AI-powered analytics tool that analyzes operational workflow logs to identify bottlenecks, detect anomalies, and generate root-cause insights.
This project simulates the type of internal analytics tool an AI or data team might build to help operations departments (HR, finance, procurement, etc.) understand inefficiencies in their workflows.
Many business operations rely on multi-step workflows such as:
- HR hiring approvals
- procurement requests
- finance approvals
- ticket escalation pipelines
- internal service requests
These processes often suffer from hidden inefficiencies that are difficult to detect manually.
This tool simulates an AI-powered workflow analytics system that automatically identifies process bottlenecks, detects abnormal delays using machine learning, and generates operational insights to improve efficiency.
Automatically identifies the slowest step in a workflow based on average step duration.
Generates possible causes and recommended actions for the detected bottleneck.
Uses an Isolation Forest model to detect unusually slow workflow cases.
Optionally generates AI-powered executive summaries of workflow performance.
Identifies the most common workflow paths across cases.
A Streamlit dashboard allows users to upload workflow logs and immediately analyze operational performance.
This system can analyze workflow logs from processes such as:
- HR approval pipelines
- procurement approvals
- finance review processes
- ticket resolution workflows
- internal request systems
By analyzing these logs, the tool can:
- Identify process bottlenecks
- Detect abnormal case delays
- Provide root cause insights
- Recommend operational improvements
Python
Pandas
FastAPI
Streamlit
Scikit-learn
OpenAI API (optional)
Clone the repository:
git clone https://github.com/YOUR_USERNAME/ai-operations-bottleneck-analyzer.git
cd ai-operations-bottleneck-analyzer
Create a virtual environment:
python -m venv .venv
Activate the environment:
Windows
.venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Start the Streamlit dashboard:
python -m streamlit run dashboard/dashboard_main.py
Then open your browser:
http://localhost:8501
Upload a workflow CSV file to begin analyzing workflow performance.
case_id,step,step_duration_hours
1001,Submit Request,1
1001,Manager Approval,4
1001,Finance Review,2
1002,Submit Request,1
1002,Manager Approval,2
1002,Finance Review,3
Each row represents a step in a workflow case.
The system generates insights including:
- Bottleneck step
- Average step duration
- Root cause analysis
- Recommended operational improvements
- Machine learning anomaly detection
- Workflow path analysis
Potential future upgrades include:
- automated workflow diagrams
- deeper process mining analytics
- multi-department workflow comparison
- real-time workflow monitoring
- automated optimization recommendations
Evan Riley
AI / Machine Learning Engineer focused on applied AI systems, operational analytics, and intelligent automation.