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OperatorPerformanceTracking¤

Track and compare operator performance from operator-ID and counter signals.

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

When to Use¤

Use for fair operator performance comparison. Tracks production output, efficiency vs targets, and quality (FPY) per operator or team. Designed for shift leads and production managers who need to identify training needs, recognize top performers, and ensure balanced workload distribution.

Quick Example¤

from ts_shape.events.production.operator_performance import OperatorPerformanceTracking

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

# Parts produced per operator
by_operator = tracker.production_by_operator(
    operator_uuid="operator-badge-001",
    counter_uuid="counter-001"
)

# Efficiency vs target
efficiency = tracker.operator_efficiency(
    operator_uuid="operator-badge-001",
    counter_uuid="counter-001",
    target_per_shift=500
)

# First-pass yield per operator
quality = tracker.quality_by_operator(
    operator_uuid="operator-badge-001",
    ok_uuid="ok-counter-001",
    nok_uuid="nok-counter-001"
)

Key Methods¤

Method Purpose Returns
production_by_operator(operator_uuid, counter_uuid) Total parts produced per operator DataFrame
operator_efficiency(operator_uuid, counter_uuid, target_per_shift) Efficiency percentage vs shift target per operator DataFrame
quality_by_operator(operator_uuid, ok_uuid, nok_uuid) First-pass yield (FPY) per operator DataFrame
operator_comparison(operator_uuid, counter_uuid) Ranked comparison of all operators DataFrame

Tips & Notes¤

Normalize for shift length and product mix

Raw output counts can be misleading if operators work different shift lengths or produce different product variants. Use efficiency metrics with appropriate targets per product type for fair comparisons.

Related modules

See Also¤