First Attempt Delivery Rate (FADR)

FADR is calculated by dividing the total number of parcels successfully delivered on the first attempt by the total number of parcels out for delivery, expressed as a percentage.

• Formula: (First Attempt Deliveries / Total Outbound Parcels) × 100
• Inverse Metric: The complement of FADR is the First Attempt Failure Rate, which directly triggers exception management workflows.
• Granularity: It is typically measured daily but is most actionable when aggregated by driver, route, or delivery window.

A failed first attempt generates a significant non-linear cost increase, often making the last mile the most expensive leg of the supply chain.

• Re-attempt Costs: Each subsequent delivery attempt can cost 2-3x the original drop due to additional fuel, labor, and vehicle depreciation.
• Reverse Logistics: Failures often trigger costly returns processing and restocking fees.
• Customer Service Load: Failed deliveries spike call center volume with WISMO (Where Is My Order?) inquiries, increasing operational overhead.

FADR serves as a direct leading indicator for Net Promoter Score (NPS) and customer retention in e-commerce.

• Convenience Failure: A missed delivery represents a broken brand promise, often resulting in immediate churn.
• Delivery Anxiety: Low FADR rates condition customers to expect failure, reducing their willingness to choose premium shipping options.
• Retail Benchmark: Top-quartile logistics providers maintain FADR rates above 95%, while sub-90% performance is typically correlated with negative customer sentiment.

Diagnosing low FADR requires segmenting failures into distinct, addressable categories rather than treating them as a monolithic problem.

• Recipient Not Available (RNA): The most common cause, often mitigated by dynamic time window optimization and proactive SMS/email notifications.
• Address Quality Issues: Incorrect or poorly standardized addresses cause geocoding failures, sending drivers to wrong locations.
• Access Restrictions: Gated communities, secure office buildings, or missing entry codes without pre-communicated instructions.
• Driver Behavior: Premature stop-closure or failure to wait sufficient time at the delivery point.

Modern logistics platforms use machine learning to predict failure probability before a vehicle departs, enabling preemptive action.

• ETA Prediction Engines: Gradient boosted trees analyze historical patterns to predict precise arrival times, enabling narrow, accurate delivery windows.
• Dynamic Re-Routing: Real-time traffic and weather data adjust routes to maintain promised windows.
• Recipient Engagement: Automated systems trigger personalized delivery confirmations and live tracking links to ensure recipient availability.
• Address Standardization: AI-powered parsing and validation against postal authority databases correct errors before label generation.

FADR is a necessary but insufficient condition for achieving high On-Time In-Full (OTIF) scores, a critical retail compliance metric.

• Temporal Component: A parcel can be delivered on the first attempt but still fail OTIF if it arrives outside the specified time window.
• Integrity Component: FADR does not measure whether the correct items were delivered in the correct quantity.
• Hierarchical Metric: FADR is a subset of the broader delivery success framework; optimizing for FADR alone without considering OTIF can lead to rushed, inaccurate deliveries.

A composite supply chain metric measuring the percentage of deliveries that arrive at the correct location, in the correct quantity, and within the specified time window. While FADR tracks the first-visit success, OTIF captures the broader contractual compliance. A delivery can fail OTIF even if FADR succeeds—for example, if the correct parcel arrives on the first attempt but outside the promised 2-hour window. Retailers like Walmart impose strict OTIF thresholds with financial penalties for non-compliance, making it a critical complement to FADR in vendor scorecards.

The digital or physical confirmation that a shipment has been successfully received at its destination. Modern PoD systems capture a timestamp, GPS coordinates, a recipient signature, and often a photograph of the parcel at the doorstep. This data is essential for validating FADR claims—without a verifiable PoD, a carrier cannot prove a first-attempt success occurred. PoD feeds directly into exception management workflows, triggering automated alerts when a delivery attempt fails and initiating the re-delivery or pickup-at-locker process.

The real-time adjustment of a vehicle's planned path in response to new events such as traffic congestion, road closures, or newly assigned on-demand orders. Dynamic re-routing directly impacts FADR by ensuring drivers can adapt to conditions that would otherwise cause a missed delivery window. For example, if an accident blocks a planned route, the system recalculates an alternative path and updates the ETA Prediction Engine to notify the recipient. This capability relies on continuous telemetry ingestion and low-latency optimization solvers.

The computational process of converting a human-readable street address into precise geographic latitude and longitude coordinates. Poor geocoding is a leading root cause of failed first attempts—an incorrectly geocoded address can route a driver to the wrong building, a back alley, or an inaccessible entrance. High-precision rooftop-level geocoding uses parcel boundary data and building footprint polygons rather than street-level interpolation, dramatically improving FADR by ensuring the navigation endpoint is the actual doorstep, not just the street segment midpoint.

A machine learning system that predicts the estimated time of arrival by analyzing historical transit data, real-time traffic, and driver behavior. Accurate ETAs are critical for FADR because they enable proactive recipient communication—when a customer knows precisely when a package will arrive, they are more likely to be present to receive it. Modern ETA engines use gradient boosted trees or deep learning on features including time of day, weather, road segment speeds, and individual driver patterns to achieve prediction errors under 5 minutes.

The process of parsing and formatting raw address data into a canonical, structured format that conforms to postal authority standards. Inconsistent address entry—such as 'St.' vs 'Street' or missing apartment numbers—directly degrades FADR by introducing ambiguity into the delivery location. Address standardization engines use fuzzy matching, postal reference databases, and machine learning to resolve these inconsistencies before the parcel enters the sortation system. This preprocessing step is especially critical in markets with non-Latin scripts or informal addressing conventions.