<![CDATA[High-speed beverage bottling lines lose a substantial share of their rated capacity to short, frequent, and easily overlooked disturbances that conventional end-of-shift reporting cannot capture. This paper reports the implementation and evaluation of an Internet of Things (IoT) real-time throughput-monitoring system on an automated bottling line at a beverage packaging plant in Pasuruan, Indonesia. The monitoring layer streams line speed, vibration, motor temperature, and air pressure from four sensor groups, flags micro-stoppages as they occur, and feeds a Pareto-based loss analysis that ranks the plant's chronic production losses by downtime and financial impact. The study analyzes 200 telemetry observations, an 80-event micro-stoppage log, four categorized loss streams, and 30 days of daily throughput records spanning a period before and after the monitoring rollout. Pareto analysis shows that micro-stoppages and speed reduction alone account for 67.9% of the 130.6 downtime hours and dominate the 391.8 million IDR of loss over the study window, identifying them as the vital few losses that warrant priority action. Telemetry profiling confirms that flagged micro-stoppages coincide with elevated vibration and motor temperature, giving operators an early physical signature to act on. After the IoT monitoring system was introduced, mean daily production loss fell from about 45,858 to 8,020 units, mean good output rose from 204,142 to 241,980 units per day, and mean capacity recovery improved from 81.7% to 96.8%, a gain of 15.1 percentage points sustained across the full post-implementation period. The results show that continuous IoT monitoring converts previously invisible micro-losses into actionable signals and delivers a large, measurable throughput improvement, providing a transferable loss-detection template for high-speed packaging operations]]>
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