First-Attempt-Delivery-Rate

The First-Attempt-Delivery-Rate (FADR) is a key metric for fulfillment teams that want to improve delivery quality, cost control, and customer satisfaction at the same time. It measures the share of all delivery attempts in which the package is successfully delivered directly on the first attempt.

The higher the metric, the lower the follow-up costs caused by repeated delivery runs, support inquiries, and delayed deliveries. If the value drops, ticket volumes, investigation cases, and carrier costs usually rise in parallel.

What the metric actually indicates

FADR answers a business-critical question: How many shipments reach the recipient without a second delivery attempt? Unlike general lead-time KPIs, this metric is very close to the service quality perceived by customers.

Formula and delineation

The standard formula is:

Count successful first deliveries within the period
Divide by all attempted deliveries in the same period
Multiply by 100

Formula: FADR (%) = (Successful first deliveries / All delivery attempts) x 100

For reliable figures, clear delineation is crucial:

Mark undeliverable addresses as a separate quality cause
Evaluate customer-rescheduled appointments separately
Do not include internal re-routings in a way that distorts the ratio

Workflow diagram (KPI determination): Collect tracking events -> Identify first delivery attempt -> Classify success status -> Cluster causes in case of failure -> Calculate KPI per carrier/region -> Weekly review routine with actions.

Why FADR is economically so important

A low FADR is rarely an isolated issue and causes direct as well as indirect costs.

Additional carrier fees due to repeated delivery attempts
Higher workload in customer service due to inquiries
Increased probability of returns when delivery paths are long
Worse rating results in shop and marketplace

Even small improvements have a strong impact at high volume. An increase from 91 to 95 percent reduces thousands of repeated delivery attempts per month and stabilizes operational planning.

Typical influencing factors

Address quality: House numbers, additional information, typos, incorrect postal code
Carrier management: Routing, delivery time windows, regional performance
Customer communication: Pre-announcement, tracking transparency, redirection options
Data and process quality: Event mapping, cut-off discipline, SLA monitoring

Benchmarks and target values

Benchmarks depend on product category, region, and delivery model. The following reference values help with classification:

Maturity level

FADR

Typical profile

Priority

Critical

< 90 %

High address error rate, little tracking communication, fluctuating carrier performance

Immediate root-cause analysis by region and carrier

Stabilization phase

90-94 %

Core processes in place, but inconsistent data quality

Address validation, event standardization, SLA review

Advanced

95-97 %

Clean standard processes and active carrier management

Segmented optimization by product and customer groups

Excellent

>= 98 %

Proactive delivery control with early escalation

Fine-tuning, exception management, continuous improvement process

Trend chart (12 months): Start at 92.3 percent, end at 96.1 percent, target line at 95 percent, plus marking of the three strongest improvement months including the corresponding action.

Causes of low first-delivery rates

1) Address and master data issues

Incorrect addresses are the biggest lever in many projects.

Missing house numbers or invalid combinations
Swapped fields between street and additional information
Non-standardized inputs for international formats

2) Unclear customer expectations in the delivery window

Without a realistic delivery window and notification, the non-delivery rate rises significantly. Transparent delivery communication is a key KPI driver.

3) Carrier mismatch by region

Not every carrier performs equally well in every region. A monthly region-by-carrier matrix quickly shows where switches or SLA adjustments make sense.

4) Inconsistent status codes

If tracking events are not standardized, first deliveries cannot be measured cleanly. Typical consequence: an apparently good ratio despite a high complaint load.

Implementation plan in 30-60-90 days

Phase 1: Create transparency (Day 1-30)

Convert tracking events into consistent success/failure clusters
Determine baseline by carrier, region, and product group
Quantify top 3 causes with cost impact

Phase 2: Roll out core actions (Day 31-60)

Enable address validation in checkout and order clearing
Standardize proactive customer notifications before delivery
Introduce regional carrier rules with SLA thresholds

Phase 3: Stabilize and scale (Day 61-90)

Weekly KPI review with cause-action tracking
Define escalation paths for critical regions
Manage targets by channel and seasonal peaks separately

Day 1-30

Build baseline and transparency

Day 31-60

Go live with core actions

Day 61-90

Achieve stabilization and scaling

Operational control model for teams

An effective model combines daily control, weekly analysis, and monthly decision-making.

Control level

Interval

Responsibility

Core questions

Operational monitoring

Daily

Fulfillment team lead

Where do the most first-delivery errors currently occur?

Performance review

Weekly

Logistics + customer service

Which cause clusters dominate, and which action is effective?

SLA decision

Monthly

Operations management

Are carrier rules, cut-off, and forecasting still appropriate?

Checklist for robust FADR control

KPI definition and formula documented consistently across the team
Tracking events per carrier mapped to a shared mapping
First delivery clearly separated from repeat delivery
Address validation integrated as mandatory before shipment
Regional performance by carrier analyzed monthly
Pre-delivery customer communication with clear time windows active
Escalation rules defined for KPI underperformance
Peak-season rules for utilization and cut-off in place
Cause-action backlog maintained with owners
Economic impact of each improvement tracked

Common interpretation mistakes

A rising FADR alone does not automatically mean better end-to-end quality if lead times or completeness decrease at the same time.

The ratio is viewed only globally, without segmentation by region and carrier
Special cases are not marked and distort the measurement
The KPI is reported but not linked to concrete actions

Tip: Use a combined view of FADR, OTIF, delivery time, and complaint rate. Only the interplay provides a reliable picture of service quality.

Practical example: Impact of structured optimization

A mid-sized online retailer with multiple carrier contracts started at 92.1 percent FADR. After standardized event mapping, address verification, and regional carrier management, the value reached 95.8 percent after three months.

At the same time, customer service ticket volume dropped by 18 percent, and costs for repeated delivery attempts decreased significantly. The decisive factor was the combination of data quality, operational discipline, and fast escalation.

Before/after comparison: Before: FADR 92.1 percent and support tickets at 100 percent baseline. After: FADR 95.8 percent and support tickets at 82 percent of baseline.