stage. As companies scale advanced simulation tools and processes, data must flow seamlessly through the digital thread, from the lab to the shop floor to manufacturing, while preserving the context required to make that data useful.
That flow is only valuable if the data carries the right context with it. Across the lifecycle, data must support the comprehensive Digital Twin and physics-based analysis at each stage while still reflecting the differences between research, development and manufacturing. With that context intact, each user can better understand what the data means and how it should inform the next decision.
Looking at the past to appreciate the future
To understand where organizations are heading, it is helpful to look at the past through the lens of what is known today. During the discussion, Nixon reflected on multiple events in his career where industrial AI and the comprehensive Digital Twin would have provided a significant advantage. One example comes from his time in the military as a facilities engineer, when his team needed to work through an overwhelming amount of federal and state regulations and laws to understand its environmental footprint. That process took weeks of reading, extracting and analyzing. If Nixon had had industrial AI at the time, the work could have taken minutes instead of weeks.
One of the greatest values of the comprehensive Digital Twin is the ability for users to understand everything from initial concepts to directly interacting with the finalized asset; to understand the challenges or opportunities. Imagine if all the data from years of runs could be fed to industrial AI, then the AI could identify what areas may need a physics-based analysis or could create potential bottlenecks. This capability can take some of the guesswork out of making decisions and detecting potential future issues. Industrial AI and the comprehensive Digital Twin help users be more effective and efficient.
AI agents improve efficiency and add value
Previously, parts of the product lifecycle were more separated and disconnected. If users did not know something, they had to call someone who did. Now, with industrial AI, operations or plant agents can quickly answer questions without requiring users to wait for a person to answer or return a call; they can simply ask the agent what is happening at the plant. Thus, knowledge becomes more connected and more readily available across the organization.
This efficiency can also extend beyond the plant, with customers being able to promptly provide feedback. Since customers can leave comments and ratings on products with just a few clicks, companies can get feedback instantaneously, analyze it and explore data points in more detail if necessary. Additionally, with industrial AI, users can more easily sift through the data and make solid conclusions. Using these AI tools reduces the feedback loop, improves the scope and accuracy of the data AI agents use, closes the gap between customers and manufacturers, and adds value for the end customer.
By connecting knowledge, shortening feedback loops and helping users make more contextualized decisions, industrial AI can help process companies turn complex lifecycle data into practical value. Learn more about how industrial AI, the comprehensive Digital Twin and digitalization impact process industries on The Industry Forward Podcast.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
