Operational Risk & Reliability Analytics

End-to-end analytics project using Python, SQLite, SQL, and Power BI to model operational risk, reliability, failure probability, and loss exposure.

Project Purpose

This project simulates 100,000 operational scenarios and analyzes which conditions are associated with higher failure rates and expected losses. It is designed as a portfolio project for roles such as Data Analyst, Operations Analyst, Risk Analyst, Supply Chain Analyst, and Business Analyst.

Hands-on analytics projects are frequently recommended for demonstrating real applied skills in SQL, Python, Power BI, storytelling, and portfolio readiness. citeturn149699search4

Tools

• Python
• Pandas / NumPy
• SQLite
• SQL
• Power BI
• GitHub

Dataset

The dataset is generated with Python.

Rows: 100,000

Main table:

operations

Main columns:

• operation_id
• country
• region
• shipment_mode
• operation_type
• weather_condition
• vendor_tier
• distance_km
• load_factor
• delay_hours
• operator_experience_years
• maintenance_score
• cost_usd
• failure_probability
• failed
• severity
• loss_usd

Business Questions

1. Which shipment modes have the highest failure rate?
2. Which load conditions generate the highest reliability risk?
3. How does delay severity affect operational failure?
4. Which countries or regions create the largest loss exposure?
5. Which operation types are associated with higher severity?
6. Which risk conditions should management prioritize?

Project Workflow

Python
→ simulated operational data
→ CSV dataset
→ SQLite database
→ SQL analysis
→ Power BI dashboard
→ business recommendations

Initial Dataset Metrics

• Total operations: 100,000
• Failure rate: 2.42%
• Total loss exposure: $6,716,769.77
• Average delay: 21.91 hours
• Average load factor: 0.715

Power BI Dashboard Plan

Page 1 — Executive Risk Overview

Cards:

• Total Operations
• Failure Rate
• Total Loss
• Average Delay

Charts:

• Failure Rate by Shipment Mode
• Total Loss by Country

Page 2 — Reliability Drivers

Charts:

• Failure Rate by Load Band
• Failure Rate by Delay Band
• Failure Rate by Weather Condition
• Failure Rate by Vendor Tier

Page 3 — Risk Exposure

Charts:

• Total Loss by Operation Type
• Severity Distribution
• High-Risk Operations Table
• Failure Rate by Experience Band

SQL Analysis Included

• Executive summary
• Failure rate by mode
• Failure rate by load band
• Failure rate by delay band
• Country-level risk exposure
• Severity distribution
• Combined load-delay risk
• Operator experience analysis
• High-risk operation ranking

Repository Structure

operational-risk-reliability-analytics/
├── data/
│   ├── raw/
│   └── processed/
├── database/
├── scripts/
├── sql/
├── powerbi/
├── screenshots/
└── docs/

How to Rebuild the Dataset

pip install -r requirements.txt
python scripts/generate_dataset.py

Portfolio Positioning

This project demonstrates:

• Python-based data generation
• Risk simulation
• SQL aggregation and KPI analysis
• SQLite database workflow
• Power BI dashboard planning
• Operational reliability analysis
• Business insight generation