DataLife
Data Analysis

Real-Time Logistics Dashboard

ClientLogiTrack Ltd
IndustrySupply Chain & Logistics
Duration3 weeks

Key Result

94% reduction in manual reporting time

The Challenge

What needed solving

LogiTrack's operations team was spending 15+ hours per week manually pulling shipment data from three different ERPs, formatting it in Excel, and emailing static PDFs to stakeholders. Data was always at least 48 hours stale, decisions were being made on outdated information, and analysts were burning out on rote copy-paste work.

The Solution

How we solved it

I built a live Power BI dashboard connected directly to LogiTrack's PostgreSQL warehouse via an incremental dbt pipeline that refreshes every 30 minutes. A Python-based data quality layer flags anomalies (late shipments, missing carrier codes) before they reach the dashboard, and a scheduled Resend email digest delivers a one-page summary to leadership every morning at 7 AM — no manual intervention required.

Results

The numbers tell the story

94%

Reduction in manual reporting hours

30 min

Data refresh cadence (was 48 hrs)

15 hrs/wk

Analyst time reclaimed

3 ERPs

Unified into a single source of truth

Tech Stack

Tools used on this project

PythondbtPostgreSQLPower BISupabasePandas
Charles delivered exactly what he promised — on time and with zero drama. Our dashboard has become the single most-used tool in the ops team. I can't imagine going back to the spreadsheet nightmare we had before.

Marcus Osei

Head of Operations, LogiTrack Ltd

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