inventory_monitoring

inventory_monitoring ¤

Inventory monitoring events for supply chain timeseries.

Detects low stock intervals, calculates consumption rates, identifies reorder point breaches, and predicts stockouts.

InventoryMonitoringEvents ¤

InventoryMonitoringEvents(
    dataframe: DataFrame,
    level_uuid: str,
    *,
    event_uuid: str = "sc:inventory",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Monitor inventory levels from a timeseries signal and detect supply chain events.

Each inventory level is tracked via a UUID signal whose numeric value represents the current stock quantity.

Example usage

tracker = InventoryMonitoringEvents(df, level_uuid='warehouse_a_level') low = tracker.detect_low_stock(min_level=100, hold='5min') rate = tracker.consumption_rate(window='1h') breach = tracker.reorder_point_breach(reorder_level=200, safety_stock=50) prediction = tracker.stockout_prediction(consumption_rate_window='4h')

Initialize inventory monitoring.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data.	required
`level_uuid`	`str`	UUID of the inventory level signal.	required
`event_uuid`	`str`	UUID assigned to generated events.	`'sc:inventory'`
`value_column`	`str`	Column containing numeric inventory levels.	`'value_double'`
`time_column`	`str`	Name of timestamp column.	`'systime'`

detect_low_stock ¤

detect_low_stock(
    min_level: float, hold: str = "0s"
) -> pd.DataFrame

Flag intervals where inventory stays below min_level for at least hold.

Parameters:

Name	Type	Description	Default
`min_level`	`float`	Threshold below which stock is considered low.	required
`hold`	`str`	Minimum duration the level must stay below threshold (e.g. `'0s'`, `'5min'`, `'1h'`).	`'0s'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, uuid, source_uuid, is_delta,
`DataFrame`	min_value, avg_value, duration_seconds.

consumption_rate ¤

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

Calculate rolling consumption rate from inventory level decreases.

Only considers intervals where the level decreased (consumption). The rate is expressed as units consumed per hour within each window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Time-based window for grouping (e.g. `'1h'`, `'30min'`).	`'1h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, uuid, is_delta,
`DataFrame`	consumption_rate, level_start, level_end.

reorder_point_breach ¤

reorder_point_breach(
    reorder_level: float, safety_stock: float = 0.0
) -> pd.DataFrame

Detect when inventory falls below reorder point or safety stock level.

Parameters:

Name	Type	Description	Default
`reorder_level`	`float`	The reorder point threshold.	required
`safety_stock`	`float`	Safety stock threshold (must be <= reorder_level).	`0.0`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: systime, uuid, is_delta, current_level,
`DataFrame`	breach_type ('reorder' or 'safety_stock'), deficit.

stockout_prediction ¤

stockout_prediction(
    consumption_rate_window: str = "4h",
) -> pd.DataFrame

Estimate time until stockout based on recent consumption rate.

For each data point, uses the consumption rate calculated over the preceding consumption_rate_window to project when inventory will reach zero.

Parameters:

Name	Type	Description	Default
`consumption_rate_window`	`str`	Lookback window for calculating consumption rate (e.g. `'4h'`).	`'4h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: systime, uuid, is_delta, current_level,
`DataFrame`	consumption_rate, estimated_stockout_time_hours.