https://github.com/profasem/logistics-performance-analysis
Power BI dashboard analyzing logistics performance, delivery delays, carrier efficiency, and regional risk.
https://github.com/profasem/logistics-performance-analysis
business-intelligence dashboard data-analysis logistics powerbi python supply-chain
Last synced: 2 months ago
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Power BI dashboard analyzing logistics performance, delivery delays, carrier efficiency, and regional risk.
- Host: GitHub
- URL: https://github.com/profasem/logistics-performance-analysis
- Owner: ProfASEM
- Created: 2026-04-20T22:16:34.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2026-04-20T23:07:52.000Z (2 months ago)
- Last Synced: 2026-04-21T00:32:43.471Z (2 months ago)
- Topics: business-intelligence, dashboard, data-analysis, logistics, powerbi, python, supply-chain
- Language: Jupyter Notebook
- Homepage:
- Size: 1.22 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Logistics Performance Analysis Dashboard 🚚📊
## 📌 Overview
This project provides a comprehensive analysis of logistics performance, focusing on identifying the key drivers of delivery delays and operational inefficiencies.
The analysis explores how delivery performance is impacted by:
* Distance
* Carrier performance
* Shipment type
* Regional differences
The goal is to move beyond basic reporting and deliver **data-driven insights that support operational decision-making**.
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**Note:** The dataset used in this project is synthetically generated based on realistic logistics patterns to simulate operational challenges and support analytical exploration.
## 🎯 Objectives
* Analyze delivery delays across multiple dimensions
* Evaluate carrier performance using a composite performance score
* Identify operational bottlenecks and inefficiencies
* Provide actionable, strategy-level recommendations
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## 🛠 Tools & Technologies
* **Power BI** – Dashboard design & visualization
* **Python (Pandas)** – Data preparation & feature engineering
* **SQL (optional)** – Data structuring
* **Excel** – Initial data exploration
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## Project Files
- `dashboard/logistics_dashboard.pbix` — Power BI dashboard
- `notebooks/data_generation.ipynb` — data preparation
- `notebooks/main_analysis` analysis
- `datasets/` — project data
- `images/` — dashboard screenshots
## 📊 Dashboard Structure
### 1. Executive Overview
Provides a high-level summary of:
* Average delay
* On-time delivery rate
* Best & worst performing carriers
* Key operational insights
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### 2. Distance Impact Analysis
Focuses on how distance affects delivery performance:
* Delays increase significantly with distance
* Sharp performance deterioration beyond 600 km
* Distance identified as the primary operational driver
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### 3. Carrier Performance Analysis
Evaluates logistics providers using:
* Performance score (weighted model)
* On-time delivery rate
* Average delay comparison
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### 4. Regional Risk Analysis
Highlights geographic impact on logistics:
* Significant delay variations across regions
* High-risk regions with consistent underperformance
* Interaction between region complexity and carrier capability
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### 5. Strategic Recommendations
Transforms insights into actionable strategies:
* Operational improvements
* Carrier performance optimization
* Region-specific logistics planning
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## 🔍 Key Insights
* Delivery performance deteriorates significantly beyond 600 km
* Distance is the strongest driver of delays
* Carrier performance varies widely, indicating structural inefficiencies
* Regional factors play a critical role in delivery reliability
* Logistics performance is driven by the interaction of distance, region, and carrier
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## 💡 Business Value
This analysis enables logistics stakeholders to:
* Identify operational bottlenecks
* Optimize routing and delivery planning
* Improve carrier evaluation using data-driven metrics
* Enhance overall delivery efficiency and reliability
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## 📈 Performance Scoring Model
A composite performance score was developed using:
* On-time delivery rate (60%)
* Average delay (30%)
* Shipping cost efficiency (10%)
This model allows for a balanced evaluation of carriers based on both reliability and efficiency.
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## 🔮 Future Improvements
* Build predictive models for delivery delays
* Integrate real-time logistics data
* Develop automated KPI monitoring dashboards
* Expand dataset to include additional regions and carriers
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## Author
**Asem Haij**
Data Analyst | Python • Power BI • SQL
[LinkedIn](asem-haij-9797562a8) | [GitHub](ProfAsem) | [Portfolio](asemhaij.com)
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## ⭐ Notes
This project is designed to reflect a **consulting-style analytical approach**, focusing on:
* Insight generation
* Business impact
* Strategic thinking
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