Poke Yoke in Manufacturing: How AI Vision Helps Mistake Proofing and Error Prevention
Outline：
- Introduction
- Definition
- Core Concepts
- Importance to Manufacturing
- Bottleneck in Designing Poka-Yoke Mechanisms
- Upgrading Tools
- Applications
- Success Story
- HOP (Human Operation Platform)
The concept of error prevention or Poka Yoke, originates from Toyota, aimed at reducing the risk of human errors during the production process.
Poka Yoke in and of itself is combined by mistake proofing and error prevention. Mistake proofing relies on tools and designs to fundamentally prevent errors from happening. Error prevention, on the other hand, steps in when mistake proofing fail to block mistakes. Preventing further escalation with smart tools issuing immediate alerts as errors occur within production.
If Poka Yoke is implemented effectively during the manufacturing process, many quality control tasks can even be eliminated.
Introduction
By establishing an effective Poka Yoke mechanism, companies can prevent potential issues in the manufacturing process, preventing defective products from reaching the market and eliminating design flaws at the source.
With the rapid advancement of AI technology, AI vision has become a key mistake proofing tool in manufacturing due to its real-time capabilities, flexibility, and irreplaceability. It significantly enhances the precision and efficiency of production processes.
How does AI vision improve Poka Yoke?
What is Mistake Proofing and Error Prevention?
Error Prevention: This involves using jigs, fixtures, or other aids to prevent errors from happening at the design stage.
For example, to ensure an operator assembles the parts correctly. A cover was designed to block non-targeted interfaces or the use connectors with anti-reverse features can also eliminate the possibility of incorrect assembly at it’s source.
Mistake Proofing: When production design can’t prevent certain errors, process-level interventions is put into place to stop errors from escalating or progressing further.
During assembling such as screwing and washing, error prevention aren’t always the most optimal method for operators to follow assembly guidelines, especially in cases of stacked components. However, implementing mistake proofing measures during the process can help detect and address errors as they occur.
How Does AI Vision Help Mistake Proofing and Error Prevention in Manufacturing Processes?
The Core of Poka Yoke
- Eliminate Human Errors from the Source
- Improve Process Stability
- Increase Yield Rate
The Importance of Poka Yoke in Manufacturing
In manufacturing, yield rate is the key indicator directly impacts market competitiveness and operational performance. To improve yield rate, the primary task is to implement both mistake-proofing and error prevention throughout the production process, ensuring that every step is executed correctly.
Common causes of error on the production line including equipment or material malfunctions, tool wear, and human error.
Amongst these, human factors are the most challenging to manage and track.
Hidden Human Errors on the Production Line
The fact that manufacturing these days are still heavily relying on manual labors. Factors such as oversight in supervising, physical conditions, and unpredictability of operators are only a few overwhelming challenges on quality controls.
The Benefits of IPQC
IPQC (In Process Quality Control) focuses on controlling quality at each step of the production process, detecting and correcting errors early to prevent them from escalating. By quickly resolving quality issues during production, companies can minimize rework and waste, while preventing defective products from reaching the market. This not only protects a company’s reputation but also significantly reduces the high costs associated with product defects.
An effective Poka Yoke mechanism can greatly enhance IPQC efforts
- Improvement in FPY: First Pass Yield, FPY refers to the number of products that pass inspection and testing on the first attempt without needing rework, modifications, or repairs, meeting quality standards. Proper operator training and adherence to manufacturing standards can effectively improve FPY.
With AI Vision, errors in the process can be prevented, significantly boosting FPY.
- Improvement in Yield RateYield Rate refers to the proportion of qualified products produced compared to the total number of products manufactured. A higher yield rate indicates a higher percentage of defect-free products on the production line, which is directly related to cost reduction, efficiency gains, and increased market share.
AI Vision helps Poka Yoke and effectively helps the improvement of Yield Rate.
Bottleneck in Designing Poka-Yoke Mechanisms: ‘Invisibility’ of Production Status
Before establishing a Poka-Yoke mechanism, accurately understanding issues on the production line is essential.
However, traditional tools used for process tracking often fail to provide production engineers with full visibility into the manufacturing process, making it difficult to resolve problems effectively.
Imagine an electric scooter assembly line where two operators work together at a dual station to complete assembly tasks. Even with the MES system, it only assesses each operator’s steps and cannot analyze the entire collaborative process. As a result, when errors are detected in the production process, engineers cannot quickly determine whether the issue lies within tools, materials, or operational workflow.
Traditional systems lack video records, leaving engineers to rely only on data, unable to pinpoint when or how the issue occurred.
Implementing tools with high transparency, capable of systematically tracing and tracking production status, is a critical step in effectively reducing errors.
Also, we recognize that not every production process can be preemptively protected by designing a mistake-proofing mechanism.
It’s impossible to eliminate all errors from the process. Therefore, tools that assist in mistake proofing become even more crucial.
How to Make Poka-Yoke Tools Better?
Engineers used to rely on post-production data. They usually found problems after the errors escalated, causing rework or customer complaints. Real-time Poka-Yoke can solve this problem. It helps engineer correct mistake in the process.
Traditional tools fail to track how workers follow SOPs. Without tracking SOP compliance, data cannot provide full insights for the process improvements.
As production gets more complex—with operators working together or production lines changing, current tools cannot quickly find and fix problems. As a result, mistake-proofing becomes difficult.
AI vision technology solves these problems. It gives real-time insights to catch errors early and keep processes running smoothly.
AI Vision Powered Poka Yoke Tools
Real-Time Capability
Since we started using AI vision, early warranty issues from the market have dropped to zero.
AI vision systems use cameras on production lines to record the process and ensure operators follow the SOPs in real time.
It catches and analyzes abnormal production steps.
For operators, it provides real-time mistake proofing. The workstation stops right away and alerts management when errors are detected. Therefore, the defects would not go to the next stage. For managers, visual reports make it easy for them to see the root causes. They can decide whether to release the workstation or solve the issues on the line.
Irreplaceability
AI vision identifies collaborative behaviors and analyzes production footage. It transforms unstructured data into structured analysis—something traditional tracking tools cannot do.
It helps production engineers track, review, and analyze SOPs.
On the management platform, engineers can easily access flagged workstations and stop causes. If needed, managers can playback the video to review production conditions.
With AI vision, managers get full visibility into production, with all details recorded. The data enables future process improvements and better Poka Yoke designs.
Flexibility
How AI Vision work together with other Quality Control Tools
With the rise of digital transformation, many factories now use IoT devices, including MES, to enhance quality control.
However, system integration is a challenge.
AI vision integrates seamlessly with other quality control systems and strengthens Poka Yoke mechanisms.
Moreover, AI vision won’t affect the current production processes. Designing Poka Yoke mechanisms becomes easier, and optimizing overall product quality becomes faster.
AI vision adjusts its detection models to match material changes or quality updates. This makes production line operations more flexible.
AI vision fulfills the real-time Poka Yoke in the process. This eliminates the need for extra quality control checkpoints, helping factories save labor and cut costs.
Success Story
A well-known scooter brand has been working on deploying a smart factory in recent years. Initially, they implemented a mistaking proofing mechanism using an MES system and AGVs. When the MES system didn’t receive the required process parameters, the AGV wouldn’t proceed, preventing errors from moving to the next assembly stage.
However, as production increased, the factory found that early warranty issues persisted. The main issue was that the MES system couldn’t trace the manual assembly process, and failures to properly execute SOPs still occurred.
Even with digital SOPs, we are not sure if workers follow them.
AI vision systems became the only solution to fulfill Poka Yoke, filling the gap by providing missing production records at key quality checkpoints.
AI Vision Powered Poka Yoke: HOP (Human Operation Platform)
“Of 1,800 senior manufacturing executives, 89% view AI as extremely important and plan to implement it in production, but only 16% have met their AI-related goals.”
In the wave of smart manufacturing, companies are eager to enhance competitiveness through AI technology.
The real challenge, however, is ensuring that AI implementation isn’t just a trend but brings tangible benefits to operations and product performance in the market. Companies need to choose technologies that address core issues.
AI vision-driven HOP is a solution.
By introducing AI vision technology on production lines, companies can improve product yield, leading to clear ROI benefits in terms of cost, competitiveness, and market share.
