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CycleTimeTracking¤

Track cycle times by part number from part-ID and cycle-trigger signals.

Module: ts_shape.events.production.cycle_time_tracking Guide: Production Monitoring

When to Use¤

Use to track individual cycle durations and identify slow cycles. Essential for performance analysis in OEE and for detecting tool wear or process drift. When cycle times gradually increase over a shift, it often indicates tool degradation or thermal effects that need attention.

Quick Example¤

from ts_shape.events.production.cycle_time_tracking import CycleTimeTracking
import pandas as pd
import numpy as np

np.random.seed(42)
cycle_times = np.random.normal(loc=55, scale=3, size=200)
cycle_times[50:55] = 90  # simulate slow cycles
df = pd.DataFrame({
    "timestamp": pd.date_range("2024-01-01 06:00", periods=200, freq="1min"),
    "part_id": ["PN-100"]*100 + ["PN-200"]*100,
    "cycle_trigger": cycle_times,
})

ct = CycleTimeTracking(df, timestamp_column="timestamp")
by_part = ct.cycle_time_by_part(part_id_uuid="part_id", cycle_trigger_uuid="cycle_trigger")
stats = ct.cycle_time_statistics(part_id_uuid="part_id", cycle_trigger_uuid="cycle_trigger")
slow = ct.detect_slow_cycles(
    part_id_uuid="part_id", cycle_trigger_uuid="cycle_trigger", threshold_factor=1.5
)
print(stats)

Key Methods¤

Method Purpose Returns
cycle_time_by_part(part_id_uuid, cycle_trigger_uuid) Compute cycle time for each part number from trigger signal intervals DataFrame with timestamp, part_id, and cycle_time
cycle_time_statistics(part_id_uuid, cycle_trigger_uuid) Calculate min, avg, max, and std deviation of cycle time per part number DataFrame with part_id and statistical columns
detect_slow_cycles(part_id_uuid, cycle_trigger_uuid, threshold_factor=1.5) Flag cycles exceeding a multiple of the average cycle time for that part DataFrame of slow cycle events with timestamp and overshoot
cycle_time_trend(part_id_uuid, cycle_trigger_uuid, part_number) Analyze cycle time trend for a specific part number over time DataFrame with timestamp and rolling average cycle time
hourly_cycle_time_summary(part_id_uuid, cycle_trigger_uuid) Summarize cycle times per hour per part for shift review dashboards DataFrame with hour, part_id, and summary statistics

Tips & Notes¤

Use threshold_factor relative to each part

The detect_slow_cycles method computes the threshold per part number, so a 1.5x factor means 1.5 times the average for that specific SKU — no need to hard-code absolute limits for each part.

Related modules

See Also¤