🚚 Logistics Data Analytics Dashboard

This project transforms raw logistics and supply chain data into actionable insights using Python for data analysis and Power BI for dynamic visualization. The goal is to help stakeholders make data-driven decisions around delivery efficiency, route risk, and operational performance.

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📦 Dataset Overview

The dataset includes the following key features:

• vehicle_gps_latitude, vehicle_gps_longitude – Real-time vehicle positions
• fuel_consumption_rate – Fuel usage per unit distance/time
• eta_variation_hours – Delay in expected delivery (converted to duration format)
• traffic_congestion_level – Traffic status (categorical or numerical)
• warehouse_inventory_level, loading_unloading_time, handling_equipment_availability – Operational factors
• order_fulfillment_status – Binary or categorical (Fulfilled / Not Fulfilled)
• weather_condition_severity, shipping_costs, supplier_reliability_score, lead_time_days
• iot_temperature, cargo_condition_status, driver_behavior_score, route_risk_level, risk_classification

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🧰 Tools & Technologies

• Python

  • pandas for data cleaning and wrangling
  • matplotlib & seaborn for EDA and trend visualization

• Power BI

  • Power Query (M) for data transformation
  • DAX and custom visuals for interactivity
  • KPI cards, slicers, maps, trend lines, and categorized filters

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📊 Power BI Dashboard Features

• KPI Cards – Real-time indicators for ETA delay, risk level, shipping costs, driver score
• Line Charts – Trends of ETA variation, risk over time, and fuel efficiency
• Donut/Bar Charts – Category-wise distribution of risk classifications, order statuses
• Geo Maps – Delivery vehicle positions overlaid with risk levels
• Slicers – Interactive filtering by traffic level, weather, and classification
• Tables with Conditional Formatting – Highlight high-risk or delayed shipments

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🔍 EDA Workflow (Python)

1. Load and inspect dataset using pandas
2. Visualize key metrics and correlations using seaborn and matplotlib
3. Preprocess fields (e.g., convert ETA variation from hours to readable duration)
4. Export cleaned data for Power BI integration (.csv)

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🚀 Getting Started

Prerequisites

• Python 3.8+
• Power BI Desktop

Setup Instructions

1. Clone this repo:

   git clone https://github.com/your-username/logistics-dashboard.git
   cd logistics-dashboard
   
   pip install pandas matplotlib seaborn