From Real-time Data to Root Cause — Systematic Loss Time Analysis with IoT

■ Key Takeaways

• Loss Time that is not properly categorized leads to ineffective Kaizen.
• Real-time IoT Data clearly distinguishes Minor, Major, and Chronic Loss.
• Understanding the cause–effect sequence is more important than viewing total loss figures.
• IoT Monitoring is a Root Cause analysis tool, not just a dashboard.

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Many factories experience low OEE or fail to meet production plans but cannot clearly identify where production time is being lost. Analyzing Loss Time using retrospective reports or daily aggregated data shows only results, not causes. Real-time IoT Data is therefore essential for accurate and systematic Loss Time analysis.

■ Problem Framing: Why Loss Time Is Difficult to Solve

When Loss Time is viewed only as a total figure, its linkage to Productivity, Quality, and Energy KPIs becomes unclear. Each team addresses different symptoms, and Kaizen efforts fail to reach the true Root Cause.

■ Root Cause Chain: What Causes Loss Time

Loss Time in production lines can be classified into three main categories:

• Minor Stop Loss: Short but frequent stoppages such as minor jams or repositioning. Often unrecorded, yet they accumulate into significant losses.
• Major Stop Loss: Long stoppages such as machine failures or mold changes. These are visible, but their frequency and recurring patterns are unclear.
• Chronic Loss: Slightly slower-than-standard operation in every cycle. This loss is difficult to detect but has the greatest impact on hourly output.

■ Method: Using IoT to Analyze Loss Time

IoT Monitoring enables real-time visibility of actual production behavior by:

• Capturing actual Cycle Time for every cycle
• Recording Stop Time along with machine status
• Displaying the chronological sequence of run and stop events
• Analyzing duration, frequency, and recurring time patterns

This data reveals clear cause-and-effect relationships.

■ Conventional Method vs IoT Monitoring

• Conventional Method: Manual recording captures only major stoppages. Kaizen is slow and results are difficult to quantify.
• IoT Monitoring: Automatic data collection reveals hidden Minor and Chronic Loss, enabling faster improvement and measurable results.

■ KPIs with Clear Impact

When Loss Time is properly categorized, factories can:

• Improve Availability by reducing hidden stoppages
• Increase hourly output without investing in new equipment
• Reduce energy per unit by minimizing machine idle time

■ Case-like Insight

Analysis of actual production data shows that the losses with the greatest impact on output are not long stoppages, but Chronic Loss recurring throughout production.

■ Implication & Next Step

If Loss Time analysis remains based on aggregated data, Kaizen decisions will be slow and Smart Factory investments may not deliver value. The next step is to link Loss Time with Productivity, Energy, and CO₂ KPIs to achieve sustainable business results.

■ Summary

Understanding Loss Time starts with Real-time Data. IoT Monitoring is not only a visualization tool but a Root Cause analysis mechanism that enables measurable Kaizen and supports Productivity and Carbon Reduction.

To apply this concept to actual production lines, Siam Asahi Manufacturing Co., Ltd.

recommends reviewing the related topics below or contacting the Production Quality Office – IoT directly.

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■ FAQ

• Q: Why is Chronic Loss more dangerous than expected?
• A: Because it occurs in every cycle and accumulates a significant impact on output.
• Q: Can factories without OEE still use this approach?
• A: Yes. IoT Monitoring helps establish accurate OEE data from the beginning.

■ Glossary (Key Terms)

• Loss Time: Production time losses (data not specified)
• Minor Stop Loss: Short but frequent stoppages
• Chronic Loss: Operation slower than the standard cycle
• IoT Monitoring: Real-time production data monitoring system