line_balancing

line_balancing ¤

Line balancing and takt analysis for assembly / production lines.

Classic industrial-engineering line-balancing metrics computed from per-station cycle-completion signals:

station cycle times (windowed),
line balance efficiency, balance delay and smoothness index,
theoretical minimum number of stations for a given takt,
a Yamazumi (station-loading) table.

A station is identified by a boolean cycle-completion-trigger signal; the time between consecutive rising edges is that station's cycle time.

LineBalancingEvents ¤

LineBalancingEvents(
    dataframe: DataFrame,
    station_uuids: dict[str, str],
    *,
    event_uuid: str = "prod:line_balance",
    value_column: str = "value_bool",
    time_column: str = "systime"
)

Bases: Base

Line balancing and takt analysis from per-station cycle signals.

Example usage::

lb = LineBalancingEvents(
    df,
    station_uuids={
        "uuid_s1": "Station 1",
        "uuid_s2": "Station 2",
        "uuid_s3": "Station 3",
    },
)
lb.station_cycle_times(window="1h")
lb.balance_metrics(takt_time="55s", window="1h")
lb.yamazumi(demand=480, available_time="8h")

Initialize the line-balancing analyser.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data.	required
`station_uuids`	`dict[str, str]`	Mapping of cycle-completion-trigger UUID -> station name, in line order, e.g. `{"u1": "Station 1"}`.	required
`event_uuid`	`str`	UUID to tag derived events with.	`'prod:line_balance'`
`value_column`	`str`	Column holding the boolean cycle trigger.	`'value_bool'`
`time_column`	`str`	Name of the timestamp column.	`'systime'`

station_cycle_times ¤

station_cycle_times(window: str = '1h') -> pd.DataFrame

Per-station cycle-time statistics per time window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window (e.g. `"1h"`, `"30m"`).	`'1h'`

Returns:

Type	Description
`DataFrame`	Summary-shape DataFrame with columns: start, end, duration_seconds,
`DataFrame`	uuid, station_name, cycle_time_mean, cycle_time_median,
`DataFrame`	cycle_time_std, cycle_count.

balance_metrics ¤

balance_metrics(
    *,
    takt_time: _Number | None = None,
    demand: float | None = None,
    available_time: _Number | None = None,
    window: str = "1h"
) -> pd.DataFrame

Line-level balance metrics per time window.

Balance efficiency = sum(station times) / (n_stations * bottleneck). Takt is resolved from takt_time or from demand plus available_time; when neither is given, theoretical_min_stations is NaN.

Parameters:

Name	Type	Description	Default
`takt_time`	`_Number \| None`	Takt time as seconds or an offset string (e.g. "55s").	`None`
`demand`	`float \| None`	Units required over `available_time`.	`None`
`available_time`	`_Number \| None`	Available production time (seconds or offset string).	`None`
`window`	`str`	Resample window.	`'1h'`

Returns:

Type	Description
`DataFrame`	Summary-shape DataFrame with columns: start, end, duration_seconds,
`DataFrame`	n_stations, bottleneck_uuid, bottleneck_cycle_time, takt_seconds,
`DataFrame`	balance_efficiency_pct, balance_delay_pct, smoothness_index,
`DataFrame`	theoretical_min_stations.

yamazumi ¤

yamazumi(
    *,
    takt_time: _Number | None = None,
    demand: float | None = None,
    available_time: _Number | None = None
) -> pd.DataFrame

Yamazumi (station-loading) table over the whole dataset.

Parameters:

Name	Type	Description	Default
`takt_time`	`_Number \| None`	Takt time as seconds or an offset string.	`None`
`demand`	`float \| None`	Units required over `available_time`.	`None`
`available_time`	`_Number \| None`	Available production time (seconds or offset string).	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: uuid, station_name, cycle_time_mean,
`DataFrame`	takt_seconds, loading_pct, idle_to_takt_seconds, is_bottleneck.
`DataFrame`	Stations are returned in the order given to the constructor.