Summary:

In this edition of Factory Trends, we explore how Explainable AI (XAI) is helping manufacturers build trust and improve efficiency across key workflows. From decision transparency to predictive maintenance, XAI aligns with lean manufacturing principles by making AI more understandable, reliable, and usable on the shop floor.

We share DAFO’s approach to implementing XAI in digital assistants—using visual validation to clarify system feedback before deployment. Finally, we highlight real-world trends showing how XAI reduces errors, improves training, and supports smarter energy and maintenance strategies across the industry.

The Demand for Precision and Trust in AI:

In conversations with IT and production managers across the manufacturing sector, one message is clear: even a 0.1% error rate is unacceptable. In high-stakes environments, uncertainty is not an option. If a digital tool introduces doubt or risk, decision-makers will simply avoid implementing it.

Modern manufacturers are increasingly cautious about AI solutions, citing concerns such as cybersecurity, data ownership, and—perhaps most notably—the opacity of AI decision-making.

As one prospect told us:

“We don’t implement anything with AI on the production line because ChatGPT gives wrong answers. We can’t risk a product skipping validation because of a misunderstood suggestion.”

To address these concerns, AI systems must not only be accurate, but also explainable and trustworthy—providing transparency behind every recommendation and decision. That’s where Explainable AI (XAI) becomes essential.

The Challenge of Precision in Computer Vision Systems:

Why do errors occur in AI systems, and how can understanding them help us correct and improve performance?

As AI technology evolves rapidly, sometimes faster than our ability to fully grasp how it works, a new set of tools has emerged: Explainable AI (XAI). These tools help us better understand what AI sees and why it makes certain decisions.

As Gabriel described it:

“Perceive the cup as the computer does, in a manner akin to how a doctor deciphers an X-ray and gives a diagnostic.”

Understanding how an AI system interprets an image or a process is key—not only to reduce errors, but to build trust and allow operators and managers to work with confidence.

What Is Explainable AI (XAI), and How Does It Support Lean Manufacturing?

Explainable AI (XAI) refers to a set of methods that help us understand how AI systems make decisions. In manufacturing, this is especially important—because quality, speed, and precision matter at every step.

XAI supports key lean manufacturing principles by making sure that AI works transparently and helps improve processes without adding uncertainty.

Here’s how it contributes:

• Accountability: XAI shows that AI decisions are based on clear logic, which is crucial when working under strict regulations.

• Problem Solving: It helps identify and fix errors faster, which is essential to keep operations running smoothly.

• Quality Control: By providing early visibility into issues, XAI supports the principle of quality at the source—catching problems before they grow.

• Building Trust: When teams can understand how AI works, they’re more likely to use it confidently and effectively.

In short, integrating XAI into your digital tools can bring more clarity and reliability to everyday decisions—making lean manufacturing truly smarter.

Our Approach

At DAFO, we use Explainable AI (XAI) to help users understand and trust the feedback from our digital assistant.

For example, in the Operator Guidance we’ve introduced a validation step that allows teams to check how the system interprets tasks before it’s used on the production line. This helps trainers and operators see what the system “sees,” making it easier to catch and correct issues like false positives or false negatives.

This approach doesn’t just increase confidence—it also improves how the model performs over time. The better we understand what the AI is doing, the better we can fine-tune it.

By making the system more transparent, we help build trust between people and technology—so teams feel supported, not replaced.

Increasing Trust and Improving the Model

Seeing how the system interprets data is essential for building trust. When users understand what the AI “sees,” they’re more confident in its decisions—and that confidence leads to better performance overall.

This not only helps detect and correct errors like bias or misclassifications, but also improves the model’s accuracy over time.

While large language models (LLMs) and AI systems can be very precise, they still face challenges like the need for significant computing power and slower response times. XAI helps address these concerns by offering clarity at a technical level—making it easier to catch false positives and prepare the system for real-world use.

By improving visibility, XAI speeds up adoption and supports smoother deployment across production environments.

Trends in Explainable AI: Smarter Maintenance

Explainable AI (XAI) is making a strong impact in manufacturing—especially in predictive maintenance.

By using advanced models like gradient-boosted decision trees combined with XAI techniques, manufacturers can now predict equipment failures before they happen. This helps schedule maintenance at the right time, avoiding unexpected downtime and saving operational costs.

Instead of reacting to issues, teams can act proactively—optimising schedules, extending equipment life, and improving overall efficiency. The ability to understand why a model predicts a fault also helps build trust in the system, encouraging wider adoption across operations.

Energy and Cost Insights for Smarter Production

In sectors like steel manufacturing, XAI is also being used to predict power consumption and production costs for customised products.

By applying machine learning models supported by explainability techniques, teams can forecast energy use more accurately—resulting in more sustainable and cost-effective operations.

This level of transparency not only supports day-to-day decisions, but also enables better long-term planning, resource allocation, and confidence when quoting or adjusting production strategies.

In short, XAI is helping manufacturers make data-driven decisions that are both smarter and more responsible.

Read the complete research here:

(PDF) Explainable AI in Manufacturing: A Predictive Maintenance Case Study (researchgate.net)

Conclusion: Smarter Work Through Transparency

Integrating Explainable AI (XAI) into manufacturing workflows opens the door to more efficient, transparent, and trustworthy processes.

In a world where precision and reliability are essential, XAI gives teams the tools to understand, adjust, and fully trust the technology they’re using. Whether it’s reducing downtime, improving product quality, or supporting better decision-making, XAI helps bridge the gap between human expertise and automated systems.

At DAFO, we’re committed to building AI tools that support—not replace—people. We believe in using technology to enhance human potential, and XAI plays a key role in making that collaboration possible.

We invite you to stay connected as we continue exploring the future of smart, responsible manufacturing. Your feedback, questions, and insights are always welcome.

Originally authored by:
Daniela Gonzalez – Founder of DAFO AI, generalist committed to advancing sustainable technology for a safer, more efficient world
Gabriel Giani – AI Specialist and Code Captain

Updated by the DAFO AI Team – July 2025
This article was revised to reflect DAFO’s expanded focus on intelligent workflows and broader manufacturing efficiency beyond assembly lines.