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US-Logistics-Performance-Dashboard

Power BI analytics dashboard using SQL, Python, and DAX (2026)

US Logistics Performance Dashboard

SQL • Python • Power BI — 2026

Author: Awais Shakeel Pasha

This project delivers a complete logistics analytics dashboard built with Power BI, analyzing 2,000 shipments from multiple US carriers.
It includes KPIs, delay metrics, transit‑time benchmarking, geographic distribution, and time‑based trends, presented in a professionally polished, multi‑page report.

📊 Dashboard Overview

The dashboard is divided into three optimized and polished pages, each with a specific analytical purpose:

🟦 Page 1 — Executive Summary (Polished Layout)

This page provides a high‑level overview of shipment performance:

✔ Key Features

  • KPI Cards: Total Shipments, On‑Time Rate, Delay Rate, Avg Transit Days
  • Total Shipments by Carrier: Clean, full‑width horizontal bar chart
  • Status Breakdown: Donut chart for Delivered / Delayed / In‑Transit / Returned
  • Shipments by Destination: US map showing major city drop‑offs
  • Time Filters: Year, Quarter, and Month slicers (clean, single-row design)

✔ What’s Improved

  • KPI cards centered and uniformly styled
  • Redundant visuals removed
  • Slicers resized and aligned in a single compact row
  • Map and carrier chart spacing aligned
  • Cleaner visual hierarchy and white-space usage

🟦 Page 2 — Carrier Delivery Performance (Polished Layout)

This page focuses on operational efficiency at the carrier level.

✔ Key Features

  • Average Transit Days by Carrier
  • Delay % by Carrier (color‑coded: red → orange → yellow)
  • Slowest Routes Table with conditional formatting
    • Origin
    • Destination
    • Transit Days
    • Shipments Count

✔ What’s Improved

  • Both bar charts aligned with equal visual height
  • Consistent color palette for delay and transit metrics
  • Full‑width slowest routes table
  • Clean header and spacing adjustments
  • More readable conditional formatting for transit outliers

🟦 Page 3 — Geographic & Trend Analysis (Polished Layout)

Highlights geographic distribution and monthly performance.

✔ Key Features

  • Full‑width Map: Shipment destinations across the US
  • Shipments by State: Ranked table with heat‑color formatting
  • Shipments Over Time: Monthly line chart with trendline

✔ What’s Improved

  • Balanced layout (30% table / 70% line chart)
  • Reduced map bubble sizes for cleaner readability
  • Visual alignment top-to-bottom
  • Improved trendline contrast

🚚 Key Insights from the Dashboard

  • Higher delay percentages observed for specific carriers (DHL, Amazon Logistics)
  • Transit times range from ~4.7 to 5.5 days, with several 7+ day slow routes
  • Shipment volume is concentrated in major hubs such as Los Angeles, Chicago, Miami, New York, and Houston
  • 2023 shows a stable monthly shipment pattern with moderate peaks

These insights vary if new or real data is used.

🧰 Tech Stack

Layer Tools
Data Extraction SQL
Data Cleaning Python (pandas, numpy)
Data Modeling Power BI (DAX, Date Tables)
Visualization Power BI Desktop
Mapping Bing Map Visual
Reporting 3‑Page Power BI Interactive Dashboard

🗂 Recommended Repository Structure

US-Logistics-Performance-Dashboard/ │ ├── README.md ├── LICENSE │ ├── powerbi/ │ └── logistics_dashboard.pbix │ ├── data/ │ └── shipments.csv │ └── screenshots/ ├── page1_executive_summary.png ├── page2_carrier_performance.png └── page3_geo_trend.png

📐 Sample DAX Used

Total Shipments = COUNTROWS(Shipments)

OnTime Rate =
DIVIDE(
    CALCULATE(COUNTROWS(Shipments), Shipments[Status] = "Delivered"),
    [Total Shipments]
)

Delay Rate =
DIVIDE(
    CALCULATE(COUNTROWS(Shipments), Shipments[Status] = "Delayed"),
    [Total Shipments]
)

Avg Transit Days = AVERAGE(Shipments[TransitDays])