Plastic Smoothie Bottles Product - China Manufacturer

Monthly Throughput, Yield, and Fill Accuracy for Plastic Smoothie Bottles

This chart displays three complementary performance metrics for plastic smoothie bottle production over a 12-month period: monthly production throughput (units per day), production yield percentage, and fill accuracy percentage. Throughput measures the average number of bottles produced per day on the primary production line. Production yield represents the share of acceptable bottles after initial inspection and in-line quality checks. Fill accuracy indicates the percentage of bottles that meet the target fill volume within a tight tolerance band. Tracking these metrics together helps operations, quality, and supply chain teams identify trade-offs between speed and consistency.

The dataset shows a seasonal increase in throughput from January to June as demand rises, peaking mid-year, then stabilizing and slightly declining toward December. Yield improves steadily during the first half of the year, reflecting process optimization efforts such as mold maintenance, staff training, and machine calibration. Fill accuracy follows a similar pattern but exhibits smaller fluctuations, suggesting that accuracy benefits from automated filling controls that resist minor throughput swings.

Notably, there are brief periods where throughput spikes coincide with minor dips in yield or accuracy, indicating that pushing equipment beyond optimal rates may increase defect risk. When throughput is reduced to steady levels, both yield and fill accuracy recover, which supports a balanced production policy that prioritizes sustained quality over short-term volume gains. The mid-year peak shows that the team successfully scaled capacity while preserving acceptable quality metrics.

This combined line chart is useful for root cause analysis, capacity planning, and continuous improvement initiatives. Teams can use the insights to schedule preventive maintenance before known high-volume months, tune fill algorithms to maintain accuracy during ramp-ups, and set realistic targets that align throughput targets with quality thresholds. Monitoring these trends over successive years provides stronger evidence for investment decisions in automation, tooling, or staffing and budgeting.