The challenge
Delivery performance and transport costs were tracked in separate places. It was hard to see where shipments were failing, which regions and couriers were underperforming, and how transport costs were moving against revenue and budget.
Late deliveries were a particular blind spot: some shipments could not even be matched in the tracking system.
The solution
A layered pipeline feeding a single Power BI report:
API dataShipment and tracking data from the logistics platform
Python ETLExtraction, transformation and loading
SASStatistical processing and data quality checks
Power BI13 pages with filters and drill-down
What the solution includes
Shipment monitorDelivered, failed attempts, in transit and exceptions
24-48-72h deliveryDelivery speed by region and province
Partial deliveriesShare of shipments not delivered in full
Courier comparisonShipment detail and efficiency by courier
Regional rankingTop and underperforming areas
Transport KPIsCost per shipment, value density, weight, pallets
Transport costCost vs revenue and budget, year on year
Parcels held at depotDuration and reasons for release delays
Late deliveriesStatus by customer type, returns and unmatched shipments
Results
- Delivery performance, courier efficiency and costs in one place
- Problem areas highlighted with conditional formatting and drill-down
- Transport costs tracked against revenue, budget and the previous year
- Late and unmatched deliveries made visible and traceable
Stack
Power BI
Python
SAS
REST API
KPI Design
Client names and confidential figures have been omitted. The images on this page contain no client data.