https://github.com/hq969/operational-analytics-for-hcl-foxconn-semiconductor-osat-facility
Operational Analytics for HCL–Foxconn Semiconductor OSAT Facility is an end-to-end data analyst portfolio project that simulates real-world manufacturing operations. It focuses on improving production efficiency, analyzing equipment downtime, optimizing workforce attendance, and predicting operational risks using machine learning,SQL, and Power BI.
https://github.com/hq969/operational-analytics-for-hcl-foxconn-semiconductor-osat-facility
downtime powerbi predict-absenteeism python3 sql workforce-trends yield yield-monitor
Last synced: 30 days ago
JSON representation
Operational Analytics for HCL–Foxconn Semiconductor OSAT Facility is an end-to-end data analyst portfolio project that simulates real-world manufacturing operations. It focuses on improving production efficiency, analyzing equipment downtime, optimizing workforce attendance, and predicting operational risks using machine learning,SQL, and Power BI.
- Host: GitHub
- URL: https://github.com/hq969/operational-analytics-for-hcl-foxconn-semiconductor-osat-facility
- Owner: hq969
- License: mit
- Created: 2025-07-06T10:57:24.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-07-06T11:20:54.000Z (4 months ago)
- Last Synced: 2025-08-17T02:47:03.758Z (3 months ago)
- Topics: downtime, powerbi, predict-absenteeism, python3, sql, workforce-trends, yield, yield-monitor
- Language: Jupyter Notebook
- Homepage:
- Size: 1.81 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
### 📊 Operational Analytics for HCL–Foxconn Semiconductor OSAT Facility
An end-to-end **operational analytics project** simulating the real-world setup of the **HCL–Foxconn Semiconductor OSAT Facility** in Uttar Pradesh, India. This project includes production analytics, equipment downtime tracking, workforce optimization, absenteeism prediction, SQL-based supply chain analysis, and Power BI dashboarding.
---
## 🏗 Project Structure
```
osat-data-analyst-project/
│
├── notebooks/
│ ├── cleaning_exploration.ipynb
│ ├── production_analysis.ipynb
│ ├── equipment_downtime.ipynb
│ ├── workforce_dashboard.ipynb
│ └── predictive_modeling.ipynb
│
├── dashboards/
│ ├── powerbi_mockup.png
│ └── powerbi_notes.md
│
├── sql/
│ └── supply_chain_delay_queries.sql
│
├── data/
│ ├── production_data.csv
│ ├── equipment_logs.csv
│ ├── workforce_schedule.csv
│ └── supply_chain_data.csv
│
├── README.md
└── requirements.txt
````
---
## 🎯 Project Objectives
- Analyze chip production efficiency and yield
- Investigate equipment downtimes and maintenance patterns
- Visualize workforce productivity and shift-level absenteeism
- Build predictive models for yield and absenteeism risk
- Write SQL queries to identify supply chain bottlenecks
- Design a complete Power BI mockup dashboard for factory operations
---
## 📦 Datasets Overview
| Dataset | Description |
|--------------------------|--------------------------------------------|
| `production_data.csv` | Daily production: chips, lines, defects, shifts |
| `equipment_logs.csv` | Downtime by machine, error logs, MTTR |
| `workforce_schedule.csv` | Daily attendance by department & shift |
| `supply_chain_data.csv` | Regional supply delays and vendor issues |
---
## 🧪 Predictive Modeling
- ✅ **Yield Forecasting** using Random Forest Regressor
- ✅ **Absenteeism Classification** with Logistic Regression
- 🔍 Metrics: RMSE, R², Accuracy, Confusion Matrix, ROC Curve
---
## 📊 Power BI Dashboard (Mockup)
- KPIs: Yield %, Defect rate, Absenteeism rate, MTTR
- Visuals: Time-series trends, heatmaps, department performance
- Screenshot: `dashboards/powerbi_mockup.png`
---
## 💻 Tech Stack
- Python (Pandas, Seaborn, Scikit-learn, Matplotlib)
- SQL (for supply chain insights)
- Power BI (Mock dashboard)
- Jupyter Notebooks
---
---
## 👤 Author
**Harsh Sonkar**
Data Analyst | AWS Data Engineer | Operational Intelligence
🔗 [LinkedIn](https://www.linkedin.com/in/harsh-sonkar-232573250)
🌐 [GitHub](https://github.com/)
---
## 📄 License
MIT License