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LeadTimeAnalysisEvents¤

Analyze lead times between order placement and delivery.

Module: ts_shape.events.supplychain.lead_time_analysis Guide: Production Guide

When to Use¤

Use to monitor supplier and internal process lead times. Pairs order and delivery events to compute lead times and flag anomalies that may disrupt production planning. Essential for supply chain reliability assessment and vendor performance tracking.

Quick Example¤

from ts_shape.events.supplychain.lead_time_analysis import LeadTimeAnalysisEvents

analyzer = LeadTimeAnalysisEvents(
    df=orders_df,
    timestamp_col="timestamp",
    event_type_col="event_type",
    order_id_col="order_id"
)

# Match order placement to delivery events
lead_times = analyzer.calculate_lead_times(
    order_event="ORDER_PLACED",
    delivery_event="DELIVERED"
)

# Summary statistics across all orders
stats = analyzer.lead_time_statistics()
print(f"Mean lead time: {stats['mean']:.1f} days")
print(f"95th percentile: {stats['p95']:.1f} days")

# Flag orders with abnormally long lead times
anomalies = analyzer.detect_lead_time_anomalies(threshold_std=2.0)
print(f"Anomalous deliveries: {len(anomalies)}")

Key Methods¤

Method Purpose Returns
calculate_lead_times() Match orders to deliveries and compute durations DataFrame with order ID, lead time, and timestamps
lead_time_statistics() Summary statistics (mean, median, percentiles) Dict with mean, median, std, p5, p25, p75, p95
detect_lead_time_anomalies() Flag orders with abnormal lead times DataFrame of anomalous orders with deviation score

Tips & Notes¤

Segment lead times by supplier or product

Global lead time statistics can hide per-supplier variation. Group by supplier before calling lead_time_statistics() to identify which vendors are unreliable and which are consistently fast.

Related modules

See Also¤