{"id":49103831,"url":"https://github.com/profasem/logistics-performance-analysis","last_synced_at":"2026-04-21T01:00:22.908Z","repository":{"id":352740191,"uuid":"1216419851","full_name":"ProfASEM/logistics-performance-analysis","owner":"ProfASEM","description":"Power BI dashboard analyzing logistics performance, delivery delays, carrier efficiency, and regional risk.","archived":false,"fork":false,"pushed_at":"2026-04-20T23:07:52.000Z","size":1278,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-04-21T00:32:43.471Z","etag":null,"topics":["business-intelligence","dashboard","data-analysis","logistics","powerbi","python","supply-chain"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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logistics performance, focusing on identifying the key drivers of delivery delays and operational inefficiencies.\n\nThe analysis explores how delivery performance is impacted by:\n\n* Distance\n* Carrier performance\n* Shipment type\n* Regional differences\n\nThe goal is to move beyond basic reporting and deliver **data-driven insights that support operational decision-making**.\n\n---\n\n **Note:** The dataset used in this project is synthetically generated based on realistic logistics patterns to simulate operational challenges and support analytical exploration.\n\n## 🎯 Objectives\n\n* Analyze delivery delays across multiple dimensions\n* Evaluate carrier performance using a composite performance score\n* Identify operational bottlenecks and inefficiencies\n* Provide actionable, strategy-level recommendations\n\n---\n\n## 🛠 Tools \u0026 Technologies\n\n* **Power BI** – Dashboard design \u0026 visualization\n* **Python (Pandas)** – Data preparation \u0026 feature engineering\n* **SQL (optional)** – Data structuring\n* **Excel** – Initial data exploration\n\n---\n\n## Project Files\n- `dashboard/logistics_dashboard.pbix` — Power BI dashboard\n- `notebooks/data_generation.ipynb` — data preparation\n- `notebooks/main_analysis` analysis\n- `datasets/` — project data\n- `images/` — dashboard screenshots\n\n## 📊 Dashboard Structure\n\n### 1. Executive Overview\n\n![Executive Overview](images/executive_overview.png)\n\nProvides a high-level summary of:\n\n* Average delay\n* On-time delivery rate\n* Best \u0026 worst performing carriers\n* Key operational insights\n\n---\n\n### 2. Distance Impact Analysis\n\n![Distance Analysis](images/delay_analysis.png)\n\nFocuses on how distance affects delivery performance:\n\n* Delays increase significantly with distance\n* Sharp performance deterioration beyond 600 km\n* Distance identified as the primary operational driver\n\n---\n\n### 3. Carrier Performance Analysis\n\n![Carrier Performance](images/carrier_performance.png)\n\nEvaluates logistics providers using:\n\n* Performance score (weighted model)\n* On-time delivery rate\n* Average delay comparison\n\n---\n\n### 4. Regional Risk Analysis\n\n![Regional Analysis](images/regional_risk_analysis.png)\n\nHighlights geographic impact on logistics:\n\n* Significant delay variations across regions\n* High-risk regions with consistent underperformance\n* Interaction between region complexity and carrier capability\n\n---\n\n### 5. Strategic Recommendations\n\n![Strategic Recommendations](images/strategic_recommendations.png)\n\nTransforms insights into actionable strategies:\n\n* Operational improvements\n* Carrier performance optimization\n* Region-specific logistics planning\n\n---\n\n## 🔍 Key Insights\n\n* Delivery performance deteriorates significantly beyond 600 km\n* Distance is the strongest driver of delays\n* Carrier performance varies widely, indicating structural inefficiencies\n* Regional factors play a critical role in delivery reliability\n* Logistics performance is driven by the interaction of distance, region, and carrier\n\n---\n\n## 💡 Business Value\n\nThis analysis enables logistics stakeholders to:\n\n* Identify operational bottlenecks\n* Optimize routing and delivery planning\n* Improve carrier evaluation using data-driven metrics\n* Enhance overall delivery efficiency and reliability\n\n---\n\n## 📈 Performance Scoring Model\n\nA composite performance score was developed using:\n\n* On-time delivery rate (60%)\n* Average delay (30%)\n* Shipping cost efficiency (10%)\n\nThis model allows for a balanced evaluation of carriers based on both reliability and efficiency.\n\n---\n\n## 🔮 Future Improvements\n\n* Build predictive models for delivery delays\n* Integrate real-time logistics data\n* Develop automated KPI monitoring dashboards\n* Expand dataset to include additional regions and carriers\n\n---\n\n## Author\n**Asem Haij**  \nData Analyst | Python • Power BI • SQL  \n[LinkedIn](asem-haij-9797562a8) | [GitHub](ProfAsem) | [Portfolio](asemhaij.com)\n---\n\n## ⭐ Notes\n\nThis project is designed to reflect a **consulting-style analytical approach**, focusing on:\n\n* Insight generation\n* Business impact\n* Strategic thinking\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprofasem%2Flogistics-performance-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprofasem%2Flogistics-performance-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprofasem%2Flogistics-performance-analysis/lists"}