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. They are paired sequentially (first order to first delivery, etc.).

Example usage

analyzer = LeadTimeAnalysisEvents(df) lead_times = analyzer.calculate_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'

calculate_lead_times

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

Match order events to delivery events by sequential pairing.

The first order is paired with the first delivery, and so on. The value_column is used as an order identifier (e.g. PO number).

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: order_time, delivery_time, uuid, is_delta,
DataFrame	lead_time_seconds, lead_time_hours, order_id.

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: order_time, delivery_time, uuid, is_delta,
DataFrame	lead_time_hours, z_score.