rework_tracking

rework_tracking

Rework tracking for parts requiring re-processing.

Track rework events and their impact: - Rework count by shift - Rework by reason code - Rework rate as percentage of production - Rework cost estimation - Rework trends over time

ReworkTracking

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

Bases: Base

Track parts that require rework (re-processing through a station).

Each UUID represents one signal: - rework_uuid: rework event counter (monotonic integer) - reason_uuid: rework reason code signal (optional) - total_production_uuid: total production counter (optional) - part_id_uuid: part number / product type signal (optional)

Merge keys: [date, shift] for shift-level, [period] for trend, [reason] for reason-level, [part_number] for part-level.

Pipeline example::

rework = ReworkTracking(df)
shift_rework = rework.rework_by_shift('rework_counter')
# → merge with ShiftReporting.shift_production() on [date, shift]
# → merge with QualityTracking.nok_by_shift() on [date, shift]
rate = rework.rework_rate('rework_counter', 'total_counter')
# → merge with ShiftReporting.shift_production() on [date, shift]

Example usage

tracker = ReworkTracking(df)

Rework per shift

shift_rework = tracker.rework_by_shift(rework_uuid='rework_counter')

Rework rate

rate = tracker.rework_rate( rework_uuid='rework_counter', total_production_uuid='total_counter', )

rework_by_shift

rework_by_shift(
    rework_uuid: str, *, value_column: str = "value_integer"
) -> pd.DataFrame

Rework count per shift.

Parameters:

Name	Type	Description	Default
rework_uuid	str	UUID for rework event counter signal.	required
value_column	str	Column containing counter values.	'value_integer'

Returns:

Type	Description
DataFrame	DataFrame with columns:
DataFrame	date, shift, rework_count

rework_by_reason

rework_by_reason(
    rework_uuid: str,
    reason_uuid: str,
    *,
    value_column_rework: str = "value_integer",
    value_column_reason: str = "value_string"
) -> pd.DataFrame

Rework quantity by reason code.

Parameters:

Name	Type	Description	Default
rework_uuid	str	UUID for rework counter signal.	required
reason_uuid	str	UUID for rework reason code signal.	required
value_column_rework	str	Column containing rework counter values.	'value_integer'
value_column_reason	str	Column containing reason codes.	'value_string'

Returns:

Type	Description
DataFrame	DataFrame with columns:
DataFrame	reason, rework_count, pct_of_total

rework_rate

rework_rate(
    rework_uuid: str,
    total_production_uuid: str,
    *,
    value_column_rework: str = "value_integer",
    value_column_production: str = "value_integer"
) -> pd.DataFrame

Rework rate as percentage of total production per shift.

Parameters:

Name	Type	Description	Default
rework_uuid	str	UUID for rework counter signal.	required
total_production_uuid	str	UUID for total production counter signal.	required
value_column_rework	str	Column containing rework counter values.	'value_integer'
value_column_production	str	Column containing production counter values.	'value_integer'

Returns:

Type	Description
DataFrame	DataFrame with columns:
DataFrame	date, shift, total_produced, rework_count, rework_rate_pct

rework_cost

rework_cost(
    rework_uuid: str,
    part_id_uuid: str,
    rework_costs: Dict[str, float],
    *,
    value_column_rework: str = "value_integer",
    value_column_part: str = "value_string"
) -> pd.DataFrame

Convert rework counts to monetary cost by part number.

Parameters:

Name	Type	Description	Default
rework_uuid	str	UUID for rework counter signal.	required
part_id_uuid	str	UUID for part number / product type signal.	required
rework_costs	Dict[str, float]	Dict mapping part numbers to cost per rework.	required
value_column_rework	str	Column containing rework counter values.	'value_integer'
value_column_part	str	Column containing part number values.	'value_string'

Returns:

Type	Description
DataFrame	DataFrame with columns:
DataFrame	part_number, rework_count, cost_per_rework, total_cost

rework_trend

rework_trend(
    rework_uuid: str,
    *,
    value_column: str = "value_integer",
    window: str = "1D"
) -> pd.DataFrame

Track rework count trend over time.

Parameters:

Name	Type	Description	Default
rework_uuid	str	UUID for rework counter signal.	required
value_column	str	Column containing counter values.	'value_integer'
window	str	Time window for aggregation (default '1D').	'1D'

Returns:

Type	Description
DataFrame	DataFrame with columns:
DataFrame	period, rework_count