#2: Jidoka as The Precursor to AI - Keeping Automation Human at Its Core

The blog series on Leveraging TPS/Lean for Transformation in the Digital Era continues by examining how digital tools can enhance TPS/Lean, driving significant transformation. I will use TPS and Lean as interchangeable terms, concentrating on their contributions to value creation, problem-solving, and continuous improvement in today's digital landscape.

In my prior blog, Keeping TPS/Lean Human-Centric, I highlighted the importance of AI and digital tools as empowering resources for people rather than replacements.

This blog will examine Jidoka, Toyota’s fundamental principle of ensuring quality through human-centered automation, and will highlight its growing importance in today's landscape of artificial intelligence and machine learning.

Artificial Intelligence (AI) and Machine Learning (ML) Meets Jidoka

AI and automation are transforming manufacturing—but can they preserve Jidoka’s human touch?

Let’s explore how Machine Learning (ML) can enhance Toyota’s concept of Jidoka (automation with a human touch) by predicting inefficiencies, preventing defects, and driving continuous improvement, without losing the human element that makes Lean so effective.

Problem-solving in traditional manufacturing has often been reactive, with teams providing temporary “fixes” that lead to waste, downtime, and costly rework.

In contrast, Lean plants emphasize quality by utilizing Jidoka to tackle issues at the source. This approach prevents problems from moving downstream to subsequent processes by employing structured problem-solving methods, addressing root causes, and implementing effective countermeasures.

But what if inefficiencies could be predicted and prevented before they even occur?

AI-driven predictive maintenance and analytics bring a new dimension to Jidoka.  This concept isn’t new, as technology may have evolved, but the core principle of Jidoka remains the same.

Over 100 Years Ago, Toyota Understood Automation with The Human Aspect Built In

Toyoda Type G Automatic Loom

In 1924, Sakichi Toyoda, the founder of the Toyota Group, transformed manufacturing with the Type G Automatic Loom.  This innovative machine wove thread into fabric and detected broken threads at their source, stopping itself to prevent defects from escalating. As a result, operators could focus on value-added improvements rather than firefighting issues downstream.

By automating defect detection, operators were freed from the need to monitor a single machine at all times, allowing them to operate and oversee multiple machines more effectively while minimizing fabric defects and rework. This innovation truly embodied the essence of the Jidoka concept, an essential pillar of the Toyota Production System (TPS), which emphasizes building quality into the manufacturing process rather than inspecting it afterward.

Fast forward to today, ML is elevating Jidoka by enabling real-time anomaly detection, predictive maintenance, and adaptive process controls. These advanced systems detect defects by analyzing extensive data sets and predict and prevent them, ensuring increased efficiency while maintaining human oversight.

In this way, ML does not replace Jidoka; it enhances it, furthering Toyota's vision of intelligent automation enriched with a human element. Imagine automatic looms that can assess fabric quality instantaneously, using machine learning algorithms to evaluate equipment conditions and adjust tension and speed as necessary to improve fabric quality, all while keeping human supervision central.

Incorporating ML into Jidoka minimizes rework, reduces waste, and improves efficiency, delivering reliable, high-quality products on time. The result? Lower costs, greater customer satisfaction, and a stronger competitive edge.

However, over-reliance on AI and ML may weaken process ownership and problem-solving skills, hindering continuous improvement efforts. While predictive analytics assist in identifying risks, human insight remains essential for making decisions that drive continuous improvement efforts.

Machines Are Learning, But What About Humans?

As machines grow increasingly complex, removing the "black box" effect through digitization and establishing feedback loops enhances the Jidoka concept. This enables machines to autonomously identify and stop defects at the source, learn from data, and provide insights, resulting in a more transparent and adaptable system.

Yet, the human touch is critical in Jidoka. Humans provide context, manage complex situations that ML models may not fully understand, make final decisions in ambiguous cases, and refine systems for improvement. By merging machine intelligence with human oversight, we ensure a balanced, efficient, and continually evolving process.

Organizations must implement AI and ML with clear guidelines to enhance visibility, support problem-solving, and uphold process ownership at every level. By establishing clear boundaries and promoting collaboration, AI/ML can drive continuous improvement while ensuring that ownership and expertise remain core to the process.

Here’s a breakdown of the roles between ML and People:

This balance ensures AI/ML enhances visibility and efficiency while people retain ownership and expertise, fostering continuous improvement and human-centered automation.

Biotech and Pharma Example

Implemented Electronic Batch Records (EBRs) combined with real-time dashboards, significantly reducing human errors, enhancing Good Documentation Practices, and improving batch tracking to embed quality and compliance into processes. This approach gave staff immediate insights into batch progress, leading to enhanced quality control and reduced waste.

Summary

Integrating ML with Jidoka enhances automation while ensuring that human insight and problem-solving continue to drive quality and continuous improvement. With clearly defined roles, where ML drives efficiency and humans provide context, make decisions, and implement improvements, we can leverage the strengths of both worlds.

As AI and Machine Learning evolve, they should complement human expertise, not replace it. Jidoka reminds us that automation is most effective when it empowers people instead of overshadowing them.

What challenges have you encountered balancing automation and human input in your processes?

In what ways do you believe AI can elevate human decision-making in business or operations?

Your insights and perspectives are valuable—please share your thoughts in the comments below.

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