Track end-to-end delivery performance for international retail logistics. This scorecard is automatically fed by an n8n workflow that aggregates shipment data from your WMS/TMS and computes weekly OTIF KPIs.

<aside> 📋

How to Use This Template

1. Connect the companion n8n workflow to your shipment data source (WMS, TMS, or database)
2. The workflow aggregates raw shipments into weekly summaries and pushes them to the database below
3. OTIF Rate and Status are auto-calculated by Notion formulas
4. Use the checkpoint breakdown (Transmission, Loading, Airport, Landing, Delivery) to pinpoint where delays originate
5. Review the AI Generated Analysis cards for per-week insights and the overall performance summary </aside>

<aside> ⚙️

n8n Automation

This scorecard is designed to be populated automatically.

The companion n8n workflow reads raw shipment records, groups them by week, computes averages and on-time percentages for each checkpoint, and creates one row per week in the database below. You can schedule it to run daily or weekly.

</aside>

Process Flow

OTIF Performance Target

<aside> 🔄

Order → Transmission → Pick and Pack → Loading → Truck to Airport → Flight → Customs Clearance → Last Mile → Store Delivery

Each checkpoint has a cut-off time. Missing a cut-off delays the shipment to the next available slot, increasing total lead time and reducing OTIF performance.

</aside>

AI Generated Analysis

Weekly OTIF Summary

Understanding the Metrics

<aside> 📊

OTIF Rate (%)

On-Time In-Full delivery rate. The percentage of shipments that arrived at the store within the target lead time. This is the primary KPI for end-to-end distribution performance.

OTIF Rate = On-Time Deliveries / Total Shipments × 100

</aside>

<aside> ⏱️

Avg Lead Time

The average number of days (and hours) from order creation to store delivery. A rising lead time often signals systemic issues even when OTIF remains acceptable. Typical target for air freight distribution: 3.0 to 3.5 days.

</aside>

<aside> 🔍

Checkpoint On-Time Rates

Each checkpoint in the supply chain has its own on-time percentage. When OTIF drops, compare these rates to find the bottleneck:

• Transmission On-Time %: Was the order transmitted to the warehouse before the cut-off?
• Loading On-Time %: Was the shipment loaded onto the truck before the 18:00 cut-off?
• Airport On-Time %: Did the truck arrive at the airport before the flight cut-off?
• Landing On-Time %: Did the flight arrive on schedule?
• Delivery On-Time %: Was the shipment delivered to the store within the target window? </aside>

<aside> 🚨

Late Shipments

The absolute count of shipments that missed the delivery target. Use this alongside OTIF Rate to understand volume impact. A 90% OTIF on 100 shipments means 10 late deliveries; the same 90% on 1,000 shipments means 100 late deliveries.

</aside>

How the n8n Workflow Works

<aside> 🔧

Data Pipeline

1. Read raw shipment records from your data source (database, API, or spreadsheet)
2. Group shipments by week (Monday to Sunday)
3. Aggregate per week: count total, count on-time flags, compute averages
4. Push one summary row per week to this Notion database via the Notion API
5. Schedule the workflow to run automatically (daily or weekly)

The Notion formulas handle OTIF Rate and Status calculations, so the n8n workflow only needs to push the raw counts and averages.

n8n Workflow & Resources

• The complete n8n workflow, markdown description, and video tutorial are available here: Supply Chain OTIF Performance Reporting

Get in Touch

For more supply chain analytics content, visit samirsaci.com

</aside>