https://github.com/brunopata/dataco-supply-chain
A complete end-to-end data project exploring operational efficiency, customer behavior and profitability across 180K+ orders. Built using BigQuery, Power BI and Jupyter — includes modeling, KPI analysis and actionable business insights.
https://github.com/brunopata/dataco-supply-chain
analytics bigquery business-intelligence dashboard data-modeling power-bi sql supply-chain
Last synced: 5 months ago
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A complete end-to-end data project exploring operational efficiency, customer behavior and profitability across 180K+ orders. Built using BigQuery, Power BI and Jupyter — includes modeling, KPI analysis and actionable business insights.
- Host: GitHub
- URL: https://github.com/brunopata/dataco-supply-chain
- Owner: brunopata
- Created: 2025-05-23T14:40:54.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-07T13:19:00.000Z (about 1 year ago)
- Last Synced: 2025-06-22T07:37:10.342Z (about 1 year ago)
- Topics: analytics, bigquery, business-intelligence, dashboard, data-modeling, power-bi, sql, supply-chain
- Language: Jupyter Notebook
- Homepage:
- Size: 125 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🚚 **DataCo Smart Supply Chain Analysis (SQL + Power BI)**
## 🧠 Summary
A complete end-to-end analytics project using SQL and Power BI to optimize supply chain performance.
This case study explores profitability, logistics delays, discount impact and customer behavior across 180K+ sales records.
## 🔍 Key Objectives
- Clean & model raw sales data into a star/snowflake schema using SQL (BigQuery)
- Perform data exploration to uncover insights on sales, shipping and customer behavior
- Build interactive Power BI dashboards to visualize KPIs and trends
- Generate actionable business recommendations through data storytelling
## 🛠 Tools Used
- **`SQL`** _(Google BigQuery)_
- **`Power BI`** _(6-page Dashboard)_
- **`dbdiagram.io`** _(Schema Design)_
- **`Jupyter Notebook`** _(Exploratory Analysis & Documentation)_
- **`GitHub`** _(Project Versioning & Publication)_
## 🗃️ Dataset
- 📦 **`Source:`**
- [DataCo Smart Supply Chain Dataset](https://data.mendeley.com/datasets/8gx2fvg2k6/5)
- [DataCo Smart Supply Chain Dataset on Kaggle](https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis)
- 🛍️ **`Data:`**
- ~18K customers
- ~180K orders
- 3 years of data
- Products acrosse 50+ categories
## 📁 Folder Structure
**├── 📊 dashboard/**
- _Power BI dashboard & screenshots of report pages_
**├── 📂 data/**
- _Cleaned & Raw dataset_
**├── 📓 notebooks/**
- _Jupyter Notebooks with SQL & Power BI logic and insights_
**├── 🧩 schema/**
- _Schema screenshots_
**└── 📄 README.md/**
- _You're here!_
## 📈 Report Highlights
A 6-page Power BI report with actionable supply chain insights such as:
1. **`Overview:`**
KPIs, revenue trends, best-selling categories
2. **`Time Intelligence:`**
Seasonality, YoY growth, shipping performance
3. **`Customer & Product:`**
Top customers, bundled purchases, segment behavior
4. **`Logistics:`**
Late Deliveries by shipping mode, region, category
5. **`Profitability:`**
Discounts, profit margin, negative-profit orders
6. **`Action Radar:`**
Summary page with strategic calls-to-action
## 💡 Key Insights
- 🛑 **54.82%** or orders are late — but average delay is low **(~0.57 days)**
- 💰 Top 10 customers contribute just **0.25%** of total revenue → retention opportunity
- 🎯 Products with **high discounts** and **low profit** are hurting profitability
- 🧭 Regions like Pacific Asia have slight higher delay rates and lower margins
- 📉 The Computers category has high revenue but is consistently unprofitable
## 🧭 Recommendations
- Improve delivery reliability _(First Class shipping → highest delay rate)_
- Review discounting strategies on loss-making products
- Expand strong product lines _(e.g. Fishing)_ and bundled sales strategies
- Focus marketing on moderate-growth regions _(Europe, Australia)_
---
## 📌 Project Files
- 📊 [Dashboard Files](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/dashboard) _(pbix, pdf & pages)_
- 📁 [Clean & Raw Data](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/data) _(compressed folders)_
- 📓 [Notebooks](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/notebooks) _(SQL & Power BI)_
- 🧩 [Table Schema Design](https://github.com/brunopata/DataCo-Supply-Chain/tree/main/schema)
## 📬 Contact
Contact with me on:
📍 [LinkedIn - Bruno Silva](https://www.linkedin.com/in/brunosilva1297/)
📍 [Kaggle - Bruno Silva](https://www.kaggle.com/patinhas)
Reach out to collaborate!
## ✅ Related Projects
> 📎 [Adventure Works SQL Analysis](https://github.com/brunopata/AdventureWorks-SQL-Analysis) - Investigate sales using Google BigQuery and SQL to drive business decisions.
> 📎 [HR Employee Attrition Analysis](https://github.com/brunopata/HR-Attrition-Analysis-PowerBI) - Discover attrition drivers and satisfaction trends in HR data.
> 📎 [Superstore Sales Analysis](https://github.com/brunopata/Superstore-Sales-Analysis) - Analyze sales performance, customer behavior and shipping efficiency.