Power BI analytics dashboard using SQL, Python, and DAX (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.
The dashboard is divided into three optimized and polished pages, each with a specific analytical purpose:
This page provides a high‑level overview of shipment performance:
- 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)
- 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
This page focuses on operational efficiency at the carrier level.
- 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
- 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
Highlights geographic distribution and monthly performance.
- 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
- Balanced layout (30% table / 70% line chart)
- Reduced map bubble sizes for cleaner readability
- Visual alignment top-to-bottom
- Improved trendline contrast
- 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.
| 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 |
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
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])