MES Production Audit Trail Analysis 🏭📊

This project simulates and analyzes data from a Manufacturing Execution System (MES) in a pharmaceutical production environment. It focuses on quality control, environmental monitoring (temperature), and operator performance tracking.

🚀 Project Overview

The goal is to monitor a production line where temperature excursions directly impact product quality. The system tracks 150 production events, identifying correlations between sensor data, shifts, and batch success rates.

🛠️ Tech Stack

• Python: Data simulation using pandas and numpy.
• Power BI: Interactive dashboard for real-time KPIs and trend analysis.
• GitHub: Version control and documentation.

📋 Data Dictionary (Variables)

Variable	Description
Timestamp	Exact date and time of the recorded event.
Batch_ID	Unique identifier for the production lot (Essential for Pharma traceability).
Operator_ID	Unique ID for the operator on shift (categorized by Morning, Afternoon, and Night).
Sensor_Temp	Critical process parameter (°C). Target is < 26.0°C.
Quality_Status	Categorical: OK (within specs) or RECHAZADO (excursion detected).
Action	System response: Proceso Normal or ALERTA: Desviación Térmica.
Machine_State	Operational status: Running or Stopped (automatic interlock).

📈 Dashboard Insights & Conclusions

Based on the Power BI analysis:

1. Thermal Stability: The line shows a high sensitivity to temperature. The Max Temperature recorded (33.48°C) triggered immediate system stops to prevent batch contamination.
2. Quality Yield: Out of 100 monitored events, 69% were successful (OK) while 31% were rejected. This indicates a need for better cooling system calibration.
3. Operator Performance:
   • OP-NIGHT-01 and OP-NIGHT-02 face the highest number of rejections, suggesting that environmental conditions or fatigue during the night shift may affect process stability.
   • OP-AFTERNOON-01 shows the most stable "OK" ratio.
4. Operational Risk: The Donut Chart confirms that 31% of total actions are Alarms, which directly correlates with the peaks shown in the Temperature Trend line chart.

⚙️ How to Run

1. Run the mes_project2.ipynb script to generate the latest MES_Production_Audit_Trail.csv.
2. Open the MES_project2.pbix file in Power BI Desktop.
3. Refresh the data source to visualize the updated manufacturing metrics.

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Developed as a technical showcase for Manufacturing Systems & Data Analysis.