Logistics & Delivery Performance (Olist Dataset) — Adura Datahub

What this project answers

• What is the on-time delivery rate, and how does it change over time?
• How long do deliveries take (delivery days) and where do delays happen?
• Which states perform best/worst on on-time rate, delivery speed, and freight cost?
• What actions can improve delivery SLA and customer experience?

Files in this repo

Executive report (PDF)

• outputs/adura_datahub_project2_olist_report.pdf

Visuals (PNG charts)

See output/:

• On-time rate trend (monthly)
• Avg delivery days trend (monthly)
• On-time rate by top states
• Avg delivery days by top states
• Avg freight by top states
• Delay days distribution

Dashboard workbook (Excel)

• adura_datahub_project2_olist_dashboard.xlsx

Data

Due to GitHub upload limits, the repo includes a representative sample + summaries packaged as:

• data/project2_data_small_pack.zip

(Data source: Olist Brazilian E-commerce dataset on Kaggle.)

KPI definitions

• On-time: delivered on or before the estimated delivery date
• Delivery days: purchase timestamp → customer delivery timestamp
• Delay days: delivered date − estimated date (positive means late)

Recommendations

1. Monitor on-time weekly by state and volume to prioritize intervention.
2. Improve slow lanes by reducing carrier-to-customer time.
3. Track freight per order and flag high-cost, slow-delivery areas.