lead_time_analysis

lead_time_analysis ¤

Lead time analysis events for supply chain timeseries.

Matches order events to delivery events, computes lead time statistics, and detects anomalous lead times.

LeadTimeAnalysisEvents ¤

LeadTimeAnalysisEvents(
    dataframe: DataFrame,
    *,
    event_uuid: str = "sc:lead_time",
    time_column: str = "systime"
)

Bases: Base

Analyze lead times between order placement and delivery in timeseries data.

Order and delivery events are identified by separate UUIDs. When the delivery rows carry the order identifier, each order is matched to the first delivery of the same identifier at or after it; otherwise each order, in time order, is paired with the first not yet used delivery at or after it.

Example usage

analyzer = LeadTimeAnalysisEvents(df) lead_times = analyzer.lead_times('order_signal', 'delivery_signal') stats = analyzer.lead_time_statistics('order_signal', 'delivery_signal') anomalies = analyzer.detect_lead_time_anomalies('order_signal', 'delivery_signal')

Initialize lead time analyzer.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data.	required
`event_uuid`	`str`	UUID assigned to generated events.	`'sc:lead_time'`
`time_column`	`str`	Name of timestamp column.	`'systime'`

lead_times ¤

lead_times(
    order_uuid: str,
    delivery_uuid: str,
    value_column: str = "value_string",
) -> pd.DataFrame

Match order events to delivery events.

The value_column is used as an order identifier (e.g. PO number). When deliveries carry identifiers that occur among the orders, each order is matched to the first delivery with the same identifier at or after the order time (unmatched orders are left out). Otherwise each order, in time order, is paired with the first delivery at or after it that is not paired yet; orders and deliveries left without a partner are dropped with a :class:~ts_shape.errors.DataQualityWarning. A lead time is never negative.

Parameters:

Name	Type	Description	Default
`order_uuid`	`str`	UUID of the order placement signal.	required
`delivery_uuid`	`str`	UUID of the delivery received signal.	required
`value_column`	`str`	Column containing order identifiers.	`'value_string'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, order_time,
`DataFrame`	delivery_time, uuid, source_uuid, is_delta, lead_time_seconds,
`DataFrame`	lead_time_hours, order_id. `start`/`end` are the order and
`DataFrame`	delivery times; `source_uuid` is `"<order_uuid>,<delivery_uuid>"`.

lead_time_statistics ¤

lead_time_statistics(
    order_uuid: str,
    delivery_uuid: str,
    value_column: str = "value_string",
) -> pd.DataFrame

Compute summary statistics of lead times.

Parameters:

Name	Type	Description	Default
`order_uuid`	`str`	UUID of the order placement signal.	required
`delivery_uuid`	`str`	UUID of the delivery received signal.	required
`value_column`	`str`	Column containing order identifiers.	`'value_string'`

Returns:

Type	Description
`DataFrame`	Single-row DataFrame with columns: mean_hours, std_hours,
`DataFrame`	min_hours, max_hours, p95_hours, count.

detect_lead_time_anomalies ¤

detect_lead_time_anomalies(
    order_uuid: str,
    delivery_uuid: str,
    threshold_factor: float = 2.0,
    value_column: str = "value_string",
) -> pd.DataFrame

Flag lead times exceeding mean + threshold_factor * std.

Parameters:

Name	Type	Description	Default
`order_uuid`	`str`	UUID of the order placement signal.	required
`delivery_uuid`	`str`	UUID of the delivery received signal.	required
`threshold_factor`	`float`	Number of standard deviations above mean to flag as anomalous.	`2.0`
`value_column`	`str`	Column containing order identifiers.	`'value_string'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: systime, order_time, delivery_time, uuid,
`DataFrame`	source_uuid, is_delta, lead_time_hours, z_score. `systime` is
`DataFrame`	the delivery time (when the anomaly becomes known).