Supply Chain Resilience Command Center

Empowering Operations with Real-Time Insights to Navigate Delays and Drive Supply Chain Resilience

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📊 About the Project: Supply Chain Resilience Command Center

🌟 Inspiration

The inspiration came from real-world challenges in supply chain disruptions — especially in rail logistics, where delays at critical junctions ripple through the entire network. We wanted to showcase how Tableau Next + Data Cloud can turn raw operational data into actionable intelligence for better planning and faster decision-making.

🎓 What We Learned

  • How to model multi-level datasets (trains, routes, stations) and map them into a unified analytics flow.
  • Techniques to blend structured CSVs (train delays, weather, supplier data) inside Tableau for a smooth data pipeline.
  • How to design drill-down dashboards that let users start from a high-level KPI (e.g., average delay per route) and zoom into granular details (e.g., station-level performance).
  • The importance of contextual alerts and scenario planning for supply chain resilience.

🛠️ How We Built It

  1. Data Preparation
  • Collected static CSVs for train routes, station delays, and weather data.
  • Cleaned and joined datasets by Train_Number and Station.
  • Modeled delay metrics as station-level facts for granular analysis.
  1. Integration
  • Used Tableau Cloud to publish cleaned datasets.
  • Connected to Salesforce Data Cloud for real-time extension (future scalability).
  • Added Slack alerts for stations with > 1 hr average delays.
  1. Visualization

  • Built dashboards with multi-level drill-downs:

    • Route overview with KPIs (on-time %, average delay).
    • Station-level heatmaps to pinpoint bottlenecks.
    • Trend lines to forecast disruptions.

  • Added “What-if” scenario planning (e.g., impact if a station is fully blocked).

🚧 Challenges We Faced

  • Data consistency: Train route datasets varied in format; aligning station codes with delay metrics required manual mapping.
  • Time constraints: Integrating supplier & shipment datasets was planned but not fully implemented due to hackathon time limits.
  • Scalability: Simulating real-time data streams with static CSVs was challenging, but Tableau Data Cloud provided a good foundation.

📈 Future Improvements

  • Add live API integration for weather and logistics data.
  • Extend model to supplier & shipment datasets for end-to-end supply chain visibility.
  • Use AI-driven predictive analytics in Tableau for proactive disruption management.

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