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SetupTimeTracking¤

Track and analyze setup/changeover durations for SMED analysis.

Module: ts_shape.events.production.setup_time_tracking Guide: Reporting Guide

When to Use¤

Use for SMED (Single-Minute Exchange of Dies) analysis. Tracks setup durations and analyzes them by product transition to identify which changeovers take longest. Essential for lean manufacturing initiatives aimed at reducing changeover time and increasing available production time.

Quick Example¤

from ts_shape.events.production.setup_time_tracking import SetupTimeTracking

tracker = SetupTimeTracking(
    df=production_df,
    start_time="2024-01-01",
    end_time="2024-03-31"
)

# List every setup event with duration
setups = tracker.setup_durations(state_uuid="machine-state-001")

# Setup statistics grouped by product transition
by_product = tracker.setup_by_product(
    state_uuid="machine-state-001",
    part_id_uuid="part-id-001"
)

# Overall setup time statistics
stats = tracker.setup_statistics(state_uuid="machine-state-001")

# Weekly trend of setup durations
trend = tracker.setup_trend(state_uuid="machine-state-001", window="1W")

Key Methods¤

Method Purpose Returns
setup_durations(state_uuid) List every setup event with start, end, and duration DataFrame
setup_by_product(state_uuid, part_id_uuid) Setup time statistics grouped by product transition pair DataFrame
setup_statistics(state_uuid) Overall setup time statistics (mean, median, std, total) DataFrame
setup_trend(state_uuid, window='1W') Rolling trend of setup durations over time DataFrame

Tips & Notes¤

Separate internal and external setup tasks

SMED distinguishes between internal setup (machine stopped) and external setup (done while running). If your state signal encodes both, filter for internal-only to get the true changeover impact.

Related modules

See Also¤