{"id":28368203,"url":"https://github.com/brunopata/dataco-supply-chain","last_synced_at":"2026-01-28T14:32:10.014Z","repository":{"id":295089335,"uuid":"989091675","full_name":"brunopata/DataCo-Supply-Chain","owner":"brunopata","description":"A complete end-to-end data project exploring operational efficiency, customer behavior and profitability across 180K+ orders. 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[DataCo Smart Supply Chain Dataset](https://data.mendeley.com/datasets/8gx2fvg2k6/5)\n  - [DataCo Smart Supply Chain Dataset on Kaggle](https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis)\n  \n- 🛍️ **`Data:`**\n  \n  - ~18K customers\n  - ~180K orders\n  - 3 years of data\n  - Products acrosse 50+ categories\n\n## 📁 Folder Structure\n**├── 📊 dashboard/**  \n- _Power BI dashboard \u0026 screenshots of report pages_\n\n**├── 📂 data/**  \n- _Cleaned \u0026 Raw dataset_\n\n**├── 📓 notebooks/**  \n- _Jupyter Notebooks with SQL \u0026 Power BI logic and insights_\n\n**├── 🧩 schema/**  \n- _Schema screenshots_\n\n**└── 📄 README.md/**  \n- _You're here!_\n\n## 📈 Report Highlights\nA 6-page Power BI report with actionable supply chain insights such as:\n\n1. **`Overview:`**  \nKPIs, revenue trends, best-selling categories\n  \n2. **`Time Intelligence:`**  \nSeasonality, YoY growth, shipping performance\n  \n3. **`Customer \u0026 Product:`**  \nTop customers, bundled purchases, segment behavior\n  \n4. **`Logistics:`**  \nLate Deliveries by shipping mode, region, category\n  \n5. **`Profitability:`**  \nDiscounts, profit margin, negative-profit orders\n  \n6. **`Action Radar:`**  \nSummary page with strategic calls-to-action\n\n## 💡 Key Insights\n- 🛑 **54.82%** or orders are late — but average delay is low **(~0.57 days)**\n- 💰 Top 10 customers contribute just **0.25%** of total revenue → retention opportunity\n- 🎯 Products with **high discounts** and **low profit** are hurting profitability\n- 🧭 Regions like Pacific Asia have  slight higher delay rates and lower margins\n- 📉 The Computers category has high revenue but is consistently unprofitable\n\n## 🧭 Recommendations\n- Improve delivery reliability _(First Class shipping → highest delay rate)_\n- Review discounting strategies on loss-making products\n- Expand strong product lines _(e.g. Fishing)_ and bundled sales strategies\n- Focus marketing on moderate-growth regions _(Europe, Australia)_\n\n---\n\n## 📌 Project Files\n- 📊 [Dashboard Files](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/dashboard) _(pbix, pdf \u0026 pages)_  \n- 📁 [Clean \u0026 Raw Data](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/data) _(compressed folders)_  \n- 📓 [Notebooks](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/notebooks) _(SQL \u0026 Power BI)_  \n- 🧩 [Table Schema Design](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/schema)\n\n## 📬 Contact\nContact with me on:  \n  \n📍 [LinkedIn - Bruno Silva](https://www.linkedin.com/in/brunosilva1297/)  \n📍 [Kaggle - Bruno Silva](https://www.kaggle.com/patinhas)  \n\nReach out to collaborate!\n\n## ✅ Related Projects\n\u003e 📎 [Adventure Works SQL Analysis](https://github.com/brunopata/AdventureWorks-SQL-Analysis) - Investigate sales using Google BigQuery and SQL to drive business decisions.\n\n\u003e 📎 [HR Employee Attrition Analysis](https://github.com/brunopata/HR-Attrition-Analysis-PowerBI) - Discover attrition drivers and satisfaction trends in HR data.\n\n\u003e 📎 [Superstore Sales Analysis](https://github.com/brunopata/Superstore-Sales-Analysis) - Analyze sales performance, customer behavior and shipping efficiency.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrunopata%2Fdataco-supply-chain","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrunopata%2Fdataco-supply-chain","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrunopata%2Fdataco-supply-chain/lists"}