logistics-kpi-tracker
Comprehensive logistics performance measurement skill with KPI tracking, benchmarking, and improvement recommendations
Best use case
logistics-kpi-tracker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Comprehensive logistics performance measurement skill with KPI tracking, benchmarking, and improvement recommendations
Teams using logistics-kpi-tracker should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/logistics-kpi-tracker/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How logistics-kpi-tracker Compares
| Feature / Agent | logistics-kpi-tracker | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Comprehensive logistics performance measurement skill with KPI tracking, benchmarking, and improvement recommendations
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Logistics KPI Tracker
## Overview
The Logistics KPI Tracker provides comprehensive logistics performance measurement with KPI tracking, benchmarking, and improvement recommendations. It monitors key metrics across all logistics functions and identifies opportunities for operational improvement.
## Capabilities
- **OTIF (On-Time In-Full) Tracking**: Track on-time and in-full delivery performance against commitments
- **Perfect Order Rate Calculation**: Calculate perfect order rates considering all order fulfillment dimensions
- **Fill Rate Analysis**: Monitor fill rates at order, line, and unit levels
- **Order Cycle Time Measurement**: Track order cycle times from order placement to delivery
- **Cost Per Order/Unit Tracking**: Calculate and monitor logistics costs at various levels
- **Benchmark Comparison**: Compare performance against industry benchmarks and best practices
- **Improvement Opportunity Identification**: Identify areas for operational improvement based on KPI analysis
## Tools and Libraries
- BI Platforms (Tableau, Power BI)
- Data Warehousing
- Logistics Dashboards
- Statistical Analysis Libraries
## Used By Processes
- All logistics processes (cross-cutting)
## Usage
```yaml
skill: logistics-kpi-tracker
inputs:
reporting_period:
start: "2026-01-01"
end: "2026-01-24"
data_sources:
orders: true
shipments: true
inventory: true
costs: true
benchmarks:
otif: 95.0
perfect_order_rate: 90.0
fill_rate: 98.0
order_cycle_time_days: 3.0
cost_per_order: 12.50
comparison:
prior_period: true
prior_year: true
outputs:
kpi_summary:
otif:
actual: 93.5
target: 95.0
variance: -1.5
trend: "improving"
prior_period: 92.8
prior_year: 91.2
perfect_order_rate:
actual: 88.2
target: 90.0
variance: -1.8
trend: "stable"
components:
on_time: 93.5
in_full: 96.2
damage_free: 99.1
accurate_documentation: 98.5
fill_rate:
actual: 97.5
target: 98.0
variance: -0.5
trend: "stable"
order_cycle_time:
actual_days: 2.8
target_days: 3.0
variance: 0.2
trend: "improving"
cost_per_order:
actual: 11.85
target: 12.50
variance: 0.65
trend: "improving"
performance_breakdown:
by_channel:
ecommerce: { otif: 91.2, fill_rate: 96.8 }
wholesale: { otif: 95.8, fill_rate: 98.2 }
retail: { otif: 94.1, fill_rate: 97.8 }
by_region:
northeast: { otif: 94.5, fill_rate: 98.1 }
southeast: { otif: 92.8, fill_rate: 97.0 }
midwest: { otif: 93.9, fill_rate: 97.5 }
improvement_opportunities:
- area: "On-Time Delivery"
current: 93.5
target: 95.0
gap: 1.5
root_causes:
- "Carrier performance in Southeast region"
- "Dock congestion at DC002"
recommendations:
- "Carrier performance review with underperformers"
- "Implement dock scheduling system at DC002"
potential_improvement: 2.0
- area: "Fill Rate"
current: 97.5
target: 98.0
gap: 0.5
root_causes:
- "Safety stock levels insufficient for high-velocity items"
recommendations:
- "Review and adjust safety stock for A-class items"
potential_improvement: 0.8
```
## Integration Points
- Enterprise Resource Planning (ERP)
- Warehouse Management Systems (WMS)
- Transportation Management Systems (TMS)
- Order Management Systems
- Business Intelligence Platforms
## Performance Metrics
- OTIF percentage
- Perfect order rate
- Fill rate
- Order cycle time
- Cost per orderRelated Skills
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