performance_loss

performance_loss ¤

Performance/speed loss tracking.

Track when machine runs slower than ideal/target speed: - Identify slow periods vs target cycle time - Calculate performance loss in minutes and parts - Performance trend over time

PerformanceLossTracking ¤

PerformanceLossTracking(
    dataframe: DataFrame,
    *,
    time_column: str = "systime",
    shift_definitions: Optional[
        Dict[str, tuple[str, str]]
    ] = None
)

Bases: Base

Track performance and speed losses against target cycle times.

Identifies hidden losses where the machine is running but slower than it should be. Typically 10-20% of production time is lost to speed losses.

Each UUID represents one signal: - cycle_uuid: cycle trigger or production counter - part_id_uuid: part number signal (optional, for per-part targets)

Merge keys: [date, shift] for shift-level, [period] for trend data.

Pipeline example::

perf = PerformanceLossTracking(df)
shift_perf = perf.performance_by_shift('cycle', target_cycle_time=45)
# → merge with DowntimeTracking.downtime_by_shift() on [date, shift]
# → merge with QualityTracking.nok_by_shift() on [date, shift]
# → feed into ShiftHandoverReport.from_shift_data()

Example usage

tracker = PerformanceLossTracking(df)

Performance by shift¤

perf = tracker.performance_by_shift( cycle_uuid='cycle_trigger', target_cycle_time=45.0, )

Identify slow periods¤

slow = tracker.slow_periods( cycle_uuid='cycle_trigger', target_cycle_time=45.0, threshold_pct=90, )

performance_by_shift ¤

performance_by_shift(
    cycle_uuid: str,
    target_cycle_time: float,
    *,
    value_column: str = "value_integer"
) -> pd.DataFrame

Calculate performance percentage per shift.

Performance = (target_cycle_time * actual_parts) / elapsed_run_time.

Parameters:

Name	Type	Description	Default
`cycle_uuid`	`str`	UUID of cycle trigger or counter signal.	required
`target_cycle_time`	`float`	Target (ideal) cycle time in seconds.	required
`value_column`	`str`	Column with counter values.	`'value_integer'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	date, shift, actual_parts, avg_cycle_time_s, target_cycle_time_s, performance_pct, loss_minutes

slow_periods ¤

slow_periods(
    cycle_uuid: str,
    target_cycle_time: float,
    *,
    threshold_pct: float = 90.0,
    window: str = "1h",
    value_column: str = "value_integer"
) -> pd.DataFrame

Identify time windows where performance is below threshold.

Parameters:

Name	Type	Description	Default
`cycle_uuid`	`str`	UUID of cycle trigger or counter signal.	required
`target_cycle_time`	`float`	Target cycle time in seconds.	required
`threshold_pct`	`float`	Performance must be below this to flag (default 90%).	`90.0`
`window`	`str`	Rolling window size (default '1h').	`'1h'`
`value_column`	`str`	Column with counter values.	`'value_integer'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	window_start, window_end, actual_parts, avg_cycle_time_s, performance_pct, loss_minutes

performance_trend ¤

performance_trend(
    cycle_uuid: str,
    target_cycle_time: float,
    *,
    window: str = "1D",
    value_column: str = "value_integer"
) -> pd.DataFrame

Track performance trend over time.

Parameters:

Name	Type	Description	Default
`cycle_uuid`	`str`	UUID of cycle trigger or counter signal.	required
`target_cycle_time`	`float`	Target cycle time in seconds.	required
`window`	`str`	Time window for aggregation (default '1D').	`'1D'`
`value_column`	`str`	Column with counter values.	`'value_integer'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	period, actual_parts, avg_cycle_time_s, performance_pct, loss_minutes