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ReworkTracking¤

Track parts that require rework from rework counter and reason-code signals.

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

When to Use¤

Use to track rework events and their cost. Identifies which parts and reasons drive the most rework, enabling targeted process improvements. Especially valuable for plants where rework is a significant cost driver and management needs visibility into root causes and financial impact.

Quick Example¤

from ts_shape.events.production.rework_tracking import ReworkTracking

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

# Rework counts per shift
by_shift = tracker.rework_by_shift(rework_uuid="rework-counter-001")

# Rework breakdown by reason code
by_reason = tracker.rework_by_reason(
    rework_uuid="rework-counter-001",
    reason_uuid="reason-code-001"
)

# Rework rate as percentage of total production
rate = tracker.rework_rate(
    rework_uuid="rework-counter-001",
    total_production_uuid="total-counter-001"
)

# Monetary cost of rework by part type
rework_costs = {"part-A": 12.50, "part-B": 8.75, "part-C": 22.00}
cost = tracker.rework_cost(
    rework_uuid="rework-counter-001",
    part_id_uuid="part-id-001",
    rework_costs=rework_costs
)

Key Methods¤

Method Purpose Returns
rework_by_shift(rework_uuid) Rework count per shift DataFrame
rework_by_reason(rework_uuid, reason_uuid) Rework count grouped by reason code DataFrame
rework_rate(rework_uuid, total_production_uuid) Rework rate as percentage of total production DataFrame
rework_cost(rework_uuid, part_id_uuid, rework_costs) Monetary cost of rework by part type DataFrame
rework_trend(rework_uuid, window='1D') Rolling trend of rework counts over time DataFrame

Tips & Notes¤

Combine with Pareto analysis

Sort rework_by_reason results by count descending to build a Pareto chart. Typically 20% of reason codes drive 80% of rework volume, giving you clear improvement targets.

Related modules

See Also¤