Artificial intelligence (AI) continues to prove itself as a powerful tool that’s helping transform manufacturing. As we discussed in part one, traditional AI and generative AI (GenAI) are similar in a lot of ways but also provide manufacturers with tools that are each designed to achieve distinct objectives, outputs and results.
Here, we take a more in-depth look at how traditional AI and GenAI work together to provide operators and maintenance personnel with exactly what they need when they need it. We also highlight the added value these tools bring to digital twins, data analytics, sustainability, productivity and quality control.
## Asset Maintenance
Traditional AI has been a valuable tool in asset maintenance for many years. It is typically trained on large amounts of historical data from manufacturing assets and can detect patterns in real-time data. These patterns help operators and maintenance personnel identify and resolve potential problems before they occur.
AI makes specific predictions based on real-time data so that maintenance activities can be performed at the appropriate time. For years, traditional AI has been one of the key technologies supporting preventive, predictive, prescriptive and condition-based maintenance.
But traditional AI results can be difficult for operators and maintenance teams to interpret and act on. That’s where GenAI comes in.
GenAI works alongside traditional AI by helping to interpret the data results and pattern analyses. GenAI takes the results and summarizes them into easily understandable terms for the operators and maintenance teams. The output gives operators clear details on what’s wrong and how to fix it.
GenAI digests operating manuals, repair manuals, technical drawings and other equipment-related materials. Using the specific patterns identified by traditional AI, it generates contextualized content—such as detailed drawings and step-by-step maintenance instructions—that clearly explain to operators and maintenance personnel what’s happening and help guide them on the appropriate corrective actions.
This is a great example of traditional AI and GenAI working together to provide operators and maintenance personnel with exactly what they need when they need it.
## Digital Twins
A digital twin is a working, computer-based replica of a physical manufacturing asset, manufacturing line or plant. Its purpose is to mirror the real-world operations as accurately as possible. Digital twins are often dynamic real-time models and are especially valuable in the design, operation and maintenance of manufacturing equipment. Their impact becomes even greater when they incorporate both traditional AI and GenAI.
When it comes to manufacturing operations and training, digital twins help optimize equipment and production line operations and support workforce training. When paired with traditional AI and GenAI, they become powerful tools for enabling real-time decision making.
Traditional AI analyzes equipment data to determine operational inefficiencies. GenAI then interprets these insights, providing clear guidance to operators by referencing engineering drawings, operating procedures and operations manuals.
## Data Analytics
Manufacturing has always been a rich source of data, but with so much data, it’s often difficult to distinguish what’s important from what’s not. Fortunately, traditional AI has made significant progress through supervised and unsupervised learning. Still, the results might be overwhelming for those who aren’t data scientists. It’s like the results need another layer of AI just to be understood.
At this point, GenAI can interpret the results produced by traditional AI and clearly explain them. It can clarify what’s happening, why it’s happening, what’s likely to happen next and what needs to be done so operators, supervisors, managers and engineers can understand and take action.
## Sustainability
Real sustainability, in terms of energy and utility usage and waste and emissions management, can be very difficult. After all, nearly every square inch of a manufacturing facility consumes energy or utilities or produces emissions, waste or all of the above.
Traditional AI takes in this vast amount of data to optimize the energy and utility usage while minimizing emissions and waste, all in real time. But even with these optimizations, understanding what’s going on can be difficult. There’s simply too much data and too many decisions being made for most people to quickly and easily grasp.
GenAI bridges that gap. It translates the traditional AI outputs into clear, actionable insights, helping operators and managers understand what’s happening, what to do, what decisions are being made and how those decisions impact energy use, utilities, emissions and waste. It delivers digestible summaries that help them understand the big picture to take informed action.
## Productivity And Quality Control
Quality control in manufacturing is a complex endeavor. The objective is to make sure that only top-quality items are shipped while keeping the facility running at peak efficiency. Traditionally, manufacturers would either slow down production to improve quality or speed it up and risk a drop in quality.
Now, traditional AI is helping to transform this process. It can perform quality control tests and inspections by ingesting data and detecting data patterns that indicate potential failures. It also looks at the large amounts of data generated by production activities to identify inefficiencies and optimize operations. When these capabilities are combined, AI can simultaneously optimize both quality and productivity. While this requires more data and tuning, the benefits are well worth the effort.
GenAI steps in to analyze these outputs and make them understandable for the operator. For traditional AI, optimizing quality or productivity separately—or both together—can become quite complicated. GenAI helps sort it all out by clearly explaining the results. It provides specific information on what’s going on, why it’s going on and what actions need to be taken to maintain both quality and productivity at the desired levels, without compromising one for the other.
## Conclusion
While traditional AI and GenAI differ in their technologies, operations and objectives, they are far more powerful when used together in manufacturing. Some of the most effective applications are those where both are integrated, delivering significant returns. In today’s manufacturing landscape, companies must be prepared to adapt and evolve. The fusion of traditional AI and GenAI will undoubtedly be a key driver of this transformation.
