Shipment Risk Analysis & Late Delivery Prediction

A data-driven supply chain analytics solution that predicts late deliveries and categorizes shipment risk using machine learning, combined with an interactive Power BI dashboard for business insights and decision-making.

Python Machine Learning Power BI Supply Chain Analytics Predictive Modeling Data Visualization
Executive View
Executive View A high-level summary of shipment risks and overall delivery performance.
Insights & Strategies
Insights & Strategies Detailed insights into delay patterns and strategic recommendations.

🎥 Video Walkthrough

📌 Problem Statement

Late deliveries significantly impact customer satisfaction, operational efficiency, and logistics costs. In the absence of predictive systems, businesses rely on reactive approaches, leading to missed opportunities for optimization.

This project addresses: How can we predict shipment delays in advance and proactively manage high-risk deliveries?

⚙️ Approach & Methodology

  • Performed data cleaning and preprocessing on 180K+ shipment records
  • Conducted exploratory data analysis to identify delay patterns
  • Engineered features like Shipping Delay (Actual vs Scheduled)
  • Built classification models: Logistic Regression (baseline) and Random Forest (final model)
  • Achieved ~70% accuracy in predicting late deliveries
  • Generated prediction probabilities and categorized risk: High Risk (>70%), Medium Risk (40–70%), Low Risk (<40%)< /li>
  • Developed interactive Power BI dashboards for visualization and insights

🧠 Concepts & Skills Applied

Predictive Analytics
Machine Learning
Feature Engineering
Power BI

📊 Key Insights

  • 38.29% of shipments are delivered late, exceeding operational benchmarks
  • Over 10,000 shipments classified as High Risk, requiring immediate attention
  • Standard Class shipping contributes the highest delay rate
  • Same Day shipping shows the best performance
  • Medium-risk shipments represent the biggest opportunity for improvement
  • Shipping schedule and mode are the strongest predictors of delays

📈 Dashboard Highlights

  • Executive Overview with KPIs and Late Delivery Rate
  • Risk Distribution (High / Medium / Low)
  • Shipping Mode Performance Analysis
  • Regional Delivery Performance
  • Strategic Insights & Recommendations

💼 Business Recommendations & Impact

  • Prioritize monitoring of high-risk shipments
  • Optimize Standard Class logistics operations
  • Implement automated risk alert systems
  • Upgrade shipping mode for high-value orders
  • Closely monitor medium-risk shipments

Expected Impact:
• 15–20% reduction in late deliveries
• 12–18% improvement in operational efficiency
• 8–12% reduction in logistics costs
• 10–15% improvement in customer satisfaction

⚡ Advanced Features

  • Machine Learning-based delay prediction
  • Risk categorization using probability thresholds
  • Feature importance analysis for explainability
  • Interactive Power BI dashboard for decision support
  • End-to-end pipeline: Data → Model → Insights → Business Action
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